Python Certification Program
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- Live or Self-paced
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Book live on Noble DesktopSummary
In our Python Certification Program, you’ll master Python fundamentals, analyze real-world datasets, and build hands-on projects that prepare you for the Certified Associate in Python Programming (PCAP) exam. This program includes:
- Our Python for Data Science Bootcamp (30 hours total)
- One free Python elective to expand your Python skills even further
- Three (3) hours of private tutoring (used in a single session)
- The Certified Associate in Python Programming (PCAP) exam with a free retake
- Additional materials to help you prepare for the exam
- Option to replace the exam with one additional hour of private tutoring
Who this course is for
Individuals looking to break into the field of data science with Python. People with minimal coding background who want to shift into more data-centric work. People who work with data in tools like SPSS, STATA, or MATLAB and would like to transition into using Python. Developers with experience in other arenas who would like to work in data science.
Curriculum
What you'll learn
- Learn foundational programming concepts including loops, functions, and objects
- Handle different types of data such as integers, floats, and strings
- Control the flow of your programs with conditional statements
- Reuse and simplify code with object-oriented programming
- Analyze tabular data with NumPy and Pandas
- Create graphs and visualizations with Matplotlib
- Make predictions with linear regression, using scikit-learn
Course outline
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Python for Data Science Bootcamp 30 hours
Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
- Learn Python fundamentals, including variables, data types, functions, loops, and control flow, for building robust programs
- Work with complex data structures such as dictionaries and lists to efficiently organize and access data
- Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration
- Generate descriptive statistics and apply filtering, grouping, and pivoting techniques to gain deeper insights
- Visualize data using Matplotlib and create clear, customized charts, including bar graphs, histograms, and scatter plots
- Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows
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Python for AI: Create AI Apps with Flask & OpenAI 30 hours
Create, style, and enhance web apps with AI capabilities. In this advanced Python course, you'll develop a powerful sentiment analysis web application with Flask and the OpenAI API integration and learn how to take your coding projects to the next level with AI.
- Set up Flask projects and create routes for web applications
- Handle GET and POST requests, and render HTML templates in Flask
- Configure and make OpenAI API requests for sentiment analysis tasks
- Design and style HTML forms, and apply CSS to your web apps
- Implement error handling and debug common issues in Flask applications
- Integrate AI models like GPT-4 into web applications using APIs
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Python Machine Learning Bootcamp 30 hours
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
- Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data
- Understand the difference between regression and classification problems and when to apply each approach
- Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest
- Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error
- Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs
- Use Python's essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms
- Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon
- Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems
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Python Data Visualization & Interactive Dashboards 24 hours
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
- Work with real-life datasets using Python’s core libraries, including NumPy for numerical computing and Pandas for data manipulation
- Create static and interactive visualizations using Matplotlib, Seaborn, and Plotly to clearly communicate trends and patterns
- Build dynamic, multi-component dashboards using Dash Enterprise, incorporating callbacks, sliders, date pickers, and more
- Practice hands-on development by applying new skills to personalized projects with guided instructor support
- Publish your dashboards online using GitHub and Heroku to demonstrate your work to potential employers or clients
- Explore best practices for styling and structuring visual narratives that are clear, persuasive, and engaging
What's included
- Free course retake within one year to refresh the material and gain practice.
- Class recordings
Stated by the provider. Confirm what your tuition covers before enrolling.
Live classes
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EST)
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In-person or live online
Tue, Thu
18:00–21:00 · America/New_York (EST)
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In-person or live online
Sun
10:00–17:00 · America/New_York (EST)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EST)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EST)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Thu, Fri
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EST)
Confirm current dates and availability on Noble Desktop before booking.
Self-paced course
Learn through recorded lessons on your own schedule.
This program is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
- Tuition
- $2,999
- Course length
- 33 hours
- Schedule
- On your schedule
In our Python Certification Program, you’ll master Python fundamentals, analyze real-world datasets, and build hands-on projects that prepare you for the Certified Associate in Python Programming (PCAP) exam. This program includes:
- Our Python for Data Science Bootcamp (30 hours total)
- One free Python elective to boost skills
- Three (3) hours of private tutoring (in one session)
- The Certified Associate in Python Programming (PCAP) exam with a free retake
- Additional materials to help you prepare for the exam
- Option to replace the exam with one additional hour of private tutoring
Who the self-paced course is for
Individuals looking to break into the field of data science with Python. People with minimal coding background who want to move into more data-centric work at their current workplace. People who work with data in tools like SPSS, STATA, or MATLAB and would like to transition into using Python and SQL. Developers with experience in other arenas, who would like to work in data science.
Self-paced curriculum
What you'll learn self-paced
- Foundational programming concepts including loops, functions, and objects
- Handle different types of data such as integers, floats, and strings
- Control the flow of your programs with conditional statements
- Reuse and simplify code with object-oriented programming
- Analyze tabular data with NumPy and Pandas
- Create graphs and visualizations with Matplotlib
- Make predictions with linear regression, using scikit-learn
Self-paced course outline
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Python for Data Science Course Online (Self-Paced) 30 hours
Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
- Foundational programming concepts, including loops, functions, and objects
- Handle different types of data such as integers, floats, and strings
- Control the flow of your programs with conditional statements
- Reuse and simplify code with object-oriented programming
- Analyze tabular data with NumPy and Pandas
- Create graphs and visualizations with Matplotlib
- Make predictions with linear regression, using scikit-learn
-
Python for AI Course Online (Self-Paced) 30 hours
Create, style, and enhance web apps with AI capabilities. In this advanced Python course, you'll develop a powerful sentiment analysis web application with Flask and the OpenAI API integration and learn how to take your coding projects to the next level with AI.
- Set up Flask projects and create routes for web applications
- Handle GET and POST requests, and render HTML templates in Flask
- Configure and make OpenAI API requests for sentiment analysis tasks
- Design and style HTML forms, and apply CSS to web apps
- Implement error handling and debug common issues in Flask applications
- Integrate AI models like GPT-4 into web applications using APIs
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Python Machine Learning Course Online (Self-Paced) 30 hours
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
- How to clean and balance your data using the Pandas library
- Applying machine learning algorithms such as logistic regression and random forests using the scikit-learn library
- Choosing good features to use as input for your algorithms
- Properly splitting data into training, testing, and cross-validation sets
- Important theoretical concepts like overfitting, variance, and bias
- Evaluating the performance of your machine learning models
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Python Data Visualization & Interactive Dashboards Online (Self-Paced) 24 hours
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
- Plan & present a data story
- Gather and manipulate data from different sources
- Find data stories through exploratory data analysis
- Manipulate data with NumPy and Pandas libraries
- Use advanced Python visualization libraries like Plotly and Dash
- Build a dashboard
- Apply the rules of effective dashboard design to create professional data science solutions
- Go live with your project & deploy the dashboard on a live server
What's included with self-paced
- PCAP Certified Associate Python Programmer exam
- Self-paced video lessons
Self-paced lessons
Watch free previews and explore the lessons included with enrollment at Noble Desktop.
Python for Data Science Course Online (Self-Paced)
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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 AI Course Online (Self-Paced)
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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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Python Machine Learning Course Online (Self-Paced)
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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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Panda 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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Python Data Visualization & Interactive Dashboards Online (Self-Paced)
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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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