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Data Analyst in Python
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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
Prerequisites
There are no prerequisites for this trackCourse
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
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Project
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.
Course
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
Project
Use data manipulation and summary statistics to analyze test scores across New York City's public schools!
Course
Learn to combine data from multiple tables by joining data together using pandas.
Course
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Course
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Project
Explore a dataset containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Skill Assessment
Skill Assessment
Course
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Project
Find out when and where crime is most likely to occur, along with the types of crimes commonly committed in LA.
Course
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Course
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Project
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!
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