Python Data Science & Machine Learning Bootcamp (NYC or Online)
- Difficulty
- Beginner
- Format
- Live or Self-paced
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Book live on Noble DesktopSummary
This program is built for anyone who wants to become a working Python programmer, with a curriculum that spans data science, machine learning, data visualization, and automation. You'll start by getting comfortable with Python fundamentals before moving into three of its most essential data science libraries, like NumPy, Pandas, and Matplotlib, using them to analyze data and uncover insights. From there, you'll learn how to build predictive models using machine learning packages like scikit-learn, and put Python to work automating everyday tasks like aggregating, updating, and formatting data.
The final stretch of the program shifts toward visualization, where you'll use Matplotlib, Seaborn, Plotly, and Dash to create compelling charts, graphs, and interactive dashboards. You'll deploy your work to GitHub, giving potential employers an easy way to see what you've built. By the time you finish, you'll have hands-on experience across the full Python data science stack and the skills needed to go after entry-level roles in data science and Python engineering.
Who this course is for
The Python Data Science & Machine Learning Bootcamp is best suited for: Anyone who wants to learn Python, machine learning, and data visualization skills Analysts who work with other data tools looking to transition to Python Developers looking to broaden their skill set by learning data science and Python
Topics included
Curriculum
What you'll learn
- Clean and analyze tabular data with NumPy and Pandas
- Create graphs and visualizations with Matplotlib and Plotly
- Build interactive dashboards with Dash
- Make predictions with linear regression
- Apply machine learning algorithms and evaluate their performance
- Learn how to write programs in Python to automate everyday tasks
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 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 for Automation 6 hours
Learn how to use Python to extract data from websites and write loops for processing a large number of pages. This course covers topics such as HTML and CSS, Python fundamentals, web scraping exercises, storage and scheduling, and real-life examples of scraping valuable data.
- Understand how websites are structured with HTML and CSS to identify elements for data extraction
- Learn Python fundamentals, such as variables, data types, conditionals, loops, and list manipulation
- Use the Requests and Beautiful Soup libraries to perform web scraping and target specific content
- Write loops to automate web scraping across multiple pages and streamline repetitive tasks
- Store scraped data in different formats, such as text files and CSVs, for analysis and reporting
- Schedule Python scripts to run on a regular basis, enabling continuous data collection and automating workflows
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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
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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 Advanced 30 hours
Take your machine learning expertise to the next level in this comprehensive, hands-on course designed to transform foundational ML knowledge into practical, real-world applications. Move beyond standard Jupyter notebooks and explore how professional ML engineers build and deploy machine learning systems across diverse domains.
- Build a complete NLP pipeline, including cleaning with RegEx, removing stopwords, lemmatizing, and vectorizing text.
- Train and evaluate a Naive Bayes machine learning model to classify movie reviews as positive or negative.
- Compare and apply pre-trained sentiment scoring systems such as TextBlob and Vader.
- Develop a recommendation engine that suggests similar products using NLP techniques.
- Learn Flask fundamentals by creating search apps, integrating APIs, and serving ML models in the browser.
- Complete a capstone project by building a Flask-powered Movie Recommender App that brings together NLP, machine learning, and web development.
What's included
- Free course retake within one year to refresh the material and gain practice.
- 8 one-on-one mentoring sessions
- Class recordings
Stated by the provider. Confirm what your tuition covers before enrolling.
Live classes
Choose dates and book your instructor-led class on nobledesktop.com.
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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
Mon, Tue, Wed, Thu, Fri
10:00–17:00 · America/New_York (EST)
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In-person or live online
Sun
10:00–17:00 · America/New_York (EST/EDT)
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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 (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
Sun
10:00–17:00 · America/New_York (EDT)
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In-person or live online
Mon, Tue, Thu, Fri, Wed
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
Tue, Thu
18:00–21:00 · 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
Sun
10:00–17:00 · America/New_York (EDT/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, Wed, Thu, Fri
10:00–17:00 · America/New_York (EDT/EST)
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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.
- Tuition
- $3,495
- Course length
- 96 hours
- Schedule
- On your schedule
Learn to apply Python to data science, machine learning, visualization, and automation as you build the programming skills needed to work with databases and analyze real-world datasets. You’ll move from Python fundamentals into NumPy, Pandas, and Matplotlib before creating predictive models with scikit-learn and using Python to automate everyday workflows.
In the final stage, you’ll design visualizations and interactive dashboards with Matplotlib, Seaborn, Plotly, and Dash, deploying your projects to GitHub to showcase your abilities. By the end of the program, you’ll be prepared for entry-level roles in data science and Python engineering.
Who the self-paced course is for
The Python Data Science & Machine Learning Bootcamp is best suited for: Anyone who wants to learn Python, machine learning, and data visualization skills Analysts who work with other data tools and want to transition to using Python Developers looking to broaden their skill set by learning data science and Python
Self-paced curriculum
What you'll learn self-paced
- Clean and analyze tabular data with NumPy and Pandas
- Create graphs and visualizations with Matplotlib and Plotly
- Build interactive dashboards with Dash
- Make predictions with linear regression
- Apply machine learning algorithms and evaluate their performance
- Learn how to write programs in Python to automate everyday tasks
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
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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 for Automation Course Online (Self-Paced) 6 hours
Learn how to use Python to extract data from websites and write loops for processing a large number of pages. This course covers topics such as HTML and CSS, Python fundamentals, web scraping exercises, storage and scheduling, and real-life examples of scraping valuable data.
- The syntax of Python and how to construct programs
- How to run your programs on a regular schedule
- Identify and correct common errors
- How to write scripts that automate manual tasks
- How to update Excel files automatically using Python
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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
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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
-
Python Machine Learning Advanced (Self-Paced) 30 hours
Take your machine learning expertise to the next level in this comprehensive, hands-on course designed to transform foundational ML knowledge into practical, real-world applications. Move beyond standard Jupyter notebooks and explore how professional ML engineers build and deploy machine learning systems across diverse domains.
- Build a complete NLP pipeline, including cleaning with RegEx, removing stopwords, lemmatizing, and vectorizing text.
- Train and evaluate a Naive Bayes machine learning model to classify movie reviews as positive or negative.
- Compare and apply pre-trained sentiment scoring systems such as TextBlob and Vader.
- Develop a recommendation engine that suggests similar products using NLP techniques.
- Learn Flask fundamentals by creating search apps, integrating APIs, and serving ML models in the browser.
- Complete a capstone project by building a Flask-powered Movie Recommender App that brings together NLP, machine learning, and web development.
What's included with self-paced
- 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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Enrollment required
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 Automation Course Online (Self-Paced)
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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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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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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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