Course Overview

This Part-Time 9-week course will introduce you to Python programming and the data science principles required to tackle real-world, data-rich problems.


Week 1: Intro to Data Science concepts and Python 101

Week 2: This week you’ll dive into Python. You’ll learn programming basics, how Jupyter Notebooks work, and advanced Python functions

Week 3: Dive deeper into Python. Learn Data manipulation, visualization, and math basics for that are necessary to be successful in data science

Week 4: Students learn Stats 101 for Data Science and then learn advanced Linear regressions, and how to validate results.

Week 5: This week we learn How to approach a data set that we’ve never seen before - Exploratory Data Analysis and then are introduced to the basic statistical models used for predictions.

Week 6: This week students dive deeper into advanced supervised and unsupervised learning.

Week 7: This week we finish up supervised/unsupervised learning models and begin learning features and are introduced to Features.

Week 8: Further Explore more sophisticated model approaches to our data and how to create complete data pipelines.

Week 9: Advanced model evaluating and Presentations.

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Course Takeaways

By the end of the course, you will be able to:

‣An understanding of problems solvable with data science and an ability to attack them from a statistical perspective.

‣The ability to write Python code to solve mathematical problems using linear algebra, calculus, probability, and statistics

‣Familiarity with the Python data science ecosystem and the various tools needed to continue developing as a data scientist.

‣ An understanding of when to use supervised and unsupervised statistical learning methods on labeled and unlabeled data-rich problems.

‣ The ability to create data analytical pipelines and applications in Python.