Start your journey to becoming a data analyst using Python - one of the most popular programming languages in the world. No prior coding experience is required; you’ll start from scratch and learn how to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher.Â
You’ll begin your data analyst training with interactive exercises and get hands-on with some of the most popular Python libraries, including pandas, NumPy, Seaborn, and many more. You’ll learn why Python for data analysis is so popular and work with real-world datasets to grow your data manipulation and exploratory data analysis skills.Â
As you progress through the courses, you’ll cover topics such as data manipulation and joining data. You’ll also learn key statistics skills, like hypothesis testing.Â
Get started today, grow your Python skills, and begin your journey to becoming a confident data analyst.
Overview
Syllabus
- Introduction to Python
- Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
- Intermediate Python
- Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
- Investigating Netflix Movies
- Data Manipulation with pandas
- Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
- Exploring NYC Public School Test Result Scores
- Joining Data with pandas
- Learn to combine data from multiple tables by joining data together using pandas.
- Introduction to Statistics in Python
- Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
- Introduction to Data Visualization with Seaborn
- Learn how to create informative and attractive visualizations in Python using the Seaborn library.
- Visualizing the History of Nobel Prize Winners
- Exploratory Data Analysis in Python
- Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
- Analyzing Crime in Los Angeles
- Sampling in Python
- Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
- Hypothesis Testing in Python
- Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
- Hypothesis Testing with Men's and Women's Soccer Matches
Taught by
Hugo Bowne-Anderson, DataCamp Content Creator, Richie Cotton, Maggie Matsui, Aaren Stubberfield, James Chapman, George Boorman, and Izzy Weber
