Python Data Analysis: NumPy & Pandas Masterclass
Python Data Analysis: NumPy & Pandas Masterclass, available at $94.99, has an average rating of 4.56, with 216 lectures, 52 quizzes, based on 1813 reviews, and has 15964 subscribers.
You will learn about 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 This course is ideal for individuals who are Analysts or BI professionals looking to learn data analysis with NumPy and Pandas or Aspiring data scientists who want to build or strengthen their Python skills or Anyone interested in learning one of the most popular open source programming languages in the world or Students looking to learn powerful, practical skills with unique, hands-on projects and course demos It is particularly useful for Analysts or BI professionals looking to learn data analysis with NumPy and Pandas or Aspiring data scientists who want to build or strengthen their Python skills or Anyone interested in learning one of the most popular open source programming languages in the world or Students looking to learn powerful, practical skills with unique, hands-on projects and course demos.
Enroll now: Python Data Analysis: NumPy & Pandas Masterclass
Summary
Title: Python Data Analysis: NumPy & Pandas Masterclass
Price: $94.99
Average Rating: 4.56
Number of Lectures: 216
Number of Quizzes: 52
Number of Published Lectures: 216
Number of Published Quizzes: 52
Number of Curriculum Items: 268
Number of Published Curriculum Objects: 268
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Analysts or BI professionals looking to learn data analysis with NumPy and Pandas
- Aspiring data scientists who want to build or strengthen their Python skills
- Anyone interested in learning one of the most popular open source programming languages in the world
- Students looking to learn powerful, practical skills with unique, hands-on projects and course demos
Target Audiences
- Analysts or BI professionals looking to learn data analysis with NumPy and Pandas
- Aspiring data scientists who want to build or strengthen their Python skills
- Anyone interested in learning one of the most popular open source programming languages in the world
- Students looking to learn powerful, practical skills with unique, hands-on projects and course demos
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:
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Intro to NumPy & Pandas
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Introduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysis
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Pandas Series
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Introduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysis
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Intro to DataFrames
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Work with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficiently
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Manipulating Python DataFrames
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Aggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting data
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Basic Python Data Visualization
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Learn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histograms
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MID-COURSE PROJECT
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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
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Analyzing Dates & Times
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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
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Importing & Exporting Data
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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
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Joining Python DataFrames
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Combine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rows
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FINAL COURSE PROJECT
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Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results
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Join today and get immediate, lifetime access to the following:
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13+ hours of high-quality video
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Python NumPy & Pandas PDF ebook (350+ pages)
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Downloadable project files & solutions
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Expert support and Q&A forum
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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:
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Course Curriculum
Chapter 1: Getting Started
Lecture 1: Course Structure & Outline
Lecture 2: READ ME: Important Notes for New Students
Lecture 3: DOWNLOAD: Course Resources
Lecture 4: Introducing the Course Project
Lecture 5: Setting Expectations
Lecture 6: Jupyter Installation & Launch
Chapter 2: NumPy Primer
Lecture 1: Pandas & NumPy Intro
Lecture 2: Numpy Arrays & Array Properties
Lecture 3: ASSIGNMENT: Array Basics
Lecture 4: SOLUTION: Array Basics
Lecture 5: Array Creation
Lecture 6: Random Number Generation
Lecture 7: ASSIGNMENT: Array Creation
Lecture 8: SOLUTION: Array Creation
Lecture 9: Indexing & Slicing Arrays
Lecture 10: ASSIGNMENT: Indexing & Slicing Arrays
Lecture 11: SOLUTION: Indexing & Slicing Arrays
Lecture 12: Array Operations
Lecture 13: ASSIGNMENT: Array Operations
Lecture 14: SOLUTION: Array Operations
Lecture 15: Filtering Arrays & Modifying Array Values
Lecture 16: The Where Function
Lecture 17: ASSIGNMENT: Filtering & Modifying Arrays
Lecture 18: SOLUTION: Filtering & Modifying Arrays
Lecture 19: Array Aggregation
Lecture 20: Array Functions
Lecture 21: Sorting Arrays
Lecture 22: ASSIGNMENT: Aggregation & Sorting
Lecture 23: SOLUTION: Aggregation & Sorting
Lecture 24: Vectorization
Lecture 25: Broadcasting
Lecture 26: ASSIGNMENT: Bringing it all together
Lecture 27: SOLUTION: Bringing it all together
Lecture 28: Key Takeaways
Chapter 3: Pandas Series
Lecture 1: Series Basics
Lecture 2: Pandas Data Types & Type Conversion
Lecture 3: ASSIGNMENT: Data Types & Type Conversion
Lecture 4: SOLUTION: Data Types & Type Conversion
Lecture 5: The Series Index & Custom Indices
Lecture 6: The .iloc Accessor
Lecture 7: The .loc Accessor
Lecture 8: Duplicate Index Values & Resetting The Index
Lecture 9: ASSIGNMENT: Accessing Data & Resetting The Index
Lecture 10: SOLUTION: Accessing Data & Resetting The Index
Lecture 11: Filtering Series & Logical Tests
Lecture 12: Sorting Series
Lecture 13: ASSIGNMENT: Sorting & Filtering Series
Lecture 14: SOLUTION: Sorting & Filtering Series
Lecture 15: Numeric Series Operations
Lecture 16: Text Series Operations
Lecture 17: ASSIGNMENT: Series Operations
Lecture 18: SOLUTION: Series Operations
Lecture 19: Numerical Series Aggregation
Lecture 20: Categorical Series Aggregation
Lecture 21: ASSIGNMENT: Series Aggregation
Lecture 22: SOLUTION: Series Aggregation
Lecture 23: Missing Data Representation in Pandas
Lecture 24: Identifying Missing Data
Lecture 25: Fixing Missing Data
Lecture 26: ASSIGNMENT: Missing Data
Lecture 27: SOLUTION: Missing Data
Lecture 28: Applying Custom Functions to Series
Lecture 29: Pandas Where (vs. NumPy Where)
Lecture 30: ASSIGNMENT: Apply & Where
Lecture 31: SOLUTION: Apply & Where
Lecture 32: Key Takeaways
Chapter 4: Intro to DataFrames
Lecture 1: DataFrame Basics
Lecture 2: Creating a DataFrame
Lecture 3: Exploring DataFrames: Heads, Tails & Sample
Lecture 4: ASSIGNMENT: DataFrame Basics
Lecture 5: SOLUTION: DataFrame Basics
Lecture 6: Exploring DataFrames: Info & Describe
Lecture 7: ASSIGNMENT: Exploring a DataFrame
Lecture 8: SOLUTION: Exploring a DataFrame
Lecture 9: Accessing DataFrame Columns
Lecture 10: Accessing DataFrame Data with .iloc & .loc
Lecture 11: ASSIGNMENT: Accessing DataFrame Data
Instructors
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Maven Analytics
Empowering everyday people with life-changing data skills -
Chris Bruehl
Lead Python Instructor at Maven Analytics
Rating Distribution
- 1 stars: 14 votes
- 2 stars: 15 votes
- 3 stars: 118 votes
- 4 stars: 577 votes
- 5 stars: 1089 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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