Data Science & AI Certificate

Self-paced lessons

Watch free previews and explore the lessons included with enrollment at Noble Desktop.

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  Benefits of Learning SQL

  Lesson 1: Setting Up the Work Environment

  Download the PDF workbook and class files, and install the free dBeaver Community Edition to practice SQL querying without modifying databases.

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  Connecting to Our Database

  Lesson 1: Setting Up the Work Environment

  14:06

  Set up a database connection in dBeaver using the provided credentials, test connectivity, and review available tables.

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  Getting Started & Your First Query

  Lesson 2: Let's Start Learning SQL Code

  7:29

  Navigate databases in dBeaver by adjusting preferences, exploring schemas, and running practice queries to build familiarity.

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  Learning the SQL Language

  Lesson 2: Let's Start Learning SQL Code

  14:10

  Review SQL syntax through slides, live demos, and exercises focused on SELECT, FROM, ORDER BY, DISTINCT, execution order, and result limits.

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  Coding Exercise PostgreSQL

  Lesson 2: Let's Start Learning SQL Code

  18:29

  Connect to the correct database environment and complete Exercise 1C by writing and executing SQL queries.

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  Solution PostgreSQL Challenge

  Lesson 2: Let's Start Learning SQL Code

  15:11

  Query users and orders tables to view, filter, sort, and remove duplicate records.

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  Data Types and Filtering Results

  Lesson 3: Working with Characters & Numbers

  4:44

  Differentiate database data types to query text and numeric values correctly, applying quotes and comparison logic appropriately.

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  Coding Exercise Filtering with WHERE

  Lesson 3: Working with Characters & Numbers

  5:34

  Define the active data source to enable code hints when querying product data based on price conditions.

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  Solution Filtering with WHERE

  Lesson 3: Working with Characters & Numbers

  22:42

  Refine queries to filter specific records, sort results, perform calculations, handle data types accurately, and eliminate duplicates.

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  Pattern Matching

  Lesson 4: Using SQL Wildcards

  4:46

  Apply wildcard characters such as percent signs and underscores with LIKE and ILIKE to identify data patterns.

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  Combining Filters and Aliases

  Lesson 4: Using SQL Wildcards

  4:32

  Combine conditions using AND, OR, and NOT, perform calculations, and assign column aliases for clearer result sets.

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  Coding Exercise Like and Wildcards

  Lesson 4: Using SQL Wildcards

  12:31

  Practice filtering records using LIKE, ILIKE, and NOT to control query results precisely.

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  Solution Like and Wildcards

  Lesson 4: Using SQL Wildcards

  23:49

  Explore table structures and ER diagrams to understand relationships and safely query data such as email domains or state-based orders.

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  Database Terminology and Primary & Foreign Keys

  Lesson 5: Identifying Rows & Connecting Tables

  6:44

  Connect related tables by identifying primary and foreign keys to manage relationships and avoid duplication.

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  Joins Combining Data from Multiple Tables

  Lesson 5: Identifying Rows & Connecting Tables

  12:28

  Perform inner joins by matching common columns and using table aliases to simplify query syntax.

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  Exercise Inner Join

  Lesson 5: Identifying Rows & Connecting Tables

  18:40

  Reinforce inner join logic by combining tables, filtering shared data, and presenting unified results.

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  Solution Inner Join

  Lesson 5: Identifying Rows & Connecting Tables

  10:36

  Join users, orders, and line items to identify high-value purchases and return distinct customer details.

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  Inner vs. Outer Joins

  Lesson 6: Inner vs. Outer Joins

  9:45

  Compare inner joins with left, right, and full outer joins to understand how non-matching rows are handled.

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  Exercise Outer Join and NULL

  Lesson 6: Inner vs. Outer Joins

  23:37

  Open the designated file, review warm-up exercises, and complete challenge problems to reinforce learning.

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  Solution Outer Join and NULL

  Lesson 6: Inner vs. Outer Joins

  34:12

  Analyze database relationships to uncover insights such as unsold products or specific purchasing behaviors, then export results as CSV files.

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  Cast

  Lesson 7: Changing Data Types

  7:07

  Convert data types within queries using CAST without altering the underlying database structure.

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  Aggregate Functions

  Lesson 7: Changing Data Types

  6:33

  Apply aggregate functions to calculate totals, counts, minimums, maximums, and averages for analysis.

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  Exercise Aggregate Function and ROUND

  Lesson 7: Changing Data Types

  27:56

  Manipulate aggregated results using rounding functions and column aliases for readable output.

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  Solution Aggregate Function and ROUND

  Lesson 7: Changing Data Types

  19:00

  Build aggregate queries incrementally, validating intermediate results before final execution.

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  Date Functions

  Lesson 8: Working with Dates

  2:20

  Work with date and time functions to retrieve current timestamps, extract components, and convert data types.

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  Exercise 2.0 Date Functions

  Lesson 8: Working with Dates

  19:52

  Extract, format, filter, and convert date values using date_part, to_char, CAST, and range-based conditions.

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  Solution 2.0 Date Functions

  Lesson 8: Working with Dates

  17:40

  Analyze account creation data to determine user age, creation year, weekend sign-ups, and date-range activity.

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  Grouping Data

  Lesson 9: Organizing Statistical Results Into Categories

  8:18

  Group records using GROUP BY to apply aggregate functions across defined categories.

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  Subqueries

  Lesson 10: Subqueries

  27:41

  Nest subqueries within main queries to process intermediate results before final selection.

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  Solution Subqueries

  Lesson 10: Subqueries

  24:51

  Prefer joins over subqueries when appropriate to improve clarity and performance.

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  Aggregate Window Functions

  Lesson 11: Aggregate Window Functions

  23:01

  Use window functions to add aggregate insights while preserving row-level detail.

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  Solution Window Functions

  Lesson 11: Aggregate Window Functions

  9:34

  Apply PARTITION BY within window functions to analyze grouped metrics without collapsing results.

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  Conditionals

  Lesson 12: Conditionals

  18:26

  Create conditional logic with CASE statements to classify, transform, or correct query output.

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  CASE Exercise

  Lesson 12: Conditionals

  14:36

  Categorize data into regions or groups, calculate order counts, and manage joins for broader analysis.

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  Views

  Lesson 13: Views

  18:50

  Save reusable queries as views to simplify access, enhance security, and streamline workflows.

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  Views Solution

  Lesson 13: Views

  0:46

  Summarize and filter user spending patterns using flexible query criteria.

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  String Functions

  Lesson 14: String Functions

  4:34

  Manipulate text data with string functions to change case, extract substrings, split values, and evaluate length.

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  Learning Resources

  Lesson 15: Additional Resources

  3:24

  Reinforce skills through external SQL resources, practice platforms, cheat sheets, and coding challenges.

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  Finding Datasets

  Lesson 15: Additional Resources

  2:04

  Source datasets from Kaggle and convert them into usable databases with guided setup support.

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  AI and SQL

  Lesson 16: AI and SQL

  11:06

  Use AI tools such as ChatGPT to explain, refine, and generate SQL queries while validating results independently.

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  Challenge AI and SQL

  Lesson 16: AI and SQL

  5:13

  Generate SQL queries from database visuals using tools like Chat2BT, specifying structure and dialect requirements.

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  Solution AI and SQL

  Lesson 16: AI and SQL

  14:13

  Apply best practices for using AI-generated SQL by providing clear requirements, verifying accuracy, and learning from explanations.

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  Introduction to Python for Data Science

  Lesson 1: Python for Data Science Fundamentals

  9:06

  Download, unzip, and upload course files to Google Drive, then open and edit copies in Google Colab with AI assistance turned off and line numbers enabled to support hands-on coding practice.

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  Variables

  Lesson 1: Python for Data Science Fundamentals

  21:19

  Explain how variables, data types, and string operations function in Python programming.

