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Python Data Analytics with Pandas – Intensive I

Duration: 5 Hours
Level: Beginner → Intermediate
Format: Live Human Online Intensive with Hands-On Labs
Tools Used: Python + Pandas (run locally in VS Code or similar IDE)

Intensive Schedule (5 Hours)

Session 1: Pandas Foundations and the Analytics Mindset

  • What Pandas is and why it’s used for analytics

  • DataFrames vs spreadsheets

  • Rows, columns, indexes, and data types

  • Thinking in transformations, not formulas

Lab 1:

  • Load datasets from CSV and Excel

  • Inspect structure using .head(), .info(), .describe()

  • Identify data quality issues

  • Establish analysis goals

Session 2: Cleaning and Preparing Data

  • Handling missing and null values

  • Fixing data types (dates, numbers, text)

  • Renaming and restructuring columns

  • Filtering and sorting data

Lab 2:

  • Clean a messy dataset

  • Convert and standardize data types

  • Filter rows based on conditions

  • Validate cleaned results

Session 3: Transforming and Analyzing Data

  • Creating calculated columns

  • Grouping and aggregations

  • Summaries and metrics

  • Replacing spreadsheet-style logic

Lab 3:

  • Build calculated fields

  • Group data by categories

  • Generate summary tables

  • Compare results to spreadsheet logic

Session 4: Combining, Reshaping, and Slicing Data

  • Merging and joining datasets

  • Concatenating data

  • Reshaping with pivot-style operations

  • Selecting subsets for analysis

Lab 4:

  • Merge multiple datasets

  • Build pivot-style summaries

  • Slice data for focused analysis

  • Validate joins and totals

Session 5: Reporting Outputs and Reusable Workflows

  • Exporting results to Excel and CSV

  • Creating repeatable analysis scripts

  • Adding validation checks

  • Preparing data for visualization or dashboards

Lab 5:

  • Export cleaned and analyzed datasets

  • Build a reusable analysis script

  • Add basic validation checks

  • Prepare outputs for downstream use

Intensive Description

Python Data Analytics with Pandas – Intensive I is a deep, hands-on introduction to using Python for real-world data analysis. This intensive is designed for learners who are ready to move beyond spreadsheets and AI-assisted tools into clear, testable, and scalable analytics workflows.

Rather than focusing on isolated functions, this intensive teaches participants how to think like a data analyst—cleaning data, transforming it intentionally, validating results, and producing outputs that can be trusted and reused.

All instruction is lab-driven, with datasets that mirror common business, accounting, and reporting scenarios.

By the end of the intensive, attendees will confidently use Pandas to clean, analyze, and prepare data—without relying on fragile formulas or guesswork.

Lab & Exercise Structure

Each lab is:

  • Instructor-led with live explanation

  • Built on realistic datasets

  • Focused on validation and correctness

  • Designed to replace spreadsheet-heavy workflows

Labs emphasize why each step matters—not just how to write the code.

Who This Intensive Is For

  • Spreadsheet power users moving to Python

  • Analysts and accountants handling growing datasets

  • Professionals automating repeatable reports

  • Anyone needing reliable, scalable data analysis

What You’ll Gain

  • Strong foundation in Pandas data analysis

  • Confidence cleaning and transforming real datasets

  • Ability to replace complex spreadsheets with code

  • Skills to build repeatable analytics workflows

Other Python Data Analytics Intensives

This intensive begins the Pandas analytics track.
Additional Python analytics intensives expand on this foundation by covering:

  • Advanced Pandas techniques and performance

  • Time series and financial data analysis

  • Visualization and dashboards

  • Integrating Pandas with databases and APIs

Python Data Analytics with Pandas – Intensive I gives you the skills to turn raw data into insight—clearly, correctly, and at scale.