Data Analytics · Intermediate
Pandas and NumPy for Data Analysis
Master the two core libraries of Python data analysis: fast numeric computing with NumPy and flexible data manipulation with Pandas.
- Price
- $35
- Level
- Intermediate
- Duration
- 7 hours
- Lessons
- 28 lessons
- Projects
- 5 hands-on projects
- Prerequisites
- Basic Python (variables, loops, functions)
Overview
Pandas and NumPy for Data Analysis is a focused 7-hour course for learners who already know basic Python and want to work with data seriously. Across 28 lessons and 5 projects, you learn the two libraries that underpin almost all Python data work.
The course starts with NumPy arrays — the foundation of fast numeric computing in Python — then moves to Pandas DataFrames for real-world tabular data. You practice the operations analysts use every day: filtering, grouping, handling missing values, and reshaping data.
Each module ends with an exercise set, and the projects use realistic, imperfect datasets so you learn to deal with data as it actually arrives: messy.
What you'll learn
- Compute efficiently with NumPy arrays and vectorized operations
- Create, inspect, and manipulate Pandas DataFrames
- Clean datasets: types, duplicates, and inconsistent values
- Filter rows and select data precisely with loc and boolean masks
- Group and aggregate data to compare segments
- Detect and handle missing values with intention, not defaults
- Transform and reshape data for analysis: apply, map, merge, and pivot
- Complete 5 projects on realistic, messy datasets
Curriculum
7 modules · 28 lessons · 7 hours of material
Module 1 — NumPy Arrays
- Why NumPy: arrays vs. lists
- Creating and indexing arrays
- Vectorized operations and broadcasting
Module 2 — DataFrames Fundamentals
- Series and DataFrames
- Reading data from files
- Inspecting and summarizing a dataset
Module 3 — Selecting and Filtering
- Selecting columns and rows with loc and iloc
- Boolean masks and complex conditions
- Project: slicing a sales dataset
Module 4 — Data Cleaning
- Fixing data types
- Removing duplicates
- Standardizing messy text columns
- Project: cleaning survey responses
Module 5 — Grouping and Aggregation
- groupby fundamentals
- Multiple aggregations at once
- Project: cohort comparison
Module 6 — Missing Values
- Finding missing data
- Dropping vs. filling: making the right call
- Interpolation and sentinel values
Module 7 — Data Transformation
- apply and map
- Merging and joining DataFrames
- Pivoting and reshaping
- Final project: full transformation pipeline
Who this course is for
- Learners who finished a Python basics course and want to go deeper into data
- Analysts moving from spreadsheets to programmatic analysis
- Developers who need to process tabular data efficiently
- Data analytics students strengthening their core toolkit
Prerequisites
Basic Python (variables, loops, functions)
Course outcomes
By the end of this course, you will be able to:
- Manipulate any tabular dataset confidently with Pandas
- Write vectorized NumPy code instead of slow Python loops
- Clean and reshape messy real-world data into analysis-ready form
- Build reusable transformation pipelines across 5 completed projects
Frequently asked questions
How much Python do I need before starting?
You should be comfortable with variables, loops, functions, lists, and dictionaries. Completing Python for Beginners — or equivalent experience — is sufficient.
Do I need to know NumPy before Pandas?
No. The course teaches NumPy first precisely because Pandas is built on top of it. Understanding arrays makes DataFrame behavior much easier to reason about.
How is this different from the Data Analytics Bootcamp?
The bootcamp is a broad beginner path covering Python, SQL, statistics, and visualization. This course goes deeper into the Pandas and NumPy toolkit specifically, at an intermediate level.