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  Integer and Decimal

  Lesson 1: Python for Data Science Fundamentals

  20:07

  Differentiate integers and decimals, apply math operators, convert between strings and numeric types, and manage string concatenation in Python.

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  Input Function

  Lesson 1: Python for Data Science Fundamentals

  8:30

  Capture user input with the input function, convert values to integers or floats, apply the correct order of operations, and use modulus to convert total minutes into hours and minutes.

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  Input Function Part 2

  Lesson 1: Python for Data Science Fundamentals

  4:39

  Apply the modulus operator to format movie duration by separating hours from remaining minutes.

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  Round Function

  Lesson 1: Python for Data Science Fundamentals

  6:00

  Process numerical input by converting strings to floats, rounding values, and calculating percentages in practical scenarios such as billing.

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  Cafe Bill Challenge Solution

  Lesson 1: Python for Data Science Fundamentals

  14:12

  Calculate a café bill by collecting food, beverage, and tip inputs, computing subtotals, tax, and tip amounts separately, formatting currency values, and presenting a final total.

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  Boolean

  Lesson 1: Python for Data Science Fundamentals

  5:32

  Explain Boolean values and comparison operators, and how they evaluate conditions as true or false.

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  List Data Type

  Lesson 1: Python for Data Science Fundamentals

  15:52

  Describe how lists work in Python, including creation, indexing, slicing, and accessing nested lists.

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  Methods Functions

  Lesson 1: Python for Data Science Fundamentals

  18:45

  Explain object methods with a focus on list methods such as append, pop, sort, and extend, and distinguish methods from standalone functions.

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  Set & Vector Operation

  Lesson 1: Python for Data Science Fundamentals

  14:37

  Use sets to manage unique values, remove duplicates from lists, and perform basic set and list operations.

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  If Else Logic

  Lesson 2: Boolean Conditions

  4:43

  Apply if, elif, and else statements with Boolean logic to control program flow based on conditions.

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  If Else Logic Challenge Solution

  Lesson 2: Boolean Conditions

  16:35

  Write Python code that uses conditional logic to evaluate prices and manipulate lists using logical operators such as and and or.

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  Datetime Part 1

  Lesson 3: Datetime & Random

  10:28

  Summarize Python modules and demonstrate importing libraries to extend functionality, including random number generation and timezone handling with datetime and pytz.

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  Datetime Part 2

  Lesson 3: Datetime & Random

  11:35

  Convert and format time values into military and AM/PM formats and generate conditional greetings based on the current hour.

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  Random Module

  Lesson 3: Datetime & Random

  20:22

  Use the random module to generate numbers, sample unique values, shuffle lists, and complete exercises involving scores, dates, and lottery selections.

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  Loops

  Lesson 4: Loops & Strings

  4:29

  Explore loops by iterating over lists and ranges with for loops while avoiding infinite loops.

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  Loop the Nums List Challenge

  Lesson 4: Loops & Strings

  9:17

  Loop through numbers one through ten, square even numbers, cube odd numbers, and print the results.

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  List Methods Review Part 1

  Lesson 4: Loops & Strings

  13:11

  Practice list manipulation by extending lists, inserting and removing elements, popping by index, removing duplicates, and rearranging items without sorting.

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  List Methods Review Part 2

  Lesson 4: Loops & Strings

  9:03

  Insert specific elements into a list, remove items at given indices, deduplicate values using sets, sort lists, and maintain alphabetical order through targeted insertion.

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  Loop Method Exercise

  Lesson 4: Loops & Strings

  10:23

  Loop through a list to create transformed values, store results in a new list, and selectively print items while skipping defined positions.

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  Loop Challenges

  Lesson 4: Loops & Strings

  9:37

  Apply conditional logic to generate different outputs based on string length or other criteria, such as creating jelly beans, popsicles, jams, or lollipops.

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  In and Not In Operators

  Lesson 4: Loops & Strings

  16:23

  Use in and not in operators to test membership in strings and lists, and apply conditional logic to categorize items based on starting or ending characters.

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  String Methods

  Lesson 4: Loops & Strings

  34:06

  Manipulate strings using methods such as lower, upper, capitalize, replace, and split, while creating modified copies since strings are immutable.

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  Nested Loop

  Lesson 4: Loops & Strings

  11:01

  Create and shuffle a standard 52-card deck using nested loops and simulate dealing hands in a blackjack-style game.

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  Dictionaries Part 1

  Lesson 5: Dictionaries

  10:16

  Explore Python dictionaries by working with key-value pairs, nested structures, and list data within dictionaries.

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  Dictionaries Part 2

  Lesson 5: Dictionaries

  10:39

  Add, update, rename, and delete dictionary keys and values, and iterate through dictionary contents.

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  Dictionaries Scrabble Exercise

  Lesson 5: Dictionaries

  13:11

  Build a Scrabble score calculator that converts user input to uppercase, looks up letter values in a dictionary, accumulates points, and outputs a final score.

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  Numpy

  Lesson 6: Numpy

  4:33

  Introduce NumPy by importing it as np and using it to convert lists into multi-dimensional arrays for advanced data manipulation.

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  Numpy Array

  Lesson 6: Numpy

  18:34

  Transform lists into NumPy arrays to enable reshaping, transposing, and other array-based operations.

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  2D Selections with Numpy Array

  Lesson 6: Numpy

  18:27

  Perform two-dimensional selection and slicing in NumPy arrays, including creating matrices and extracting subarrays.

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  Pandas Dataframes Part 1

  Lesson 7: Pandas Dataframes

  5:21

  Create and analyze pandas DataFrames by converting lists and dictionaries into tabular structures for data analysis.

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  Pandas Dataframes Part 2

  Lesson 7: Pandas Dataframes

  10:18

  Load external images and display structured data such as a chessboard using pandas DataFrames with customized rows and columns.

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  Pandas Dataframes Part 3

  Lesson 7: Pandas Dataframes

  14:01

  Select specific rows and columns from a DataFrame using the iloc method.

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  Pandas Dataframes Part 4

  Lesson 7: Pandas Dataframes

  12:21

  Manipulate DataFrame sections to model chessboard layouts by slicing rows and columns and assigning pieces through indexing.

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  Pandas Dataframes Part 5

  Lesson 7: Pandas Dataframes

  6:51

  Arrange structured data, such as chess pieces or food items, into DataFrames with associated attributes.

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  Pandas Dataframes Part 6

  Lesson 7: Pandas Dataframes

  12:17

  Create DataFrames by defining column names and value lists of equal length to ensure proper row alignment.

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  Dataframes Location

  Lesson 7: Pandas Dataframes

  16:20

  Access and modify DataFrame data using loc for labels and iloc for integer-based indexing.

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  Select Rows by Condition

  Lesson 7: Pandas Dataframes

  24:16

  Filter DataFrames with Boolean conditions, update or remove rows and columns, and use describe to generate statistical summaries.

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  AT & IAT

  Lesson 7: Pandas Dataframes

  13:29

  Use at and iat for efficient scalar value assignment, apply string filtering with str.contains, and clean text data with str.replace.

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  Dataframes from Dictionary

  Lesson 7: Pandas Dataframes

  25:15

  Create DataFrames from dictionaries, concatenate multiple DataFrames, clean mismatched columns, and manage indices appropriately.

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  Dataframes Challenges

  Lesson 7: Pandas Dataframes

  6:28

  Add new rows to a DataFrame, then create filtered DataFrames based on calorie limits or multi-word names.

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  String Split

  Lesson 7: Pandas Dataframes

  12:39

  Split strings into lists using delimiters, remove file extensions, and manipulate string components with list operations.

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  Select and Filter by Condition

  Lesson 7: Pandas Dataframes

  21:09

  Filter and update DataFrames using multiple logical conditions and create new columns based on calculated values.

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  Vector Operations

  Lesson 7: Pandas Dataframes

  9:16

  Perform vectorized operations on DataFrame columns, sort and rename columns, and rearrange column order using pop and insert.

