Python Data Analysis: NumPy & Pandas Masterclass from Udemy

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)

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What's inside

Learning objectives

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

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

Syllabus

Getting Started

Course Structure & Outline

READ ME: Important Notes for New Students

DOWNLOAD: Course Resources

Introducing the Course Project

Setting Expectations

Jupyter Installation & Launch

NumPy Primer

Pandas & NumPy Intro

Numpy Arrays & Array Properties

NumPy Arrays

ASSIGNMENT: Array Basics

SOLUTION: Array Basics

Array Creation

Random Number Generation

ASSIGNMENT: Array Creation

SOLUTION: Array Creation

Indexing & Slicing Arrays

The .loc Accessor

ASSIGNMENT: Indexing & Slicing Arrays

SOLUTION: Indexing & Slicing Arrays

Array Operations

ASSIGNMENT: Array Operations

SOLUTION: Array Operations

Filtering Arrays & Modifying Array Values

The Where Function

Where Function

ASSIGNMENT: Filtering & Modifying Arrays

SOLUTION: Filtering & Modifying Arrays

Array Aggregation

Array Functions

Sorting Arrays

Duplicate Index Values & Resetting The Index

ASSIGNMENT: Aggregation & Sorting

SOLUTION: Aggregation & Sorting

Vectorization

Broadcasting

ASSIGNMENT: Bringing it all together

SOLUTION: Bringing it all together

Key Takeaways

QUIZ: NumPy Primer

Pandas Series

Series Basics

Pandas Data Types & Type Conversion

ASSIGNMENT: Data Types & Type Conversion

SOLUTION: Data Types & Type Conversion

The Series Index & Custom Indices

The .iloc Accessor

ASSIGNMENT: Accessing Data & Resetting The Index

SOLUTION: Accessing Data & Resetting The Index

Filtering Series & Logical Tests

Sorting Series

Pandas Where (vs. NumPy Where)

ASSIGNMENT: Sorting & Filtering Series

SOLUTION: Sorting & Filtering Series

Numeric Series Operations

Text Series Operations

Info & Describe

ASSIGNMENT: Series Operations

SOLUTION: Series Operations

Numerical Series Aggregation

Categorical Series Aggregation

ASSIGNMENT: Series Aggregation

SOLUTION: Series Aggregation

Missing Data Representation in Pandas

Identifying Missing Data

Fixing Missing Data

ASSIGNMENT: Apply & Where

ASSIGNMENT: Missing Data

SOLUTION: Missing Data

Applying Custom Functions to Series

SOLUTION: Apply & Where

QUIZ: Pandas Series

Intro to DataFrames

DataFrame Basics

Creating a DataFrame

Reading In a Dataset

Exploring DataFrames: Heads, Tails & Sample

ASSIGNMENT: DataFrame Basics

SOLUTION: DataFrame Basics

Exploring DataFrames: Info & Describe

ASSIGNMENT: Exploring a DataFrame

SOLUTION: Exploring a DataFrame

Accessing DataFrame Columns

Accessing DataFrame Data with .iloc & .loc

ASSIGNMENT: Accessing DataFrame Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops real-world data analysis skills with NumPy and Pandas, which are fundamental Python libraries for data science

Taught by Maven Analytics, who are recognized experts in training data analysts and business intelligence professionals

Emphasizes hands-on practice through project-based learning, which helps learners apply their skills to real-world scenarios

May require prior Python experience, as it assumes some familiarity with the language

Intermediate-level course for those with some data analysis experience looking to enhance their NumPy and Pandas skills

Course may be less beneficial for those seeking a comprehensive introduction to data analysis as it focuses on advanced applications

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python Data Analysis: NumPy & Pandas Masterclass with these activities:

Learn the fundamentals of NumPy arrays

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Refreshes understanding of NumPy arrays and their properties to prepare for more advanced operations in the course.

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Create and manipulate NumPy arrays

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Provides practice in creating, manipulating, and operating on NumPy arrays, reinforcing essential concepts early on.

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Create a NumPy array from a list of numbers and explore its properties.
Manipulate array elements using indexing and slicing.
Perform element-wise operations on arrays using NumPy methods.

Visualize data using Pandas

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Encourages practical application of Pandas for data visualization, reinforcing techniques and fostering a deeper understanding of data structures.

Browse courses on Pandas

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Create a Pandas DataFrame from a dictionary or list of lists.
Visualize data using the Pandas plot function to create histograms, line charts, and scatterplots.

Four other activities

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Perform data aggregation and manipulation

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Provides hands-on practice in data aggregation and manipulation, reinforcing techniques and enhancing proficiency in data analysis.

Browse courses on Pandas

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Aggregate data using Pandas groupby and aggregation functions.
Manipulate dataframes by merging, joining, and concatenating.

Analyze real-world data with Pandas

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Challenges students to apply their Pandas skills to a real-world data analysis project, fostering critical thinking and problem-solving abilities.

Browse courses on Pandas

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Load and explore a real-world dataset using Pandas.
Perform data cleaning and preprocessing to prepare it for analysis.
Analyze the data using Pandas and interpret the results.
Visualize the data insights using appropriate charts and graphs.

Share knowledge and support others

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Encourages students to reinforce their learning and deepen their understanding by assisting others in the course community.

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Answer questions in course forums and discussion threads.
Provide feedback and support to fellow students on their projects or assignments.

Create a study guide

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Promotes active learning and self-directed review by encouraging students to compile key concepts into a cohesive study guide.

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Summarize key points and concepts from each lesson and module.
Include diagrams, examples, and practice questions to reinforce understanding.

Career center

Learners who complete Python Data Analysis: NumPy & Pandas Masterclass will develop knowledge and skills that may be useful to these careers:

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Python Data Analysis

NumPy & Pandas Masterclass

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