Udemy

Python Data Analysis: NumPy & Pandas Masterclass

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  • 19,619 Students
  • Updated 11/2025
4.6
(2,485 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
13 Hour(s) 31 Minute(s)
Language
English
Taught by
Maven Analytics • 1,500,000 Learners, Chris Bruehl
Rating
4.6
(2,485 Ratings)
1 views

Course Overview

Python Data Analysis: NumPy & Pandas Masterclass

Learn NumPy + Pandas for data analysis, data science & business intelligence, w/ a top Python data science instructor!

This is a hands-on, project-based course designed to help you master two of the most popular Python packages for data analysis and business intelligence: NumPy and Pandas.


We'll start with a NumPy primer to introduce arrays and array properties, practice common operations like indexing, slicing, filtering and sorting, and explore important concepts like vectorization and broadcasting.


From there we'll dive into Pandas, and focus on the essential tools and methods to explore, analyze, aggregate and transform series and dataframes. You'll practice plotting dataframes with charts and graphs, manipulating time-series data, importing and exporting various file types, and combining dataframes using common join methods.


Throughout the course you'll play the role of Data Analyst for Maven Mega Mart, a large, multinational corporation that operates a chain of retail and grocery stores. Using the Python skills you learn throughout the course, you'll work with members of the Maven Mega Mart team to analyze products, pricing, transactions, and more.


COURSE OUTLINE:


  • Intro to NumPy & Pandas

    • Introduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysis


  • Pandas Series

    • Introduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysis


  • Intro to DataFrames

    • Work with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficiently


  • Manipulating Python DataFrames

    • Aggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting data


  • Basic Python Data Visualization

    • Learn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histograms


  • MID-COURSE PROJECT

    • Put your skills to the test with a brand new dataset, and use your Python skills to analyze and evaluate a new retailer as a potential acquisition target for Maven MegaMart


  • Analyzing Dates & Times

    • Learn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averages


  • Importing & Exporting Data

    • Read in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its source


  • Joining Python DataFrames

    • Combine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rows


  • FINAL COURSE PROJECT

    • Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results


Join today and get immediate, lifetime access to the following:


  • 13+ hours of high-quality video

  • Python NumPy & Pandas PDF ebook (350+ pages)

  • Downloadable project files & solutions

  • Expert support and Q&A forum

  • 30-day Udemy satisfaction guarantee


If you're a data analyst, data scientist, business intelligence professional or data engineer looking to add Pandas to your Python skill set, this course is for you.


Happy learning!

-Chris Bruehl (Python Expert & Lead Python Instructor, Maven Analytics)


__________

Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!


See why our courses are among the TOP-RATED on Udemy:


"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C.


"This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M.


"Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.

Course Content

  • 10 section(s)
  • 216 lecture(s)
  • Section 1 Getting Started
  • Section 2 NumPy Primer
  • Section 3 Pandas Series
  • Section 4 Intro to DataFrames
  • Section 5 Aggregating & Reshaping DataFrames
  • Section 6 Basic Data Visualization in Python
  • Section 7 MID-COURSE PROJECT
  • Section 8 Analyzing Dates & Times
  • Section 9 Importing & Exporting Data
  • Section 10 Joining DataFrames

What You’ll Learn

  • Master the essentials of NumPy and Pandas, two of Python's most powerful data analysis packages
  • Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
  • Analyze and manipulate dates and times for time intelligence and time-series analysis
  • Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms
  • Import and export flat files, Excel workbooks and SQL database tables using Pandas
  • Build powerful, practical skills for modern analytics and business intelligence


Reviews

  • P
    Pentapalli Appalaraju
    5.0

    Good class and provided the live example which is most helpful for begineers

  • M
    Matthew Johnson
    4.5

    There is a lot of good information being presented. I'm glad I have the pdf of the slides to look through. It would be nice if the PDF had bookmarks.

  • M
    Marcus Chua
    4.5

    Lessons and topics are pretty well organized and clearly taught. Only improvement so far is to update some of the methods/functions used in the assignment such as the date time ones. I could not use the ones provided instead I used pd.to_datetime() to get the dates for example. Other than that, its been very good!

  • H
    Hunter Sills
    1.0

    Too much irrelevant information, poorly worded exercises, inconsistencies between concepts learned and concepts in exercises, not nearly enough practice, insufficient time spent on any concept. I'm spending literally 10x the video time teaching myself the concepts. Not what I was expecting from Maven.

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