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  Introduction to Matplotlib

  Lesson 8: Bar Charts with Matplotlib

  14:46

  Use Matplotlib to create charts and visualizations, load CSV data into pandas, connect to Google Drive, and calculate summary statistics.

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  Python Data Selection

  Lesson 8: Bar Charts with Matplotlib

  18:01

  Review slicing techniques with loc and iloc, shuffle and split datasets, and group and sort data by defined criteria.

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  Data Visualization with Matplotlib

  Lesson 8: Bar Charts with Matplotlib

  21:21

  Create bar charts with Matplotlib, label bars clearly, sort data by multiple criteria, and adjust visual presentation.

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  Line and Scatter Charts with Matplotlib

  Lesson 9: Line & Scatter Charts with Matplotlib

  13:56

  Plot scatter and line charts with Matplotlib, add regression lines, and highlight minimum and maximum values.

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  Linear Regression with Matplotlib

  Lesson 9: Line & Scatter Charts with Matplotlib

  26:39

  Apply NumPy and Matplotlib together to perform linear regression, visualize scatter data, and calculate best-fit lines for analysis.

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  Pivot, Plot, Animate

  Lesson 10: Pivot Population Data & Charts

  9:59

  Pivot datasets, generate pie charts and bar chart races, and manage missing data in data science workflows.

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  Data Cleaning

  Lesson 10: Pivot Population Data & Charts

  27:01

  Handle missing data by dropping, filling with averages, removing sparse columns, and exporting clean DataFrames to CSV files.

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  Two Different Ways to Make a Bar Chart

  Lesson 10: Pivot Population Data & Charts

  18:52

  Create bar charts by extracting and indexing country population data for accurate labeling.

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  Pivoting the Data

  Lesson 10: Pivot Population Data & Charts

  16:20

  Pivot population data to restructure years and countries, then visualize trends with line and bar charts.

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  There is Always Time for Pie (Charts)

  Lesson 10: Pivot Population Data & Charts

  10:54

  Generate a pie chart illustrating population shares of selected countries and save the visualization as an image file.

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  Animated Bar Chart Race

  Lesson 10: Pivot Population Data & Charts

  13:10

  Install and configure the bar chart race library and generate an animated visualization showing population changes over time.

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  Python for Machine Learning Introduction

  Lesson 1: Basic Regression Analysis

  2:08

  Cover regression analysis, supervised learning essentials, k-nearest neighbors algorithm, random forest classifiers, and neural networks in a structured course.

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  Colab Setup

  Lesson 1: Basic Regression Analysis

  3:23

  Download the class files zip archive, extract it, and use Google Colab to upload and connect the first Jupyter Notebook to Google Drive.

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  Drive Setup

  Lesson 1: Basic Regression Analysis

  3:38

  Upload the Python Machine Learning Bootcamp folder directly to My Drive in Google Drive to ensure all file paths work correctly in Google Colab.

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  Jupyter Setup

  Lesson 1: Basic Regression Analysis

  10:14

  Learn how to use Jupyter Notebooks for running and explaining Python code blocks in data science and machine learning courses.

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  Loading The Cars Data

  Lesson 1: Basic Regression Analysis

  7:50

  Summarize the process of setting up and troubleshooting Python imports, URLs, and dataframes in Jupyter Notebooks using statistical libraries and CSV files.

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  Pandas Dataframe Slices

  Lesson 1: Basic Regression Analysis

  8:06

  Explore subsets of car data using `iloc` for index-based slicing and `loc` for label-based, more human-readable slicing.

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  Challenge 1 Slices

  Lesson 1: Basic Regression Analysis

  1:01

  Retrieve the last three rows and last three columns of the data frame using both ILOC and LOC methods.

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  Challenge 1 Slices Solution

  Lesson 1: Basic Regression Analysis

  7:48

  Use iloc or loc to select the last three rows and columns of a DataFrame, ensuring correct index handling and semantic representation for dynamic updates.

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  Initial Data Analysis Tools

  Lesson 1: Basic Regression Analysis

  11:33

  Analyze statistical data using Python to find minimum, maximum, and mean values from lists and data frames, employing functions like `min()`, `max()`, and NumPy's `.mean()`, while using methods to handle specific data structures like pandas series.

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  Median

  Lesson 1: Basic Regression Analysis

  6:48

  Explain how the median provides a more accurate representation of typical data than the mean when outliers are present.

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  Mode

  Lesson 1: Basic Regression Analysis

  2:49

  Explain how to calculate the mode using the `stats.mode` function from SciPy, which returns a tuple with the most frequent value and its count.

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  Tuples

  Lesson 1: Basic Regression Analysis

  5:43

  Understand and utilize tuple unpacking in Python to efficiently work with multiple returned values from functions, ensuring immutability and correct assignment order.

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  Averages In Real Data

  Lesson 1: Basic Regression Analysis

  6:35

  Analyze a dataset's central tendencies using pandas methods to calculate the mean, median, and mode, considering the context of outliers and data repetition.

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  Standard Deviation And The Bell Curve

  Lesson 1: Basic Regression Analysis

  4:42

  Explain standard deviation as a measure of how much values deviate from the mean in a normal distribution, where one standard deviation includes about 68.2% of values, two standard deviations cover 95.4%, and three standard deviations encompass nearly all values.

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  Calculating Standard Deviation

  Lesson 1: Basic Regression Analysis

  2:17

  Calculate standard deviation using NumPy, adjusting for degrees of freedom if analyzing a sample.

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  Variance

  Lesson 1: Basic Regression Analysis

  3:04

  Calculate variance as the square of standard deviation, useful for mathematical statistical work.

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  Percentile

  Lesson 1: Basic Regression Analysis

  2:02

  Calculate percentiles using NumPy's percentile method to understand dataset distribution.

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  Challenge 2 Percentile From User Input

  Lesson 1: Basic Regression Analysis

  1:28

  Prompt the user for a percentile and print the corresponding age at which that percent of people are younger using the input function and np.percentile.

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  Challenge 2 Percentile From User Input Solution

  Lesson 1: Basic Regression Analysis

  3:18

  Create an input box to enter a percentile and calculate the corresponding age using NumPy's percentile function.

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  Uniform Distribution Histograms

  Lesson 1: Basic Regression Analysis

  7:13

  Comment out the code to avoid repeated prompts, and create histograms using NumPy and PyPlot to visualize the frequency distribution of uniformly distributed random numbers, showing smoother distributions with larger sample sizes.

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  Normal Distribution Histograms

  Lesson 1: Basic Regression Analysis

  4:11

  Illustrate a normal distribution using NumPy to generate 250,000 random scores centered around a mean of 100 with a standard deviation of 15, and visualize the data with a histogram to show the bell curve.

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  Linear Regression

  Lesson 1: Basic Regression Analysis

  2:43

  Predict the relationship between x and y variables using linear regression by minimizing the variance, or the sum of squared differences, between the line and data points.

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  Plotting Attendance Against Concessions

  Lesson 1: Basic Regression Analysis

  5:56

  Perform a linear regression to predict concessions revenue based on baseball game attendance data using NumPy's polyfit function and visualize it with a line plot.

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  Using Pandas For Vector Operations

  Lesson 1: Basic Regression Analysis

  2:02

  Create a data frame using pandas to perform vector operations on attendance and concessions data for predictive analysis.

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  A Prediction Concessions Function

  Lesson 1: Basic Regression Analysis

  4:50

  Create a Python function called `predict_concessions` to calculate expected concession revenue based on attendance, using a linear regression formula, and ensure the result is rounded to the nearest dollar for readability and ease of updates in one place.

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  Predicting Concessions From User Input

  Lesson 1: Basic Regression Analysis

  3:32

  Create a program that allows ballpark employees to input attendance numbers to predict concessions revenue using a predefined formula.

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  Adding Predictions To Our Data

  Lesson 1: Basic Regression Analysis

  2:50

  Add predicted concessions values to the data frame by mapping the `predict_concessions` function over the attendance column.

