Is this Track suitable for beginners?

Yes, this track is suitable for beginners with no prior coding experience. All the courses included in this track provide a comprehensive introduction to their respective topics as well as foundational knowledge which can be used for more advanced data analysis topics.

What is the programming language of this Track?

This Track uses the Python programming language which is one of the most popular programming languages in the world.

Which jobs will benefit from this Track?

This track is great for any aspiring data professional or researcher who wants to gain domain-specific expertise, or for those who want to gain a solid foundation in data analysis for their current job.

How will this Track prepare me for my career?

By the end of this Track, you will be equipped with the necessary skills to work with real-world datasets, clean, manipulate and visualize data, and gain important statistical skills such as hypothesis testing.

How long does it take to complete this Track?

This track usually takes 36 hours to complete as it consists of several courses that significantly upskill users.

What's the difference between a skill track and a career track?

A skill track focuses on a specific area or set of skills such as a programming language, project management techniques, or design tools. A career track focuses on preparing you for employment, typically including job-specific skills, such as data analysis or software engineering.

What kind of datasets are used in the Track?

Throughout this Track, you'll work with datasets from a variety of different industries and disciplines. These datasets are typically created from real-world examples, and some are even provided through the course itself.

What will I learn in this Track?

You will learn how to import, clean, manipulate, and visualize data using some of the most popular Python libraries, including pandas, NumPy, Seaborn, and more. In addition, you will gain important statistics skills such as hypothesis testing and sampling, as well as joining data with pandas.

Does DataCamp offer Data Analyst Certification?

Yes. DataCamp's industry recognized Certifications include the Data Analyst Certifications (ranked #1 by Forbes), designed to equip you with the essential skills and practical knowledge required to excel in the role of a Data Analyst.

Data Analyst in Python | Learn Python for Data Analysis

Data Analyst in Python

Certification available

4.5+

97 reviews

Updated 03/2026

Develop your data analytics skills in Python. Gain the data analyst skills to manipulate, analyze, and visualize data. No coding experience required!

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Track Description

Data Analyst in Python

Become a Data Analyst with Python

Launch your data analytics career by mastering Python, the most popular programming language for data analysis. In this Track, you'll learn how to import, clean, manipulate, and visualize data using Python's powerful libraries. No prior coding experience is required; we'll guide you from the basics to advanced data analysis techniques.

Develop Essential Data Analysis Skills

Through hands-on exercises and real-world projects, you'll gain the fundamental skills every data analyst needs:

Clean and preprocess data using pandas and NumPy
Create compelling visualizations with Seaborn and Matplotlib
Perform exploratory data analysis to uncover insights
Apply statistical techniques like hypothesis testing and sampling
Combine data from multiple sources using joins and merges

Work with Real-World Datasets

Practice your skills on a variety of datasets reflecting the challenges data analysts face daily. You'll investigate Netflix movies, explore NYC public school test scores, analyze crime patterns in Los Angeles, and more. These projects will build your confidence in tackling real data problems and communicating your findings effectively.

A Comprehensive Curriculum for Aspiring Data Analysts

This Track provides a comprehensive learning path for aspiring data analysts. You'll start with the basics of Python programming and gradually progress to more advanced data manipulation and statistical techniques. The courses cover key libraries like pandas, NumPy, and Seaborn, ensuring you have a well-rounded data analysis toolkit.

Why Python for Data Analysis?

Python has become the go-to language for data analysis due to its simplicity, versatility, and powerful ecosystem. Its extensive libraries make it easy to perform complex data manipulations, create stunning visualizations, and apply statistical models. Python's popularity also means a wealth of community resources and strong demand for Python skills in the job market.

Launch Your Data Analytics Career

By completing this Track, you'll be ready to:

Apply for entry-level data analyst positions
Contribute to data-driven projects and decision-making
Continue learning advanced topics in data analysis and data science
Communicate insights effectively to both technical and non-technical audiences

Whether you're looking to start a new career in data analytics, enhance your current skill set, or improve your ability to make data-driven decisions, this Track will give you the foundation you need to succeed.

Prerequisites

There are no prerequisites for this track

Course
1
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.
Course
2
Intermediate Python
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Project
bonus
Investigating Netflix Movies
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.
Course
AI Native
4
Data Manipulation with pandas
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
Project
bonus
Exploring NYC Public School Test Result Scores
Use data manipulation and summary statistics to analyze test scores across New York City's public schools!
Course
6
Joining Data with pandas
Learn to combine data from multiple tables by joining data together using pandas.
Course
7
Introduction to Statistics in Python
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Course
8
Introduction to Data Visualization with Seaborn
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Project
bonus
Visualizing the History of Nobel Prize Winners
Explore a dataset containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Skill Assessment
bonus
Data Manipulation with Python
Course
12
Exploratory Data Analysis in Python
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Project
bonus
Analyzing Crime in Los Angeles
Find out when and where crime is most likely to occur, along with the types of crimes commonly committed in LA.
Course
14
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.
Course
15
Hypothesis Testing in Python
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Project
bonus
Hypothesis Testing with Men's and Women's Soccer Matches
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!