Introduction to
Data Science
(with Python)
Data Science
(with Python)
What's Included?
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Live weekly zoom meeting, with a focus on interactive learning
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Two options for enrollment, depending on the student's prior coding experience level
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Weekly live office hour with Dr. Luis Finotti
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Weekly problem sets and with written feedback
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The choice between a graded track and a certificate of completion track
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Optional exploratory videos and text references
Small Group Class Discussions
Through our live, interactive small group classes, we'll introduce students to concepts from data analysis, such as data exploration, predictions using previous data, and inference. With a class size capped at 12 students, there will be ample opportunity for students to contribute and receive any needed support.
Please note the content is based on a text out of the University of California Berkeley, and so is college level content. However, this is not course that is focused on mathematical depth. Although we will discuss many theoretical topics, such as interpreting data, best practices, bias, etc., at its core, this is a course focused primarily on coding, and we will use Python to perform all the necessary tasks.
Our intention is always to create a growth focused, supportive, mistakes-embraced environment with the appropriate level of challenge to keep our students growing. Student participation is an essential component of the course, and students' questions and comments will lead many of our discussions.
There are two options for registration:
1. Students with little to no coding experience in Python can register for the longer version of the course that includes three weeks of basic Python instruction held before the main Data Science course begins. This option is $500.
2. Students who don't need support with coding in Python can register for the shorter version of the course. This option is $400.
Very little mathematical background is necessary, with only Algebra I required as a prerequisite. Although we will briefly use some exponential and log functions, and briefly discuss some probability and statistics (among other concepts), these concepts will be introduced and discussed without assuming prior knowledge. If you have any concerns about whether or not your student is ready for this course, please contact us through our Contact Page form.
Topics Covered
* Introduction to Jupyter Notebooks
* Basics of Python
* Numerical Computations with NumPy
* Data Frames (using pandas)
* Visualization (using pandas and MatPlotLib)
* Randomness and Probability
* Testing Hypotheses
* A/B Testing
* Inference
* Linear Correlation and Predictions
* Classification with Nearest Neighbors
The primary reference text is the free textbook developed for UC Berkeley called Computational and Inferential Thinking: The Foundations of Data Science.
The primary reference text is the free textbook developed for UC Berkeley called Computational and Inferential Thinking: The Foundations of Data Science.
Meet the instructor
Dr. Luis Finotti
Patrick Jones - Course author
Ready to Enroll?
Please send us your enrollment request via this form
Payment is sent upon enrollment confirmation via your choice of Zelle, Venmo, or Paypal.
Thank you!
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