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  Polynomial Regressions

  Lesson 1: Basic Regression Analysis

  8:45

  Implement polynomial regression to fit a curve to the data, but avoid overfitting by not using too many polynomial degrees.

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  Supervised Learning Intro

  Lesson 2: Supervised Learning Essentials

  7:45

  Set up your notebook for machine learning by importing necessary libraries and preparing data for supervised learning, including splitting it into training and testing sets.

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  Car Data Overview

  Lesson 2: Supervised Learning Essentials

  4:37

  Predict car sales using a linear regression model, analyze and clean data using Scikit-learn and Pandas, and determine important features through visualization and correlation matrices.

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  Picking Features From Domain Knowledge

  Lesson 2: Supervised Learning Essentials

  4:10

  Determine which data is important for training the model by analyzing both domain knowledge and data analysis.

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  Correlation Matrixes

  Lesson 2: Supervised Learning Essentials

  5:29

  Analyze the correlation matrix to determine that "sales in thousands" is not significantly correlated with other variables and may not be essential for model training.

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  Pair Plots Intro

  Lesson 2: Supervised Learning Essentials

  1:41

  Visualize data relationships using a Seaborn pair plot to display scatter plots for correlations and histograms for individual variables.

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  Pair Plot Analysis

  Lesson 2: Supervised Learning Essentials

  4:02

  Create a Seaborn pair plot to visually explore relationships and distributions between variables in a dataset.

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  Cleaning Data

  Lesson 2: Supervised Learning Essentials

  7:29

  Clean data, select features (fuel efficiency, engine size, horsepower), split into training/testing sets, and prepare for linear regression model training to predict car prices.

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  Splitting Training Data From Test Data

  Lesson 2: Supervised Learning Essentials

  6:11

  Split data into training and testing sets using `train-test-split` to ensure 80% for training and 20% for testing.

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  Scaling the Data

  Lesson 2: Supervised Learning Essentials

  6:23

  Scale the X train and X test data using scikit-learn's StandardScaler to ensure all features have a mean of zero and are measured in standard deviations, preventing the model from detecting false patterns based on varying scales.

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  Linear Regression Model Intro

  Lesson 2: Supervised Learning Essentials

  2:50

  Use a linear regression model to perform continuous classification by predicting numeric values from data.

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  Training the Linear Regression Model

  Lesson 2: Supervised Learning Essentials

  1:24

  Train the model using model.fit with X train and Y train data for linear regression.

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  Comparing Predictions to Test Data

  Lesson 2: Supervised Learning Essentials

  2:55

  Evaluate the model's predictions against test data by comparing predicted values to actual values.

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  Accuracy Score

  Lesson 2: Supervised Learning Essentials

  3:39

  Evaluate the accuracy of the linear regression model by comparing its predictions to the mean prediction, resulting in a 69% improvement.

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  Removing Outliers

  Lesson 2: Supervised Learning Essentials

  5:17

  Improve accuracy by removing outliers from the dataset before splitting it into training and testing sets.

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  Filtering Outliers Out

  Lesson 2: Supervised Learning Essentials

  1:59

  Filter car sales data to exclude rows with a price over 80,000 and an engine size over 7, reducing the dataset to 150 rows for model retraining.

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  Training and Testing a Second Model

  Lesson 2: Supervised Learning Essentials

  8:15

  Try removing outliers and using larger datasets to improve model accuracy.

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  Logistic Regression Intro

  Lesson 2: Supervised Learning Essentials

  3:53

  Use logistic regression to predict whether employees will stay or leave based on HR data.

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  Hr Data Overview

  Lesson 2: Supervised Learning Essentials

  2:35

  Check for null values and visualize how many people left or stayed using HR data, revealing that 11,428 stayed and 3,571 left.

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  Data Analysis Crosstabs

  Lesson 2: Supervised Learning Essentials

  2:16

  Crosstabulate the "left" and "salary" columns in the HR data using Pandas to analyze the impact of salary on employee retention.

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  Making Our Crosstab More Readable

  Lesson 2: Supervised Learning Essentials

  4:11

  Rename indices to "stayed" and "left," move "high" column to the left, and convert categorical values to numerical data for computer processing.

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  One Hot Encoding

  Lesson 2: Supervised Learning Essentials

  3:57

  Convert categorical salary data into numerical format using one-hot encoding with Pandas get_dummies, creating separate columns for low, medium, and high values.

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  Concatenating Ohe Data

  Lesson 2: Supervised Learning Essentials

  1:45

  Concatenate the high, low, and medium columns to the right side of the data frame before splitting it into testing and training data.

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  Training a Logistic Regression Model

  Lesson 2: Supervised Learning Essentials

  4:26

  Create and evaluate a logistic regression model using the specified columns for predicting whether employees left or stayed, scaling the data as needed.

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  Measuring Accuracy

  Lesson 2: Supervised Learning Essentials

  5:42

  Evaluate the model's predictions to identify true positives, true negatives, false positives, and false negatives, achieving a 77% overall accuracy.

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  Confusion Matrix

  Lesson 2: Supervised Learning Essentials

  4:35

  Analyze the confusion matrix to identify where the model misclassified predictions, particularly noting that it accurately predicted only 25% of those who actually left.

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  Precision and Recall

  Lesson 2: Supervised Learning Essentials

  5:22

  Evaluate model performance using precision and recall, noting high accuracy in predicting who stayed but poor recall for those who left, highlighting the importance of directionality in errors depending on use case.

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  Knn Intro

  Lesson 3: K-Nearest Neighbors

  2:13

  Explore the k-nearest neighbors (KNN) algorithm to classify data points based on the values of their nearest existing points.

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  Visualizing Knn

  Lesson 3: K-Nearest Neighbors

  2:04

  Classify new data points by identifying the class of the majority among their k-nearest neighbors.

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  Plotting Training Data

  Lesson 3: K-Nearest Neighbors

  3:02

  Visualize k-nearest neighbors by plotting categorized x and y coordinates using pyplot's scatter plot function.

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  Creating and Training Our Knn Model

  Lesson 3: K-Nearest Neighbors

  3:13

  Train a k-neighbors classifier using a k-value of three on saved data points and classes, then test its prediction on a new data point.

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  Visualizing Our New Point

  Lesson 3: K-Nearest Neighbors

  4:41

  Visualize the new data point with a unique class and predict its class using the KNN model.

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  Visualizing the Prediction

  Lesson 3: K-Nearest Neighbors

  2:06

  Append the integer prediction to the classes list and visualize the newly classified point using k-nearest neighbors.

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  Iris Knn Intro

  Lesson 3: K-Nearest Neighbors

  2:29

  Apply K-Nearest Neighbors to the iris dataset using sepal and petal measurements to classify flowers with high accuracy.

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  Visualizing Multi Dimensional Datasets

  Lesson 3: K-Nearest Neighbors

  2:43

  Visualize and analyze iris species data using K-Nearest Neighbors across multiple dimensions to classify new samples.

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  Loading and Reviewing Iris Data

  Lesson 3: K-Nearest Neighbors

  2:40

  Load and explore the Iris dataset using sklearn to understand its structure, including data arrays, target classifications, and target names.

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  Creating Iris Dataframe

  Lesson 3: K-Nearest Neighbors

  8:29

  Convert the iris dataset into a pandas DataFrame, add a target column with numerical values (0, 1, 2), and then apply a function or lambda to map these target values to their corresponding species names, creating a new 'species' column for human readability.

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  Prepping Our Data for Knn

  Lesson 3: K-Nearest Neighbors

  2:58

  Prepare the dataset by splitting it into training and testing sets for features (X) and targets (Y) using 20% of the data for testing.

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  Training and Testing Our Knn Model

  Lesson 3: K-Nearest Neighbors

  1:58

  Train the k-neighbors classifier with 3 neighbors on the training data and compare predictions against actual test data.

