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Data Analyst in Python

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Zaktualizowano 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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Opis utworu

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.

Wymagania wstępne

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  • 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

    Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

  • Project

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    Investigating Netflix Movies

    Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.

  • 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

    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.

Data Analyst in Python
9 courses
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Kontynuując, akceptujesz nasze Warunki korzystania, naszą Politykę prywatności oraz fakt, że Twoje dane są przechowywane w USA.