Data Science Jumpstart with 10 Projects using Jupyter Course

Course Summary

Unlock the Power of Data with Python.

What students are saying

I love you for your work on Python for Entrepreneurs (and everything else too)! :-) Your teaching is excellent and the course format is super helpful. I find myself progressing with python so much faster with your teaching. Please continue to make great python courses! They are a blessing.
-- Ann R.

Source code and course GitHub repository

github.com/talkpython/data-science-jumpstart-with-10-projects-course

What's this course about and how is it different?

This course will empower you with the skills and tools to dive deep into data science using Python. We assume you have a foundational understanding of Python but not data science concepts. This course exposes you to the same tools that data scientists, data engineers, analysts use data to tackle real-world challenges.

In this course, you will:

  • Delve into loading, cleaning, summarizing, and basic statistics with both CSV and Excel data.
  • Master the art of combining and reshaping datasets to uncover hidden patterns in the Retail Data Insights project.
  • Understand missing data handling, abnormal data recognition, and foundational machine learning techniques through Health Data Deep Dives.
  • Create models to explore Air Quality Trends & Movie Reviews.
  • Construct interactive dashboards using Plotly and explore SQL databases in the Interactive Dashboards & SQL Exploration section.
  • Harness powerful libraries such as Pandas, Matplotlib, Plotly, and more.

Transform from a data enthusiast to a Data Hero!

View the full course outline.

Who is this course for?

For anyone passionate about unveiling data insights using Python. Whether you are a beginner just starting with data science or an intermediate learner aiming to solidify your understanding and get exposure to more libraries, this course has something for everyone.

Follow along with subtitles and transcripts

Each course comes with subtitles and full transcripts. The transcripts are available as a separate searchable page for each lecture. They also are available in course-wide search results to help you find just the right lecture.

Each course has subtitles available in the video player.

Get hands-on for almost every chapter

The best way to learn new data tooling techniques or library features is to try them out. Every key concept in this course comes with a practical section where you can dive in and get your hands dirty. Not just understanding the theory, but actually applying it, helps you get a true feel for the tools and techniques, and you'll begin to see how you can integrate them into your own data projects.

These hands-on exercises typically range from 10 to 20 minutes, ensuring you get immediate practice on what you've learned without it becoming too time-consuming.

github.com/talkpython/data-science-jumpstart-with-10-projects-course


Who am I? Why should you take my course?

Who is Matt Harrison?

Who is Matt Harrison? I'm Matt. Thanks for dropping by. My friend Michael asked me to prepare a class to explain the cool new features in Python 3. There are a couple of reasons I'm especially qualified to teach you Python.

I've been using Python professionally since 2000 across a broad domain of areas. I ran the local Python group five years. Helping to mentor newbies and connect them with others in the industry.

I've been training and teaching for a long time. I've taught at large companies, small companies, universities, and many conferences. I've given courses for elementary students on programming drones with Python, and have taught retired professionals to program in Python.

I'm a best selling Python author. My most recent book Illustrated Guide to Python 3 has been a best seller on Amazon, as have my Learning the Pandas Library and my Treading on Python series.

Students like my training. I focus on the practical application. Below are some quotes from recent students.

"The hands-on activities were a great learning tool. Matt presented the material very well and did a great job of interacting with students and answering their questions and concerns. The material was perfect for learning new things with Python 3 and OOP!" - Jennifer S.
"Good overview of Python and showed a range of commands. Covered many aspects of the language and you were left with a sense of the capabilities." - Sam V.
"Matt's experience with Python and his ability to deal with those fundamental getting started problems. He demonstrated a way to get started, without getting bogged down by the overwhelming complexity and potential of Python." - Jake R.
"Matt obviously had an in-depth knowledge of Python and did a terrific job of explaining the material he presented. Questions were clearly answered and explanations of the code were easy to follow. Basing the course on the Markov Chain example was exceptionally well done. The example hits on many concepts that are important to an experienced programmer, with the code still being accessible to an entry-level programmer." - Anna O.

Free office hours keep you from getting stuck

One of the challenges of self-paced online learning is getting stuck. It can be hard to get the help you need to get unstuck.

That's why at Talk Python Training, we offer live, online office hours. You drop in and join a group of fellow students to chat about your course progress and see solutions via screen sharing.

Just visit your account page to see the upcoming office hour schedule.

The time to act is now

Take advantage of the Python data libraries today. You will use the same tools that professionals use and learn how to handle data with these 10 projects.