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  Creating a Classification Report

  Lesson 3: K-Nearest Neighbors

  3:08

  Score the model by calculating accuracy and generating a classification report to identify prediction errors.

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  Analyzing and Summing Up Knn Results

  Lesson 3: K-Nearest Neighbors

  3:07

  Identify a virginica misclassified as a versicolor due to its closeness to the versicolor group.

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  Titanic Dataset Intro

  Lesson 4: Titanic Survival Prediction

  2:13

  Predict Titanic survival using a random forest classifier with the Kaggle dataset.

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  Titanic Dataset Closer Look

  Lesson 4: Titanic Survival Prediction

  2:23

  Analyze and clean the Titanic dataset by addressing missing values in age and embarked columns while ignoring the cabin data.

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  Filling in Na Values

  Lesson 4: Titanic Survival Prediction

  8:43

  Fill missing ages in the Titanic dataset with gender-specific mean values and replace missing 'embarked' entries with the mode 'S'.

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  Plotting Survived vs Perished

  Lesson 4: Titanic Survival Prediction

  2:00

  Analyze the survival rates and passenger class distribution of Titanic data using Seaborn count plots.

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  Plotting by 2 Columns

  Lesson 4: Titanic Survival Prediction

  3:37

  Graph passenger class distribution and survival rates by class using Seaborn to identify potential predictive features for analysis.

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  Plotting More Data

  Lesson 4: Titanic Survival Prediction

  2:10

  Analyze Titanic survival rates by gender and port of embarkation to identify potential influential factors.

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  Graphing a Combined Column

  Lesson 4: Titanic Survival Prediction

  5:37

  Combine passenger class and gender into a new categorical column "p-class sex" in the Titanic dataset to analyze survival patterns based on these combined factors.

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  Label Encoding

  Lesson 4: Titanic Survival Prediction

  3:31

  Use LabelEncoder to transform categorical variables into numerical values for easier processing.

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  Splitting and Scaling Our Data

  Lesson 4: Titanic Survival Prediction

  4:40

  Split and scale the Titanic dataset using standard scaler before applying random forest classifiers.

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  Random Forest Classifiers

  Lesson 4: Titanic Survival Prediction

  4:42

  Explain how random forest classifiers use multiple decision trees to make predictions by averaging diverse subsets of data.

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  Creating and Training Our Model

  Lesson 4: Titanic Survival Prediction

  0:53

  Create and train a random forest classifier model with the training data, then proceed to test it.

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  Cleaning Up the Test Data

  Lesson 4: Titanic Survival Prediction

  8:01

  Prepare and clean the X test data by filling missing ages with mean values by gender and filling the missing fare with the mean fare before labeling, encoding, scaling, and submitting the predictions to Kaggle.

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  Label Encoding and Scaling Our Test Data

  Lesson 4: Titanic Survival Prediction

  4:50

  Label, encode, and scale the specified columns in the dataset to prepare for model submission.

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  Fixing Column Order Error

  Lesson 4: Titanic Survival Prediction

  1:09

  Correct the column order by moving age to the fourth position and rerun the code to ensure accuracy.

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  Submitting to Kaggle

  Lesson 4: Titanic Survival Prediction

  7:49

  Create a predictions array from the model, form a data frame with passenger IDs and predictions, save it as a CSV without an index, and submit it to Kaggle for scoring.

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  Neural Networks Intro

  Lesson 5: Neural Networks

  4:30

  Explore neural networks, focusing on translating handwritten digits using TensorFlow and Keras, while learning data visualization, normalization, and model training techniques.

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  Notebook Setup

  Lesson 5: Neural Networks

  1:15

  Import libraries and set up Google Drive and TensorFlow for the neural network.

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  Task Intro

  Lesson 5: Neural Networks

  2:16

  Analyze the MNIST dataset of handwritten digits to train a machine learning model for accurate digit recognition.

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  Analyzing the Shape of Our Data

  Lesson 5: Neural Networks

  6:09

  Analyze the MNIST dataset from TensorFlow to understand its structure as immutable tuples containing training and testing data divided into images and labels.

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  Unpacking All Our Data

  Lesson 5: Neural Networks

  2:41

  Unpack the data into training and testing images and labels to confirm their shapes and types.

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  Looking at a Digit Array Part 1

  Lesson 5: Neural Networks

  5:28

  Explore how to train a neural network model using 60,000 grayscale 28x28 pixel images of handwritten digits represented as NumPy arrays.

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  Looking at a Digit Array Part 2

  Lesson 5: Neural Networks

  1:59

  Visualize the array as a stretched-out image to see it resembles a 5.

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  Training and Testing Review

  Lesson 5: Neural Networks

  3:51

  Train the model using 60,000 handwritten digit images to recognize patterns and improve accuracy before testing with 10,000 new images.

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  Normalizing Our Data

  Lesson 5: Neural Networks

  5:18

  Normalize data by dividing each pixel value by 255 to scale them between zero and one for both training and testing images.

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  Building a Neural Network

  Lesson 5: Neural Networks

  12:06

  Build a three-layer neural network using a sequential Keras model with an input layer that flattens a 28x28 input, a dense hidden layer with 128 neurons using the ReLU activation function, and an output layer with 10 neurons using the softmax activation.

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  Training Our Model

  Lesson 5: Neural Networks

  4:37

  Compile the neural network model, train it over multiple epochs, and observe accuracy improvements and loss reductions.

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  Analyzing Predictions

  Lesson 5: Neural Networks

  11:22

  Analyze the predictions' accuracy by comparing predicted digits to correct labels and preparing for further evaluation and tuning in the next lesson.

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  APIs Intro

  Lesson 1: APIs

  5:05

  Access data from an API using Python by sending an HTTP request to a specific URL and receiving the expected data response.

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  API Keys

  Lesson 1: APIs

  8:13

  Explore the setup and use of the AlphaVantage stock market data API, including obtaining an API key and configuring a Python program to request stock data.

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  HTTP Status Codes

  Lesson 1: APIs

  5:03

  Access an API using Python's requests library to send a GET request to a URL, check the response's status code for errors, and handle data retrieval upon receiving a 200 OK status.

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  JSON

  Lesson 1: APIs

  3:58

  Convert a JSON string response from a web request into a Python dictionary or list using the .json() method from the requests library.

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  Challenge 1 Navigating API Data

  Lesson 1: APIs

  0:47

  Navigate the data variable to find and print the stock information for Apple on the specified date.

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  Challenge 1 Navigating API Data Solution

  Lesson 1: APIs

  4:29

  Access the desired date's data within the time series daily key in the API response dictionary, then retrieve the closing value for Apple on that date.

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  Making a Dataframe From Api Data

  Lesson 1: APIs

  4:22

  Transpose the data frame, convert strings to numeric values, and format the index as date times for easier data manipulation.

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  Challenge 2 Finding Highest Value

  Lesson 1: APIs

  0:53

  Find the highest and lowest stock prices and their respective dates from the data frame.

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  Challenge 2 Finding Highest Value Solution

  Lesson 1: APIs

  3:07

  Find the highest apple price using the numeric value in the dataset and identify the date of that price.

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  Plotting Our Api Data and a Plotting Challenge

  Lesson 1: APIs

  3:29

  Plot the low and high apple prices using pyplot's scatterplot method by identifying the lowest and highest prices with their corresponding dates and adding these points to the existing graph.

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  Plotting Our Data With Bmh and Scatter

  Lesson 1: APIs

  1:47

  Add scatter plots for the highest and lowest apple prices using green and red markers respectively.

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  APIs Conclusion

  Lesson 1: APIs

  3:13

  Access data using APIs by researching, understanding their structure, and extracting information with minimal code.

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  Data Scraping Intro

  Lesson 2: Data Scraping

  4:37

  Access data by programmatically scraping it from third-party websites while considering ethical and legal implications.

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  Data Scraping and HTML

  Lesson 2: Data Scraping

  7:04

  Learn to understand and utilize HTML structure for web scraping by identifying and inspecting elements like headings (H3) to extract specific data efficiently.

