Self-Paced Course
Data Analysis with Python & Pandas
Master the basics of NumPy and Pandas for data analysis, and learn how to explore, transform, aggregate, join and visualize dataframes.
Course Description
This is a hands-on, project-based course designed to help you learn two of the most popular Python packages for data analysis: 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.
If you're a data scientist, BI analyst or data engineer looking to add Pandas to your Python skill set, this is the course for you.
COURSE CONTENTS:
13.5 hours on-demand video (22.0 CPE credits)
48 homework assignments (plus 2 course projects)
8 quizzes
2 skills assessments (1 benchmark, 1 final)
COURSE CURRICULUM:
- Welcome to the Course!
- Benchmark Assessment
- Course Structure & Outline
- DOWNLOAD: Course Resources
- Introducing the Course Project
- Setting Expectations
- Pandas & NumPy Intro
- Numpy Arrays & Array Properties
- ASSIGNMENT: Array Basics
- SOLUTION: Array Basics
- Array Creation
- Random Number Generation
- ASSIGNMENT: Array Creation
- SOLUTION: Array Creation
- Indexing & Slicing Arrays
- ASSIGNMENT: Indexing & Slicing Arrays
- SOLUTION: Indexing & Slicing Arrays
- Array Operations
- ASSIGNMENT: Array Operations
- SOLUTION: Array Operations
- Filtering & Modifying Array Values
- The Where Function
- ASSIGNMENT: Filtering & Modifying Arrays
- SOLUTION: Filtering & Modifying Arrays
- Array Aggregation
- Array Functions
- Sorting Arrays
- ASSIGNMENT: Array Functions & Methods
- SOLUTION: Array Functions & Methods
- Vectorization
- Broadcasting
- ASSIGNMENT: Bringing it all together
- SOLUTION: Bringing it all together
- Key Takeaways
- QUIZ: NumPy Primer
- 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
- The .loc Accessor
- Duplicate Index Values & Resetting The Index
- ASSIGNMENT: Accessing Data & Resetting The Index
- SOLUTION: Accessing Data & Resetting The Index
- Filtering Series & Logical Tests
- Sorting Series
- ASSIGNMENT: Sorting & Filtering Series
- SOLUTION: Sorting & Filtering Series
- Numeric Series Operations
- Text Series Operations
- 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: Missing Data
- SOLUTION: Missing Data
- Applying Custom Functions to Series
- Pandas Where (vs. NumPy Where)
- ASSIGNMENT: Apply & Where
- SOLUTION: Apply & Where
- Key Takeaways
- QUIZ: Pandas Series
- DataFrame Basics
- Creating a DataFrame
- ASSIGNMENT: DataFrame Basics
- SOLUTION: DataFrame Basics
- Exploring a DataFrame: Heads, Tails & Sample
- Exploring a DataFrame: Info & Describe
- ASSIGNMENT: Exploring a DataFrame
- SOLUTION: Exploring a DataFrame
- Accessing DataFrame Columns
- Accessing DataFrame Data with .iloc & .loc
- ASSIGNMENT: Accessing DataFrame Data
- SOLUTION: Accessing DataFrame Data
- Dropping Columns & Rows
- Identifying & Dropping Duplicates
- ASSIGNMENT: Dropping Data
- SOLUTION: Dropping Data
- Filtering DataFrames
- PRO TIP: The Query Method
- ASSIGNMENT: Filtering DataFrames
- SOLUTION: Filtering DataFrames
- Sorting DataFrames by Axes
- Sorting DataFrames by Values
- ASSIGNMENT: Sorting DataFrames
- SOLUTION: Sorting DataFrames
- Renaming & Reordering Columns
- ASSIGNMENT: Renaming & Reordering Columns
- SOLUTION: Renaming & Reordering Columns
- Arithmetic & Boolean Column Creation
- ASSIGNMENT: Arithmetic & Boolean Columns
- SOLUTION: Arithmetic & Boolean Columns
- PRO TIP: Advanced Conditional Columns with Select
- ASSIGNMENT: The Select Function
- SOLUTION: The Select Function
- The Map Method
- PRO TIP: Multiple Column Creation with Assign
- ASSIGNMENT: Map & Assign
- SOLUTION: Map & Assign
- DataType Conversion
- The Replace Method
- PRO TIP: Memory Usage & DataTypes
- PRO TIP: Downcasting Numeric Data Types
- PRO TIP: The Categorical Data Type
- ASSIGNMENT: DataFrame DataTypes
- SOLUTION: DataFrame DataTypes
- Key Takeways
- QUIZ: DataFrames
- Basic Aggregations
- The groupby Method
- Grouping By Multiple Columns
- ASSIGNMENT: Basic DataFrame Aggregation
- SOLUTION: Basic DataFrame Aggregation
- Multi-Index DataFrames
- Modifying Multi-Indices
- ASSIGNMENT: Multi-Index DataFrames
- SOLUTION: Multi-Index DataFrames
- The Agg Method & Named Aggregations
- ASSIGNMENT: The Agg Method
- SOLUTION: The Agg Method
- PRO TIP: Aggregations While Keeping Rows
- ASSIGNMENT: The Transform Method
- SOLUTION: The Transform Method
- Pivot Tables in Pandas
- Multiple Aggregation Pivot Tables
- Filtering Pivot Tables
- PRO TIP: Pivot Table Heatmaps
- ASSIGNMENT: Pivot Tables
- SOLUTION: Pivot Tables
- Melting DataFrames
- ASSIGNMENT: Melting DataFrames
- SOLUTION: Melting DataFrames
- Key Takeaways
- QUIZ: Aggregating & Reshaping DataFrames
- The matplotlib API & The .plot() Method
- Line Charts
- ASSIGNMENT: Basic Line Chart
- SOLUTION: Basic Line Chart
- Chart Titles
- Chart Colors
- Color Palettes
- Line Style
- Chart Legends & Gridlines
- Subplots & Figure Size
- Setting a Style & Saving a Plot
- ASSIGNMENT: Stylized Line Chart
- SOLUTION: Stylized Line Chart
- Bar Plots
- Grouped Bar Plots
- Stacked Bar Plots
- ASSIGNMENT: Bar Plots
- SOLUTION: Bar Plots
- Scatter Plots
- ASSIGNMENT: Scatter Plots
- SOLUTION: Scatter Plots
- Histograms
- ASSIGNMENT: Histograms
- SOLUTION: Histograms
- Additional Plots & Further Exploration
- Key Takeaways
- QUIZ: Basic Data Visualization
- Mid-Course Project Intro
- SOLUTION: Mid-Course Project
- Times in Base Python
- Time Deltas
- ASSIGNMENT: Base Python DateTimes
- SOLUTION: Base Python DateTimes
- NumPy/Pandas Datetime Data Types
- Date & Time Parts
- ASSIGNMENT: Pandas Datetime Basics
- SOLUTION: Pandas Datetime Basics
- Converting To Datetimes
- ASSIGNMENT: Datetime Conversion
- SOLUTION: Datetime Conversion
- Time Deltas & Arithmetic
- ASSIGNMENT: Time Deltas
- SOLUTION: Time Deltas
- Time Series Indices
- Missing Time Series Data
- Time Series Aggregation
- ASSIGNMENT: Time Series Manipulation
- SOLUTION: Time Series Manipulation
- Shifting Time Series
- PRO TIP: Diff
- Rolling Aggregations
- ASSIGNMENT: Advanced Time Series Manipulation
- SOLUTION: Advanced Time Series Manipulation
- Key Takeaways
- QUIZ: Analyzing Dates & Times
- read_csv Revisted
- Changing Column Names
- Setting an Index Column
- Column Selection
- Row Selection
- Missing Values
- Parsing Dates
- DataTypes
- PRO TIP: Converters
- ASSIGNMENT: Preprocessing Data With read_csv
- SOLUTION: Preprocessing Data with read_csv
- Importing from .txt
- Importing from Excel
- Exporting to Flat Files
- ASSIGNMENT: Importing & Exporting Excel Data
- SOLUTION: Importing & Exporting Excel Data
- Working With SQL SQL Databases
- Other Supported File Formats
- Key Takeaways
- QUIZ: Importing & Exporting Data
- Why Multiple Tables
- Appending DataFrames
- ASSIGNMENT: Appending DataFrames
- SOLUTION: Appending DataFrames
- Joining DataFrames
- Join Types
- Inner Joins
- Left Joins
- ASSIGNMENT: Joining DataFrames
- SOLUTION: Joining DataFrames
- The Join Method
- Key Takeaways
- QUIZ: Joining DataFrames
- Final Project Intro
- SOLUTION: Final Project
- Final Assessment
- Course Feedback Survey
- Share the love!
- Next Steps
WHO SHOULD TAKE THIS COURSE?
- Analysts or BI professionals looking to learn NumPy & Pandas
- Aspiring data scientists who want to build core Python skills
- Anyone interested in learning one of the most popular open source programming languages in the world
WHAT ARE THE COURSE REQUIREMENTS?
- Jupyter Notebooks (free download, we'll walk through the install)
- No advance preparation is required (basic familiarity with programming is a plus, but not a prerequisite)
WHAT ARE THE COURSE OBJECTIVES?
Identify NumPy array properties and syntax, including array creation, indexing & slicing, operations, aggregation, vectorization, and broadcasting
Identify basic properties and data types for Pandas Series and DataFrames
Identify and interpret Pandas syntax for exploring Series and DataFrames, including indexing, accessing, sorting, and filtering
Identify and interpret Pandas syntax for manipulating Pandas Series and DataFrames, including handling missing values, applying custom functions, and dropping & creating columns
Identify and interpret examples of optimizing memory use in Pandas DataFrames, including type conversion and downcasting
Identify and interpret Pandas syntax for aggregating DataFrames, including grouping columns, accessing multi-index DataFrames, aggregating groups, and pivoting & unpivoting
Identify and interpret basic data visualization methods using Pandas, including customizing chart formatting, changing chart types, and saving charts as images
Identify and interpret Pandas syntax for working with time series data, including the datetime data type, formatting & parting, time deltas, shifting, resampling, and aggregating
Identify the proper syntax and functions for reading, processing, and writing data from different sources, including flat files, SQL databases, and other formats
Identify and interpret Pandas syntax for combining multiple DataFrames, including appending data to add rows and using several join methods to add related columns
CPE ACCREDITATION DETAILS:
CPE Credits: 22.0
Field of Study: Information Technology
Delivery Method: QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: July 21, 2022
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