Gain the Python skills you need to start and grow your career as a data scientist. You’ll learn to create data visualizations, perform web-scraping, build machine learning algorithms, and much more. By the end, you’ll be able to analyze datasets, help make business decisions, and use machine learning to solve complex problems.
In this path, you’ll learn how to master mandatory data scientist technical skills, including object-oriented and functional programming with Python, libraries like scikit-learn, Matplotlib, NumPy, and pandas. You’ll also master web scraping and SQL queries, deep learning and machine learning, and predictive analysis.
To help you stand out from other candidates, we included concepts such as the UNIX command line, Git, and GitHub to develop efficient collaboration.
Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
- Python for Data Science: Fundamentals
- Python for Data Science: Intermediate
- Pandas & NumPy Fundamentals
- Exploratory Data Visualization
- Storytelling Through Data Visualization
- Data Cleaning and Analysis
- Data Cleaning in Python: Advanced
- Data Cleaning Project Walkthrough
- Elements of the Command Line
- Text Processing in the Command Line
- SQL Fundamentals
- Intermediate SQL for Data Analysis
- APIs and Web Scraping in Python
- Statistics: Fundamentals
- Statistics Intermediate: Averages & Variability
- Probability Fundamentals
- Conditional Probability
- Hypothesis Testing: Fundamentals
- Machine Learning Fundamentals
- Calculus for Machine Learning
- Linear Algebra for Machine Learning
- Linear Regression for Machine Learning
- Machine Learning in Python: Intermediate
- Decision Trees
- Deep Learning: Fundamentals
- Machine Learning Project
- Kaggle Fundamentals
- Functions: Advanced
- Command Line: Intermediate
- Git & Version Control
- Spark & Map-Reduce