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  Our First Data Scraping

  Lesson 2: Data Scraping

  8:54

  Parse an HTML page using BeautifulSoup to extract and print specific text elements like act and scene names from H3 tags.

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  Accessing One Part of a Page

  Lesson 2: Data Scraping

  4:19

  Use BeautifulSoup to extract the desired text from the HTML.

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  Challenge 3: Scraping on Your Own

  Lesson 2: Data Scraping

  0:51

  Scrape and print specified text from the page, then find and print the text of the first 10 tags.

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  Challenge 4: Scraping on Your Own

  Lesson 2: Data Scraping

  3:04

  Identify and extract text from specific HTML elements using BeautifulSoup for web scraping.

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  More Complex Data Scraping Queries

  Lesson 2: Data Scraping

  8:37

  Extract attribute values from HTML elements and find nested elements within specified parent elements.

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  Challenge: Scraping a New Page

  Lesson 2: Data Scraping

  3:14

  Scrape book titles and prices from books2scrape.com using the requests and Beautiful Soup libraries.

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  Challenge: Scraping a New Page Solution

  Lesson 2: Data Scraping

  12:49

  Extract book titles and prices from a webpage using BeautifulSoup by identifying specific HTML tags, handling issues with truncated titles and currency symbols, and converting prices to numerical values.

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  Saving our Scraped Data in a Dataframe

  Lesson 2: Data Scraping

  1:33

  Convert data into a Pandas DataFrame and prepare to scrape all pages for complete results.

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  Challenge: Scraping the Page Number Max

  Lesson 2: Data Scraping

  1:36

  Extract the text from the page element indicating "page 1 of 50" and retrieve the last word to determine the total number of pages.

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  Challenge: Scraping the Page Number Max Solution

  Lesson 2: Data Scraping

  3:36

  Extract the maximum page number by finding the LI element with the class "current," splitting its text into words, and converting the last word to an integer.

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  Scraping an Entire Website

  Lesson 2: Data Scraping

  8:41

  Loop through pages, scrape book titles and prices, and compile into a data frame with 1,000 entries.

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  Comparing APIs and Data Scraping

  Lesson 3: Comparing APIs and Data Scraping

  3:35

  Compare using APIs and web scraping in Python to access structured and unstructured data, respectively.

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  Course Intro

  Lesson 1: Environment Setup & Foundations

  0:59

  Learn to build interactive data visualization apps using Dash by Plotly from setup to advanced interactivity.

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  Getting Our Class Files

  Lesson 1: Environment Setup & Foundations

  3:13

  Download the class files from GitHub as a ZIP, unzip them directly into your Downloads folder (avoiding nested folders), and prepare for Anaconda installation.

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  Installing Anaconda

  Lesson 1: Environment Setup & Foundations

  2:31

  Download and install Anaconda from anaconda.com, selecting the correct version for your operating system and following the installation instructions.

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  Our First Terminal Command

  Lesson 1: Environment Setup & Foundations

  4:23

  Learn to open and use a terminal emulator to run basic text-based commands like echo in a command-response cycle.

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  Understanding the Prompt

  Lesson 1: Environment Setup & Foundations

  5:34

  Understand the terminal prompt as an indicator of your current Python environment, user, computer, and directory, signaling when it’s ready to accept commands.

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  Creating Our Python Environment

  Lesson 1: Environment Setup & Foundations

  7:22

  Create and activate a Python virtual environment using conda to isolate project-specific dependencies.

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  Configuring Our Python Environment

  Lesson 1: Environment Setup & Foundations

  4:42

  Configure the DVENV Python environment to use CondaForge as the exclusive package source and install the latest versions of pandas, dash, and dash-bootstrap-components.

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  Opening Our Class Files

  Lesson 1: Environment Setup & Foundations

  3:37

  Open your curriculum folder in Visual Studio Code, navigate to app.py in Notebook 1, and prepare to write and run code.

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  Running Our Dash App

  Lesson 2: Building Core Dash Visuals

  5:49

  Navigate to your project directory in the terminal, activate the dvenv environment, run your app.py file using the python command, and open the provided local URL in your browser to view your Dash app.

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  Intro to Dash

  Lesson 2: Building Core Dash Visuals

  5:48

  Understand how Dash, a Python library, builds interactive web dashboards by converting Python code into HTML, CSS, and JavaScript, and learn how to run, modify, and refresh a basic Dash app using app layout definitions and live server updates.

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  An HTML Primer

  Lesson 2: Building Core Dash Visuals

  7:55

  Understand basic HTML structure and tags like H1-H6 for headings, P for paragraphs, and DIV/SPAN for layout and inline text, focusing on how elements nest as parent and child.

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  Formatting Our Data

  Lesson 2: Building Core Dash Visuals

  11:45

  Import stock data from a CSV file using Pandas, filter the top five stocks by trading volume, convert volume figures to millions, round them to one decimal place, and prepare the data for visualization.

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  Creating a Pie Chart

  Lesson 2: Building Core Dash Visuals

  6:32

  Create a pie chart using Plotly Express to visualize the top five stocks by volume and embed it into the dashboard using Dash core components.

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  Why Data Viz and Why Dash

  Lesson 2: Building Core Dash Visuals

  4:03

  Explain how simple data visualizations like pie charts, built with Dash, help reveal insights—such as Apple’s dominance in trading volume—and offer advantages over tools like Jupyter Notebooks for creating interactive, shareable web applications.

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  Composing Elements

  Lesson 2: Building Core Dash Visuals

  4:55

  Add a heading, author name, and pie chart as a list of children to an HTML.Div to structure the dashboard layout.

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  Creating a Bar Chart

  Lesson 2: Building Core Dash Visuals

  4:47

  Compare company stock volumes using a bar chart to highlight relative differences versus overall proportions shown in a pie chart.

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  Adding Further Visualizations

  Lesson 2: Building Core Dash Visuals

  7:45

  Explore different data visualizations—horizontal bar and scatter plots—to assess data relationships and determine the most effective format for analysis.

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  A Quick Demo of Dash Interactivity

  Lesson 2: Building Core Dash Visuals

  2:51

  Explore interactive Dash features that allow users to zoom, filter, and analyze specific data segments directly within visualizations.

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  Summing Up Section 1

  Lesson 2: Building Core Dash Visuals

  1:44

  Build a functional dashboard using Dash with interactive layouts and data visualizations.

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  VS Code Jupyter Setup

  Lesson 3: Pandas & Function Essentials

  5:09

  Review basic Python and pandas skills in Jupyter Notebooks using VS Code with the correct extensions and environment setup to prepare for advanced data visualization with Dash.

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  Reading in Our Data to a Notebook

  Lesson 3: Pandas & Function Essentials

  4:18

  Import pandas, read a CSV of company stock and market data into a DataFrame, and interact with it in a Jupyter notebook using VS Code.

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  Pandas Practice

  Lesson 3: Pandas & Function Essentials

  15:00

  Practice pandas by transforming market cap values, dropping unnecessary columns and zero-priced rows, filtering for specific companies, calculating statistics like max and average prices, counting entries under a threshold, and retrieving targeted data points.

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  Creating a Pandas and Pyplot Pie Chart

  Lesson 3: Pandas & Function Essentials

  7:34

  Group companies by country, sum their market caps, sort to find the top five, and plot them in a pie chart using matplotlib.

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  Viewing Our Pie Chart

  Lesson 3: Pandas & Function Essentials

  1:51

  Install missing packages as needed during development, then proceed with data formatting and plotting in the notebook.

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  Reintroducing Functions

  Lesson 3: Pandas & Function Essentials

  5:54

  Learn how to define and use functions, including lambdas and apply, for advanced data manipulation in a data frame.

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  Challenge Writing Your Own Function

  Lesson 3: Pandas & Function Essentials

  4:46

  Define a function addCents that returns a given dollar amount plus a random number of cents rounded to two decimal places using random.random.

