Data Analysis with Pandas & NumPy

Data Analysis with Pandas & NumPy – taught by Muhammad Shafiq – is a hands‑on Udemy course that equips learners with practical Python‑based data‑analysis skills.
If you search for a free data analysis course, Udemy data analysis course, or learn data analysis online, this 2026‑updated program appears among the top results.
The curriculum focuses on real‑world data manipulation, NumPy numerical computing, and Pandas DataFrame mastery, preparing students for entry‑level analyst roles and certification‑ready projects.

What You'll Learn

• Build end‑to‑end data pipelines using Pandas and NumPy to transform raw files into clean, analysis‑ready datasets.
• Master NumPy array operations, broadcasting, and vectorized calculations for high‑performance numerical work.
• Learn how to load CSV, Excel, JSON, and SQL data sources directly into Pandas DataFrames.
• Understand data‑cleaning techniques, including missing‑value imputation, duplicate removal, and type conversion.
• Create advanced indexing, selection, and filtering queries that isolate precise data subsets.
• Implement grouping, aggregation, and pivot‑table strategies to summarize large datasets efficiently.
• Apply merging and concatenation methods for combining multiple DataFrames across common keys.
• Analyze basic visualizations with Matplotlib and Seaborn to communicate insights derived from Pandas analysis.

Course Details

• Instructor: Muhammad Shafiq
• Rating: 4.2 stars
• Duration: Not specified
• Level: Beginner to Intermediate
• Language: English (en‑US)
• Enrolled students: 21,996
• Last updated: Not specified
• Certificate: Yes, upon completion
• Includes: Lifetime access, mobile‑friendly playback, downloadable resources

What This Course Covers

Introduction to Python for Data Analysis

• Overview of Python’s role in the data‑science ecosystem.
• Installation of Anaconda, Jupyter Notebook, and essential libraries.
• Basic Python syntax, data structures, and control flow relevant to analysts.

NumPy Fundamentals

• Creation of NumPy arrays, dimensionality, and dtype selection.
• Vectorized arithmetic, broadcasting rules, and performance advantages.
• Common functions for statistical calculations and linear algebra.

Pandas DataFrames & Series

• Building Series and DataFrames from dictionaries, lists, and external files.
• Indexing, slicing, and label‑based selection using .loc and .iloc.
• Applying functions with .apply(), .map(), and lambda expressions.

Data Cleaning & Preprocessing

• Detecting and handling missing values with fillna() and dropna().
• Removing duplicate rows and standardizing column data types.
• Converting dates, categoricals, and text fields for analysis readiness.

Advanced Indexing, Merging & Aggregation

• Multi‑level (hierarchical) indexing for complex data hierarchies.
• Merging, joining, and concatenating multiple DataFrames on keys.
• GroupBy operations, pivot tables, and custom aggregation functions.

Exporting & Visualizing Insights

• Saving cleaned data to CSV, Excel, and SQL databases.
• Generating line, bar, and scatter plots with Matplotlib and Seaborn.
• Embedding visualizations in Jupyter notebooks for reporting purposes.

Who Should Take This Course

• Aspiring data analysts who need a solid Python foundation for everyday tasks.
• Business‑intelligence professionals seeking faster data‑processing workflows.
• Researchers and academics handling experimental datasets that require cleaning and summarization.
• Undergraduate or graduate students pursuing degrees in data science, computer science, or related fields.
• Junior developers transitioning into analytics‑focused roles within tech companies.

Prerequisites

• No prior experience needed — this course is beginner‑friendly.
• Basic computer literacy and familiarity with installing software.
• Recommended: elementary understanding of statistics and Excel for context, though not required.

Why Enroll in This Course

The course delivers a complete, project‑driven pathway from raw data ingestion to actionable insights, making it ideal for career‑switchers and students alike. A free coupon provides 100 % off for a limited time, ensuring learners can start immediately without financial barriers. Because the content is updated regularly, students benefit from the latest library versions and industry best practices as of July 2026. Compared with other Udemy offerings, this program emphasizes practical implementation over theory, accelerating real‑world competency.

Course Highlights

• Lifetime access to all video lectures, quizzes, and downloadable assets.
• Self‑paced learning that fits any schedule, with progress tracking in Udemy’s app.
• Certificate of completion that can be added to LinkedIn or résumé.
• Hands‑on projects using real‑world datasets from finance, health, and e‑commerce domains.
• Mobile‑friendly interface allowing study on smartphones or tablets.
• 30‑day money‑back guarantee for risk‑free enrollment.

Frequently Asked Questions

Q: Is this course really free?
A: Yes, a free coupon grants 100 % off the regular price for a limited period, allowing unlimited access without payment.

Q: What will I learn in this data analysis course?
A: You will master NumPy numerical operations, Pandas data manipulation, data cleaning, advanced indexing, merging techniques, and basic visualizations, all using Python.

Q: Do I get a certificate after completing this course?
A: Upon finishing all lectures and assessments, Ud