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Complete Python Data Analysis : Numpy and Pandas Masterclass

Name: Complete Python Data Analysis : Numpy and Pandas Masterclass
Rating: 4.3 (4 reviews)

Master data analysis with NumPy and Pandas by cleaning, transforming, merging and visualizing real-world datasets.

Created bySatyapal Singh

Last updated 11/2025

English

What you'll learn

Master the essentials of NumPy and Pandas, two of Python's most powerful data analysis packages
Analyze and manipulate dates and times for time intelligence and time-series analysis
Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms
Build powerful, practical skills for modern analytics and business intelligence

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

11 sections • 105 lectures • 10h 36m total length

Introduction2:16
Master NumPy, the foundation of numerical computing in Python, and learn how its nd array, broadcasting, and vectorization power data computation with Pandas, SciPy, TensorFlow, and PyTorch.
Creating Arrays4:52
Array Attributes and Properties6:40
Explore how NumPy arrays are built and behave by examining ndim, shape, size and item size, and learn to convert dtypes, reshape, flatten, and transpose with T or transpose.
Reshape a NumPy Array
Indexing and Slicing7:13
Array Operations8:31
Statistical and Mathematical Functions5:48
Explore NumPy's statistical and mathematical functions, including aggregate operations and axis-based operations. Apply cumulative calculations and element-wise functions like sine, cosine, exponential, square root, and log.
Array Statistics
Advanced Array Manipulation6:56
Filtering and Conditional Logic6:03
Sorting and Searching6:22

Series8:07
DataFrame Basics4:31
Inspecting Data5:53
Inspect data in pandas using head, tail, info, and describe to quickly understand a dataset’s structure, shape, data types, and quality.
Indexes Columns DataTypes8:04
Explore pandas dataframes by mastering indexes, columns, and data types, then inspect, modify, and convert data with practical examples for real-world datasets.
Selecting Data loc & iloc7:29
Master pandas with loc and iloc to access data by labels or positions, filter with conditions, and update values in data frames.
Select with iloc
Select with loc
Adding, Renaming, Deleting Columns4:57
Basic Statistics Summary7:29

Advanced loc7:11
Explore advanced indexing and filtering in pandas with the log indexer, enabling label-based selection, ranges, and conditional updates on a product sales dataset.
Advanced iloc6:54
Basic Conditional Filtering6:47
Filtering Using Query7:44
Filter Using .query()
Membership Filtering ( isin )7:33
Discover pandas membership filtering with the dot isIn() method to filter rows by multiple values, exclude with not, and apply dynamic, multi-column conditions.
Range Filtering ( between )6:22
Range Filtering with .between()
Detecting Duplicates8:17
Conditional Assignment ( where )7:27
Boolean Masking6:45
Chaining Filtering Operations4:08
Filtering Pitfalls6:23

Identifying Missing Data5:44
Detect missing data in pandas using isna and notna. Identify incomplete rows and columns and assess the percentage of missing values to guide preprocessing.
Identify Missing Data
Handling Missing Data5:54
Filling Missing Data7:07
Fill Missing Values
Interpolation Techniques6:14
Type Detection and Conversion7:32
Text Cleaning str Accessor6:49
Handling Outliers8:30
Normalizing and Standardizing7:34
Chaining and Pipe6:49

Introduction to Combining Data7:42
Vertical Concatenation5:59
Vertical Concatenation
Horizontal Concatenation6:24
Master horizontal concatenation of data frames with pandas using concat along axis 1 to join columns and align indices. Use keys to distinguish columns and handle NaN for missing data.
Handling Mismatched Columns in Concatenation6:07
Basic Merging5:56
Types of Joins7:02
Explore pandas joins: inner, left, right, and outer, using merge to combine data on a shared key, preserving or enriching datasets with missing values.
Merging on Index & MultiIndex7:00
Join Method7:24
Inner Join Using Merge
Combining Multiple DataFrames6:43
Dealing with Duplicate or Overlaping Keys7:23
Detect duplicate keys and overlapping column names when merging pandas data frames, then fix by dropping duplicates or aggregating, and use suffixes to clarify overlapping columns.