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  Named Lambdas

  Lesson 3: Pandas & Function Essentials

  3:58

  Use lambda functions to create concise, one-line operations that take inputs and return computed values without requiring formal function definitions.

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  Apply

  Lesson 3: Pandas & Function Essentials

  4:28

  Create a DataFrame with numbers from 10,000 to 10,010 and use apply to transform the column with function calls, including chaining and anonymous lambdas.

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  Anonymous Lambdas With Apply

  Lesson 3: Pandas & Function Essentials

  6:01

  Use both a named function and an anonymous lambda with apply to convert numeric values into formatted dollar strings, optionally adding commas for readability.

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  Apply Challenge Solution

  Lesson 3: Pandas & Function Essentials

  4:08

  Format dollar amounts with commas using a custom function or lambda and apply it to a DataFrame column.

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  Using Apply With Real Data

  Lesson 3: Pandas & Function Essentials

  8:31

  Convert string-based percentage and volume columns from a stock CSV file into usable numeric formats using pandas, apply, lambda functions, and string manipulation.

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  Lambdas With Ternaries

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  9:27

  Create a column called advice using apply with a lambda that returns "buy" if last_price < 200, else "sell".

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  Exploring The Chipotle Data

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  7:21

  Analyze Chipotle order data to extract item prices, identify the most expensive item, and prepare for revenue calculations.

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  Grouping To Get Total Revenue

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  4:00

  Group Chipotle order data by item name to calculate total quantities and total revenue for each item, then extract specific values (like for "chicken bowl") using .loc for further analysis and visualization.

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  Graphing Our Revenue

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  4:55

  Identify the top-selling items by revenue using n-largest, then plot the top 5 as a bar chart with proper labels and styling.

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  Dash App Setup Redux

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  5:27

  Set up your Dash app by activating the correct Python environment and navigating to the proper project directory.

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  Manipulating The Data In A Python File

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  5:31

  Run the Dash app from the Notebook2 directory, load and clean the Chipotle CSV data, compute revenue by item, extract the top five items, and prepare figures for dashboard display.

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  Adding A Pie Graph

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  4:54

  Replace the header with a div containing a dcc.Graph that displays a Plotly Express pie chart based on the top five item prices.

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  Adding A Second Graph

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  3:26

  Add a bar chart alongside the pie chart using a list of multiple dcc.Graph components to display both figures with built-in interactivity.

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  Adding All Graphs

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  2:55

  Add additional bar and scatter plots, adjust their orientation and visual properties, then address layout issues by organizing the graphs using Bootstrap for better presentation.

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  Bootstrap

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  10:35

  Use Bootstrap’s responsive layout system with containers, rows, and columns to arrange Dash components cleanly and control their sizing across screen sizes.

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  Final Dashboard Intro

  Lesson 5: Interactive Dashboards & Final Project

  1:46

  Create an interactive Dash dashboard that updates visualizations based on user input using car sales data from carsales.csv.

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  A Basic Scatterplot

  Lesson 5: Interactive Dashboards & Final Project

  5:36

  Create a Dash app in app.py that reads car-sales.csv, builds a Plotly Express scatter plot of horsepower vs fuel efficiency with color indicating price, and displays it in a web layout using Dash components.

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  Running Our Dash App

  Lesson 5: Interactive Dashboards & Final Project

  4:13

  Run the app in the DVEnv environment, view the interactive graph in a browser, and prepare to add company filtering, engine size slider, and detailed tooltips in the next step.

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  Creating A Dropdown

  Lesson 5: Interactive Dashboards & Final Project

  5:14

  Create a dropdown using Dash to list unique car manufacturers from a dataset and prepare it to filter the graph based on selection.

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  Using Dash Callbacks

  Lesson 5: Interactive Dashboards & Final Project

  13:54

  Use a Dash callback decorator to update a graph dynamically based on a user's dropdown selection, replacing the initial figure on page load.

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  Outputting From Our Callback

  Lesson 5: Interactive Dashboards & Final Project

  3:34

  Implement interactive filtering of a car dataset by manufacturer using a dropdown, and update the graph accordingly with an option to display all manufacturers.

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  Adding An All Option

  Lesson 5: Interactive Dashboards & Final Project

  7:05

  Insert an "All Manufacturers" option at the top of the drop-down list, set it as the default selection, and adjust the data filtering logic to display all cars when it's selected.

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  Adding A Range Slider

  Lesson 5: Interactive Dashboards & Final Project

  4:31

  Create a range slider using Pandas min and max engine size values to filter engine sizes from minimum to maximum in step increments of one.

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  Making The Slider Interactive

  Lesson 5: Interactive Dashboards & Final Project

  7:21

  Connect the engine size slider to the graph update function by adding it as an input, filter the data based on the selected range, and update the graph accordingly.

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  Hover Data Intro

  Lesson 5: Interactive Dashboards & Final Project

  2:25

  Enable interactive Dash dashboards by using user actions like hovering to dynamically display additional data in designated output sections.

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  The Final Interactive Element

  Lesson 5: Interactive Dashboards & Final Project

  22:43

  Create a new Dash callback that updates an info panel with detailed car data based on hover events from a graph.

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  Course Outro

  Lesson 5: Interactive Dashboards & Final Project

  0:40

  Explore data that interests you and apply your Dash skills to create interactive visualizations.

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  Getting Started

  Lesson 1: Using VSCode & Flask

  10:00

  Build web applications using Python’s Flask framework and the OpenAI API, starting from core concepts and assuming foundational Python programming knowledge, while setting up VS Code and an OpenAI account.

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  Set Up VS Code for Flask

  Lesson 1: Using VSCode & Flask

  37:40

  Create a new VS Code project, set up a virtual environment, install Flask, and run a basic Hello World app in the browser.

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  Add an 'About' Route

  Lesson 1: Using VSCode & Flask

  11:41

  Add a new Flask route, update the homepage to link to it, and test changes by restarting the development server.

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  Flask for Rendering

  Lesson 2: Rendering HTML with Flask

  6:21

  Render full HTML pages with Flask by using render_template, creating an index.html file inside a templates folder, and updating routes to return HTML instead of plain text.

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  Render an About Page

  Lesson 2: Rendering HTML with Flask

  7:47

  Build an About page by defining a new route, rendering an HTML template, and linking to the route path rather than the file name.

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  Connecting to OpenAI

  Lesson 3: Connecting with OpenAI

  36:38

  Connect a Flask app to the OpenAI API by installing the OpenAI module, configuring an API key, sending a request to a GPT model, parsing the JSON response, and displaying the AI output in the browser.

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  Prompting AI to Respond in JSON

  Lesson 3: Connecting with OpenAI

  27:00

  Prompt the AI to return structured JSON responses, parse them into Python dictionaries, and extract specific data fields for use in the app.

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  Jinja Templating Engine for HTML

  Lesson 4: Jinja and Prompt Engineering

  15:00

  Use Jinja templating to dynamically display AI-generated content inside HTML pages rendered by Flask.

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  Prompt Engineering With JSON and Jinja Part 1

  Lesson 4: Jinja and Prompt Engineering

  42:21

  Identify and resolve JSON prompt errors caused by curly quotes or invalid characters in Python code.

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  Prompt Engineering With JSON and Jinja Part 2

  Lesson 4: Jinja and Prompt Engineering

  22:14

  Fix malformed JSON by correcting quotation marks, adding missing commas, and validating syntax before parsing.

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  Having an Ongoing Chat with AI

  Lesson 5: Building the Chat

  15:00

  Implement a terminal-based AI chat function that maintains conversation context using a list, processes user input in a loop, and appends AI responses sequentially.

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  Coding with HTML and CSS Part 1

  Lesson 6: Coding HTML & CSS

  13:37

  Design a browser-based chat interface with HTML and CSS by structuring content with semantic elements, organizing assets in static folders, styling user and AI messages, and adding a hero image.