Buy for $49 + tax Bundle and save 85% Team Gift

Questions? Send us an email: contact@talkpython.fm

Course Outline: Chapters and Lectures

Welcome to the Course
8:14
Welcome
0:51
Installing Jupyter in a Virtual Environment
2:00
Running in Github Codespaces
1:37
How to use Jupyter
2:09
How to use VS Code
1:10
Remember the Exercises
0:27
Project 1: Working with Student Information CSV Files
31:58
Intro csv v2
0:34
Loading CSV data from a ZIP file with Pandas and Pyarrow
5:25
Summary stats in Pandas using describe, dtypes, and quantile
6:34
Pearson and Spearman Correlations in Pandas and Heatmaps
5:36
Understanding Pandas Categoricals with value_counts and Cross Tabulations
4:49
Visualizations in Pandas, with Histograms, Scatterplots, and Barplots
8:36
Summary
0:24
Project 2: Excel Integration with Adult Income Data
14:38
Intro excel
0:42
Create an Excel in Pandas with to_excel
1:45
Read Excel file in Pandas with read_excel and Pyarrow
1:31
Understanding Counts and Frequencies of Missing Data in Pandas with isna, any, sum, and mean
3:02
Quantifying Strings with filter and value_counts
2:07
Understanding Numbers with Correlations, Scatterplots, and Histograms
3:33
Writing and Formatting Excel Sheets in Pandas with to_excel and XlsxWriter add_format
1:48
Summary
0:10
Project 3: Merging AirBnB Temperature Data
11:56
Intro
0:14
Loading Data for Merging with Pyarrow
0:57
Merging Dataframes with the merge method and left_on, right_on parameters
1:33
Validating one to one and one to many merges
2:51
Debugging Merging by piping dataframe size
2:36
Cleanup columns after merging with loc
2:19
Export Merged data to Excel
0:56
Merging summary
0:30
Project 4: Understanding Grouping and Aggregation Retail Data
24:06
Intro grouping
0:38
Loading Retail Data from Excel into Pandas Dataframe
0:33
Using Feather and Pyarrow to Speed up loading Retail Data in Pandas
0:49
Exploratory Data Analysis (EDA) in Pandas with describe, histograms, and value_counts
3:48
Aggregating in Pandas to Calculate Sales by Year
2:43
Using Groupby in Pandas to visualize Sales by country
6:05
Using Grouper in Pandas to Groupby by Month Frequency
3:35
Grouping by Month and Country and Visualizing with a Line Plot
5:30
Summary
0:25
Project 5: Cleaning Heart Disease Data in Pandas
23:26
Intro cleaning
0:37
Loading Multiple Files into a Single Pandas Datafarme with Glob
0:46
Understanding the Heart Data to Cleanup
2:46
Fixing the Age Column Type to Int8
0:43
Converting the Numeric Sex Column into a String
1:17
Converting the Chest Pain Column into an Int8
0:49
Dealing with ? Characters in the Trestbps Numeric Column
2:25
Creating a Function to Repeat Common Cleanup in the Chol Column
3:07
Using the Cleanup Function for the Fbs Column
1:05
Fixing the Restecg Column
1:27
Fixing the Thalach Column
0:14
Fixing the Exang Column
0:14
Updating the Cleanup Function to Clean the Oldpeak Column
0:22
Cleaning the Slope Column
0:19
Cleaning the Ca Column
0:17
Converting Numeric Values to Catgoricals with the Thal Column
0:38
Fixing the Num Column
1:07
Comparing Memory usage in Pandas with memory_usage
0:49
Refactoring to a Function in Pandas for Cleanup
4:18
Cleaning summary
0:06
Project 6: Working with Time Series - Air Quality over Time
21:52
Intro time series air quality dataset
0:31
Load CSV file from a Zip file with Pandas
0:51
Checking for Missing Values and Shape in Pandas
0:52
Parsing Dates Using Format Strings and to_datetime
2:03
Rename columns in Pandas to Remove Invalid Characters
2:36
Make a Function to Clean up Pandas Data
0:52
Converting Dates to UTC in Pandas
0:57
Converting Dates to Italian time in Pandas and pytz
1:29
Making Line Plots for Time Series Data in Pandas
3:23
Interpolating and Filling in Missing values in Pandas
3:26
Resampling Time Series Data in Pandas with resample
2:30
Creating 7 Day Rolling Averages in Pandas with rolling
1:45
Updating the Function with Cleanup Functionality
0:16
Summary
0:21
Project 7: Working with Movie Review Text Data in Pandas
11:37
Intro text v2
0:25
Load movie review text data from a directory
1:31
Exploring the str attribute in Pandas for String manipulation
0:55
Using Spacy to Remove Stop words in Pandas
2:43
Using scikit-learn to calculate Tfidf for Pandas text
1:43
Using XGBoost to Create a Classification Model
2:40
Predicting Values with XGBoost and Pandas
1:40
Project 8: Predicting Heart Disease with Machine Learning
20:16
Intro v2
0:21
Combining Multiple Datasets with Pandas and concat
2:00
Exploring heart disease with aggregations and scatterplots
5:00
Preparing a Pandas Dataset to Create an XGBoost Model
4:58
Tuning an XGBoost Model with Hyperopt
6:01
Using a Confusion matrix to Understand the Model
1:47
Ml summary
0:09
Project 9: SQL / Database Integration
8:48
Intro SQL
0:13
Load CSV data into a Pandas dataframe and cleaning it
1:32
Using SqlAlchemy to Connect to a SQLite Database
0:55
Create a database table with Pandas using to_sql
0:31
Query a SQLite table from Pandas using read_sql
1:19
Query a SQLite table with Pandas
1:57
Visualize SQLite Data using Pandas
1:54
Summary SQL
0:27
Project 10: Making a Snow Report Dashboard with Dash and Plotly
13:26
Intro plotly
0:10
Load CSV data into Pandas dataframe
0:22
Clean Pandas data with a function for plotly
1:44
Creating a Line Plot in Plotly for Pandas
2:01
Creating a Bar plot in Plotly
2:28
Creating a Scatter plot in Plotly
3:40
Creating a Dashboard with Dash and Plotly Graphs
1:43
Creating a Plotly Dashboard using Dash with Widgets
1:10
Summary plotly
0:08
Course Conclusion
1:16
Conclusion
1:16
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