Intro to Time Series5:55
Explore time series analysis in pandas, learning how to convert timestamps to a date-time index with pd.to_datetime and perform time-based slicing, resampling, and rolling calculations.
Datetime and Timestamp Objects7:18
Master date and time handling with Python's DateTime and Pandas Timestamp to create, compare, and manipulate timestamps, extract components, and compute time deltas for event-log analysis.
Datetime Index, Period Index, TimeDelta Index8:45
Parsing and Converting Dates7:15
Setting and Sorting By Datetime Index6:52
Select Data by Year
Time based Selection and Slicing7:25
Time Deltas and Date Arithmetic6:36
Slice by Date Range
Resampling and Frequency Conversion6:17
Shifting , Lagging & Leading6:52

Vectorized Operations in Pandas4:03
Discover how vectorized operations in pandas replace loops by applying calculations to entire columns, delivering faster, cleaner data analysis with numpy under the hood.
BroadCasting in Pandas & Numpy4:54
Arithmetic Alignment Between Dataframes5:00
See how pandas aligns data by index labels during arithmetic on series and dataframes, matching by rows and columns and handling missing values with NAN via fill values.
Arithmetic Alignment Between DataFrames
.map(), .apply(), .applymap() in Depth4:56
Explore how Pandas map, apply, and applyMap transform data from series to custom row or column operations on data frames, using practical examples like salary rules and feature engineering.
Map Values Using .map()
Avoiding Loops Using Numpy4:15
Handling Complex Column Transformation3:31
Method Chaining and Numeric Processing Pipeline4:12

Intro to Data Visualization5:07
Explore data visualization in pandas to reveal trends and relationships using built-in line, bar, and scatter plots powered by matplotlib.
Line and Bar Charts6:44
Scatter Plots and Relationship Analysis5:49
Histogram and Density Plots5:08
Customizing Titles, Axes, Legends & Colors4:55
Enhance pandas charts by adding titles, axis labels, legends, and colors, then adjust line styles, grid, and figure size to improve readability and interpretation.
Subplot and Layout Control5:37
Visualization Essentials7:53

Calculating Price Changes3:49
Calculate Financial Returns2:01
TVPI3:54
CAGR3:22
Geometric Returns2:45
Risk vs Returns4:17
Simple vs Compound Interest7:22
Continuous Compounding5:35
Intro to log Returns4:22
Explore log returns and continuous compounding in stock prices, showing how to compute them with numpy log and pandas using close prices and shift, and compare to simple returns.
More about Log Returns4:30
Explore why log returns are popular, highlighting their additive property, normal distribution assumptions, volatility modeling, risk management, and portfolio optimization, and how they enable accurate CAGR calculations.

Requirements

Basic Python Needed.

Description

This course is a complete, practical guide to mastering data analysis using NumPy and Pandas, the two most widely used Python libraries in analytics, data science, finance, and machine learning. Designed for beginners and professionals alike, this course takes you from the foundations of data manipulation to advanced analytical techniques used in real-world projects.

You’ll begin by building a solid understanding of NumPy arrays and vectorized operations—the performance engine behind Pandas. Then, you’ll move step by step into Pandas, learning how to create, explore, filter, clean, and transform DataFrames like a true data professional. You’ll unlock methods to handle messy datasets, group and aggregate data for insights, and merge multiple data sources just like SQL joins.

The course also includes a dedicated module on time series analysis, equipping you with practical skills for analyzing stock trends, sales performance over time, and time-based logs. You’ll explore rolling windows, resampling, and date handling—essential tools for business analytics. Finally, you’ll learn to visualize data using Pandas and Matplotlib, creating professional charts that communicate insights clearly.

By the end of this course, you'll be able to confidently work with real datasets and build powerful data analysis workflows in Pandas and Numpy like an industry expert.

Who this course is for:

Analysts or BI professionals looking to learn data analysis with NumPy and Pandas
Aspiring data scientists who want to build or strengthen their Python skills
Anyone interested in learning one of the most popular open source programming languages in the world
Students looking to learn powerful, practical skills with unique, hands-on projects and course demos

Complete Python Data Analysis : Numpy and Pandas Masterclass

What you'll learn

Explore related topics

Coding Exercises

Course content

Numpy Crash Course9 lectures • 55min

Intro To Pandas7 lectures • 47min

Data Selection, Filtering and Indexing11 lectures • 1hr 16min

Data Cleaning and Preprocessing ( Intermediate )9 lectures • 1hr 2min

Aggregation, Grouping & Reshaping ( Intermediate to Advanced)11 lectures • 1hr 20min

Merging, Joining & Concatenation ( Advanced Foundations )10 lectures • 1hr 8min

Time Series & Data Handling ( Advanced )9 lectures • 1hr 3min

Advance Numeric Operations & Functional Techniques7 lectures • 31min

Data Visualization with Pandas + Matplotlib7 lectures • 41min

Financial Returns with Pandas10 lectures • 42min

Requirements

Description

Who this course is for: