Data Science Python

Pandas Mastery: Data Analysis in Python

Build confident, professional data analysis workflows with Python’s most essential data library

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Quick Course Facts
19
Self-paced, Online, Lessons
19
Videos and/or Narrated Presentations
6.9
Approximate Hours of Course Media
About the Pandas Mastery: Data Analysis in Python Course

Pandas Mastery: Data Analysis in Python is a practical Data Science course designed to help you work confidently with real-world datasets using Pandas. You will learn how to import, clean, transform, analyze, summarize, and export data while building habits that support accurate, repeatable analysis.

Build Professional Data Science Workflows With Pandas

  • Build confident, professional data analysis workflows with Python’s most essential data library.
  • Learn the core Pandas skills used in Data Science, analytics, reporting, and business intelligence.
  • Practice cleaning messy data, handling missing values, combining datasets, and preparing data for analysis.
  • Complete an end-to-end applied project that turns raw data into clear, reproducible insights.

This course teaches practical Pandas data analysis skills for modern Data Science work in Python.

Pandas Mastery: Data Analysis in Python starts with the foundations of Series, DataFrames, indexes, data types, and analytical workflows, then moves into importing data from CSV, Excel, JSON, and databases. You will learn how to inspect DataFrames, diagnose data quality issues, and make informed decisions about how data should be prepared before analysis.

As the course progresses, you will develop essential Data Science techniques for selecting, sorting, filtering, renaming, transforming, and reshaping data. You will also practice handling missing data, cleaning text and categories, removing duplicates, working with dates and times, and creating features that make datasets easier to understand and analyze.

You will then build stronger analytical workflows with grouping, aggregation, pivot tables, crosstabs, dataset joins, window functions, rolling metrics, ranked analysis, and time series analysis. The professional workflow lessons help you improve memory usage, performance, method chaining, exporting results, and building reproducible reports.

By the end of this course, you will be able to approach data analysis with clearer judgment, stronger Pandas skills, and a more professional process. You will leave ready to use Pandas in Data Science projects, workplace reporting, research, and applied analytics with greater speed, accuracy, and confidence.

Course Lessons

Full lesson breakdown

Lessons are organized by topic area and each includes descriptive copy for search visibility and student clarity.

Foundations

4 lessons

Lesson 1: Getting Oriented with Pandas and Analytical Workflows

18 min Preview
This lesson orients learners to Pandas as a practical tool for analytical work in Python. It introduces the role Pandas plays in a professional data workflow, the core objects learners will use throug…

Lesson 2: Series, DataFrames, Indexes, and Data Types

21 min
This lesson establishes the core mental model for working with pandas: a Series is a labeled one-dimensional array, a DataFrame is a labeled two-dimensional table, an Index provides the labels used fo…

Lesson 3: Importing Data from CSV, Excel, JSON, and Databases

20 min
In this lesson, students learn how Pandas brings external data into a DataFrame from the most common professional sources: CSV files, Excel workbooks, JSON data, and relational databases. The focus is…

Lesson 4: Inspecting DataFrames and Diagnosing Data Quality

19 min
In this lesson, students learn how to inspect a pandas DataFrame before analysis, using a repeatable first-pass workflow that reveals structure, column names, data types, missing values, suspicious ra…

Core DataFrame Skills

3 lessons

Lesson 5: Selecting Rows, Columns, Slices, and Boolean Conditions

22 min
In this lesson, students learn how to select data from a pandas DataFrame with confidence. The focus is on choosing columns, retrieving rows by label or position, slicing ranges, and filtering records…

Lesson 6: Sorting, Filtering, Renaming, and Reordering Data

18 min
In this lesson, students learn the everyday DataFrame operations that turn messy tables into analysis-ready data: sorting rows, filtering with boolean conditions, renaming labels, and reordering colum…

Lesson 7: Creating, Updating, and Transforming Columns

21 min
In this lesson, students learn how to create, update, and transform DataFrame columns using practical pandas techniques. The focus is on building new variables from existing data, applying conditional…

Cleaning and Preparation

3 lessons

Lesson 8: Handling Missing Data with Practical Judgment

22 min
Missing data is not just a technical nuisance; it is a judgment call that can change the meaning of an analysis. In this lesson, students learn how to detect missing values in pandas, measure their sc…

Lesson 9: Cleaning Text, Categories, Duplicates, and Inconsistent Values

23 min
This lesson shows how to clean the messy values that often block reliable analysis: inconsistent text, mislabeled categories, duplicate records, and values that mean the same thing but are written dif…

Lesson 10: Working with Dates, Times, and Time-Based Features

24 min
This lesson teaches students how to clean and prepare date and time data in pandas so it can be used reliably in analysis. Students learn how to convert messy string columns into true datetime values,…

Analysis Techniques

4 lessons

Lesson 11: Grouping, Aggregating, and Summarising Data

23 min
In this lesson, students learn how to use pandas groupby workflows to turn row-level data into useful summaries. The focus is on practical analysis patterns: grouping by one or more columns, applying …

Lesson 12: Pivot Tables, Crosstabs, and Multi-Level Summaries

22 min
In this lesson, students learn how to turn row-level data into clear multi-dimensional summaries using pivot_table , crosstab , and grouped aggregations with multiple index levels. The emphasis is on …

Lesson 13: Combining Datasets with concat, merge, and join

24 min
This lesson teaches students how to combine related datasets in pandas using the three core tools: concat , merge , and join . Students learn when each method is appropriate, how row-wise and column-w…

Lesson 14: Reshaping Data with melt, pivot, stack, and unstack

23 min
In this lesson, learners practice reshaping pandas DataFrames so data is arranged for analysis, visualization, and reporting. The lesson focuses on the four core reshaping tools: melt for wide-to-long…

Advanced Analysis

2 lessons

Lesson 15: Window Functions, Rolling Metrics, and Ranked Analysis

24 min
In this lesson, students learn how to use pandas window-style operations to answer questions that depend on nearby rows, ordered records, or relative position within a group. The focus is on practical…

Lesson 16: Time Series Analysis with Pandas

25 min
This lesson teaches practical time series analysis in Pandas: converting date columns, using a DatetimeIndex, sorting and slicing by time, resampling observations into useful periods, computing rollin…

Professional Workflows

2 lessons

Lesson 17: Efficient Pandas: Memory, Performance, and Method Chaining

23 min
This lesson teaches practical ways to make pandas workflows faster, lighter, and easier to maintain without sacrificing clarity. Students learn how to inspect memory usage, choose better dtypes, avoid…

Lesson 18: Exporting Results and Building Reproducible Reports

19 min
In this lesson, students learn how to turn Pandas analysis into outputs that other people can trust, rerun, and use. The focus is not just saving a DataFrame, but choosing the right export format, pre…

Applied Project

1 lesson

Lesson 19: Capstone: End-to-End Data Analysis Case Study

25 min
In this capstone lesson, students complete an end-to-end pandas analysis workflow that mirrors a professional data project: clarify the business question, inspect raw data, clean and reshape it, engin…
About Your Instructor
Professor Charles Knight

Professor Charles Knight

Professor Charles Knight guides this AI-built Virversity course with a clear, practical teaching style.