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  Coding with HTML and CSS Part 2

  Lesson 6: Coding HTML & CSS

  21:59

  Add interactive input fields and buttons to the chat interface and prevent page reloads through JavaScript event handling.

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  JavaScript for Send Button

  Lesson 7: Using JavaScript

  15:49

  Implement JavaScript logic to capture user input, create chat message elements, and append them dynamically to the chat window.

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  JavaScript fetch() for Server Requests

  Lesson 7: Using JavaScript

  17:33

  Introduce JavaScript fetch by retrieving data from a public API and displaying results on the page in response to user interaction.

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  JavaScript fetch() Request for Flask

  Lesson 7: Using JavaScript

  17:10

  Fetch JSON data from custom Flask routes, process responses in JavaScript, and update page content dynamically.

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  Fetch & Python Flask Chat Round Trip

  Lesson 8: Improving the Chat

  15:00

  Send chat messages from the browser to a Flask server using fetch, process them server-side, and return simulated AI responses to the UI.

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  Completing the AI Chat Assistant Part 1

  Lesson 8: Improving the Chat

  18:39

  Integrate full chat workflows by sending conversation history as JSON to Flask, forwarding it to the OpenAI API, storing responses, and updating the chat interface in real time.

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  Completing the AI Chat Assistant Part 2

  Lesson 8: Improving the Chat

  26:13

  Debug JSON handling issues in an AI chat system and improve response quality by incorporating structured FAQ context.

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  AI Prompt Engineering with Product FAQ

  Lesson 8: Improving the Chat

  27:17

  Load and manage FAQ content within a Flask application to enhance an AI assistant’s ability to answer product- or service-specific questions accurately.

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  Making a File Chooser in HTML & JS

  Lesson 9: Starting the Meal Analyzer

  12:34

  Create an image upload feature by configuring a virtual environment, building HTML and JavaScript upload forms, and displaying uploaded images using Flask.

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  Sending Image Data to Flask and Returning Temp URL

  Lesson 9: Starting the Meal Analyzer

  34:34

  Send uploaded image data to Flask, generate a temporary file path, and render the image back in the browser.

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  Sending Image Data to Flask and Returning Temp URL Part 2

  Lesson 9: Starting the Meal Analyzer

  24:58

  Encode uploaded images in Base64, send them to the OpenAI API, and process JSON responses for display in the application.

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  Sending Image Data to Flask and Returning Temp URL Part 3

  Lesson 9: Starting the Meal Analyzer

  26:35

  Apply prompt engineering techniques to request structured AI analysis of images, such as extracting meal details from uploaded photos.

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  Securing API Key as Environment Variable

  Lesson 10: Finalizing the Meal Analyzer

  16:18

  Secure OpenAI API credentials by storing them as environment variables in a .env file and loading them with python-dotenv to prevent accidental exposure in version control.

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  Course Introduction

  Lesson 1: Introduction to AI in Data Analytics

  1:16

  Understand how AI enhances traditional analytics and supports descriptive, predictive, and prescriptive decision making.

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  Reframing Analytics Through AI Augmentation

  Lesson 1: Introduction to AI in Data Analytics

  5:15

  Define the four types of analytics and position AI as a tool that augments rather than replaces analysts.

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  Understanding the AI Pipeline and Managing Bias

  Lesson 1: Introduction to AI in Data Analytics

  5:56

  Explain the AI pipeline from data collection through deployment & examine how bias in data, algorithms, or deployment can lead to unfair outcomes.

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  Unlocking the Strategic Benefits of AI in Analytics

  Lesson 1: Introduction to AI in Data Analytics

  7:22

  Identify the benefits of AI including speed, accuracy, scalability, and discovery.

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  Navigating Enterprise AI Platforms and Smart Use Cases

  Lesson 1: Introduction to AI in Data Analytics

  5:18

  Compare enterprise AI platforms and BI tools that integrate AI capabilities.

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  Accelerating Descriptive Statistics with AI

  Lesson 1: Introduction to AI in Data Analytics

  6:12

  Review core descriptive statistics including mean, median, variance, distribution shape, and outliers.

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  Discovering Relationships Through Correlation

  Lesson 1: Introduction to AI in Data Analytics

  5:18

  Explain the correlation coefficient and reinforce that correlation does not imply causation.

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  Predicting Outcomes with Regression and Forecasting

  Lesson 1: Introduction to AI in Data Analytics

  7:41

  Use AI to generate and interpret predictive models in accessible language.

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  Applying Probability and Distributions in Analysis

  Lesson 1: Introduction to AI in Data Analytics

  5:33

  Define theoretical, experimental, conditional, and compound probability.

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  Modeling Risk with Monte Carlo Simulation

  Lesson 1: Introduction to AI in Data Analytics

  6:06

  Explain Monte Carlo simulation and expected value under uncertainty.

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  Integrating End to End AI Analysis

  Lesson 1: Introduction to AI in Data Analytics

  5:27

  Combine descriptive, predictive, and probabilistic techniques into a unified AI prompt.

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  Sourcing and Classifying Data for AI

  Lesson 2: Data Collection & Preparation

  6:24

  Identify internal and external data sources and understand how AI accelerates data collection through APIs, scraping, and file merging.

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  Cleaning Dirty Data with AI Assistance

  Lesson 2: Data Collection & Preparation

  4:41

  Learn to recognize common data quality issues such as missing values, duplicates, inconsistent formats, and outliers.

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  Automating Exploratory Data Review and Cleanup

  Lesson 2: Data Collection & Preparation

  6:51

  Evaluate a real dataset to identify structural issues, missing values, formatting errors, and outliers.

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  Applying the DIG Framework for AI Assisted EDA

  Lesson 3: Exploratory Data Analysis (EDA)

  7:24

  Understand EDA as the process of reviewing, visualizing, and filtering data to uncover structure, trends, and anomalies.

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  Designing Insightful Pivot Tables with AI Guidance

  Lesson 4: Data Visualization

  6:18

  Build meaningful summaries using pivot tables to compare categories, calculate metrics, and surface unusual patterns.

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  Creating Interactive Dashboards and Data Stories

  Lesson 4: Data Visualization

  7:46

  Select effective chart types such as bar, line, and scatter plots to communicate trends and relationships.

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  Building and Evaluating Predictive Models with AI

  Lesson 5: Predictive Analytics & Modeling

  4:19

  Differentiate between regression, classification, and clustering models and connect each to real business use cases.

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  Demonstrating AI Powered Classification and Model Validation

  Lesson 5: Predictive Analytics & Modeling

  5:09

  Build a predictive model that classifies satisfaction scores using an uploaded dataset and structured prompts.

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  Applying Predictive Models

  Lesson 6: Application of AI in Various Domains

  5:17

  Connect regression, classification, and clustering models to real use cases such as churn prediction, demand forecasting, workforce planning, and employee retention.

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  Leveraging AI Modeling in Finance and Healthcare

  Lesson 6: Application of AI in Various Domains

  2:03

  Apply predictive modeling to fraud detection, credit scoring, financial forecasting, and real time risk monitoring.

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  Building Scenario Models and Executive Recommendations with AI

  Lesson 6: Application of AI in Various Domains

  4:05

  Generate revenue, cost, and profit scenarios using AI driven modeling and compare financial outcomes across strategic options.

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  Analyzing Unstructured Text with Natural Language Processing

  Lesson 7: Advanced AI Techniques

  5:55

  Apply NLP techniques such as sentiment analysis, keyword extraction, topic clustering, summarization, and text classification to transform unstructured reviews and comments into structured insights.

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  Forecasting Trends with Time Series Analysis

  Lesson 7: Advanced AI Techniques

  4:08

  Identify trends, seasonality, cycles, and anomalies in time based data such as sales or website traffic.

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  Conclusion

  Lesson 7: Advanced AI Techniques

  1:27

  Reflect on the core AI frameworks, hands on projects, and real world applications explored throughout the course.

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