Complete Guide to Python Data Analysis with Real Datasets

Name: Complete Guide to Python Data Analysis with Real Datasets
Rating: 4.1 (36 reviews)

Learn Python Programming, Data Analysis, and Machine Learning Techniques to Solve Real World Business Challenges with AI

Created byBrighter Futures Hub

Last updated 9/2025

English

English [Auto],

What you'll learn

Overview of Python in Data Science
Variables, Data Types, Lists, Dictionaries
Control Structures (if/else, loops)
Functions and Modules
Introduction to Key Libraries:NumPy, Pandas, Matplotlib
Data Selection and Filtering
Handling Missing Data
Data Transformation and Aggregation
Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
Encoding Categorical Variables
Feature Scaling and Normalization
Merging and Joining DataFrames
Introduction to Matplotlib: Line Plots, Bar Charts, Pie Charts
Introduction to Seaborn: Heatmaps, Pairplots, Violin Plots
Customizing Visualizations: Titles, Labels, Legends, Colors
Correlation and Regression Analysis

Course content

7 sections • 21 lectures • 3h 14m total length

Overview of Python in Data Science6:09
Explore how Python enables data science with simple syntax, cross-platform flexibility, and a rich library ecosystem for data manipulation, statistical analysis, machine learning, and visualization.
Variables, Data Types, Lists, Dictionaries11:06
discover how to declare variables in Python, explore data types such as int, float, string, and bool, and work with lists and dictionaries, including indexing, updating, and printing types.
Functions and Modules6:38
Define and call functions in Python, pass parameters, and return values, then organize code with modules using the built-in math module and a custom module for reuse.
Introduction to Key Libraries:NumPy, Pandas, Matplotlib10:47
Explore NumPy for numerical computing with arrays and broadcasting, pandas for data frames and manipulation, and Matplotlib for versatile plots.

Introduction to Pandas: Series and DataFrames6:20
Learn pandas basics in Python by building Series and DataFrames, creating a Series from a list, and converting dictionaries into a DataFrame with pd.DataFrame.
Reading and Writing Data (CSV, Excel, JSON)6:52
Learn to read and write data with Pandas in Python, using CSV, Excel, and JSON formats, including reading CSV with read_csv, writing with to_csv, and handling multiple Excel sheets.
Data Selection and Filtering8:13
Master data selection and filtering in pandas by selecting columns and rows from a data frame and applying conditional filters to extract relevant data from real datasets.
Handling Missing Data7:50
Identify, remove, and fill missing data in pandas dataframes using isnull and isna checks, dropna, and practical filling techniques for real datasets.
Data Transformation and Aggregation9:39
Learn data transformation and aggregation in Python with pandas, applying functions to modify columns, and computing sums, averages, and medians across groups.

Understanding EDA and its Importance6:03
Explore exploratory data analysis (EDA) and its importance for data science. Learn data cleaning, transformation, descriptive statistics, and visualization to uncover patterns, detect outliers, and guide modeling decisions.
Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation9:36
Learn to compute mean, median, mode, variance, and standard deviation in Python from real datasets, using code to illustrate central tendency and data dispersion.

Encoding Categorical Variables11:00
Learn to encode categorical variables in Python with pandas, using label encoding, one-hot encoding via get_dummies, and ordinal encoding for ordered categories, preparing data for machine learning models.
Feature Scaling and Normalization7:40
Learn feature scaling and normalization for data preprocessing. Apply min-max scaling to 0-1 and robust scaling to reduce outlier impact on age and salary features.
Merging and Joining DataFrames7:56
Explore merging and joining data frames in Python with pandas, using inner joins on the employee ID and index to combine department and salary data from multiple datasets.
Reshaping Data (Pivot Tables, Melt, Stack/Unstack)14:13
Explore reshaping data with pandas in Python, using pivot tables, melt, and stack/unstack to convert wide data into long form, indexing by date and category to summarize sales.

Introduction to Matplotlib: Line Plots, Bar Charts, Pie Charts11:39
Explore Matplotlib to visualize data with line plots, bar charts, and pie charts, using examples like prime numbers and programming language popularity to illustrate labels, colors, and titles.
Introduction to Seaborn: Heatmaps, Pairplots, Violin Plots10:49
Explore seaborn heatmaps for visualizing correlations, pair plots for exploring relationships among multiple numerical variables, and violin plots for understanding data distribution, using iris and Titan data sets.
Customizing Visualizations: Titles, Labels, Legends, Colors13:18
Learn to customize matplotlib visuals with titles, axis labels, legends, and colors, including multi-series plots and bar charts using hex color codes and tailored fonts.

Requirements

No prior experience is needed.

Description

Complete Guide to Python Data Analysis with Real Datasets

In today’s world, data is everywhere — but raw data by itself doesn’t tell a story. The ability to clean, analyze, and visualize data is one of the most valuable skills in business, research, and technology. If you want to turn raw numbers into actionable insights, this course will take you step by step through the complete process of Python based data analysis using real world datasets.

This is not just another theory heavy Python course. Instead, it’s built around practical, hands on projects that mirror the type of work done by professional data analysts and data scientists. By the end of this course, you’ll be able to confidently use Python’s most powerful libraries to solve real data challenges.

What You’ll Learn in This Course:

Even if you’re new to Python, we’ll guide you through the basics you need — from variables and loops to functions and data structures.
Learn how to manipulate large datasets efficiently using Pandas DataFrames and NumPy arrays.
Master techniques to handle missing values, duplicates, inconsistent formats, and messy datasets to make them ready for analysis.
Discover hidden patterns and trends in your data using descriptive statistics and hands on analysis.
Create powerful, easy to understand visualizations using Matplotlib and Seaborn. Learn to build line charts, bar plots, histograms, scatter plots, heatmaps, and more.
Work on real world datasets from domains like business, finance, healthcare, sports, and social media. These case studies will prepare you for real life applications.
Get a beginner friendly introduction to predictive modeling with Scikit learn, including regression and classification examples.

Why Choose This Course?

No more toy examples. You’ll be working with real, messy datasets just like professionals do.
Complex concepts are broken down into simple, beginner friendly explanations.
Employers value data analysts who can work with real data. This course gives you exactly that experience.
From Python basics to advanced analysis techniques, everything you need is included here.

By the End of This Course, You Will Be Able To:

Use Python confidently for data analysis tasks.
Clean, transform, and prepare datasets for deep insights.
Build interactive and meaningful data visualizations.
Take your first steps into machine learning workflows.

Data analysis is one of the most indemand skills in today’s job market, and Python makes it easier and more powerful than ever. With real datasets, handson projects, and clear explanations, this course ensures you not only learn data analysis concepts but also apply them in practice.

Enroll today and start transforming data into insights with Python!

Who this course is for:

Anyone curious about data and looking to explore data-driven problem solving.
Students and professionals who want to add Python and data analysis skills to their toolkit.
Who want to start a career in data analysis or data science.
Business professionals who want to use data for decision-making and insights.

Complete Guide to Python Data Analysis with Real Datasets

What you'll learn

Explore related topics

Course content

Introduction to Python for Data Analysis4 lectures • 35min

Data Manipulation with Pandas5 lectures • 39min

Exploratory Data Analysis (EDA)2 lectures • 16min

Advanced Data Cleaning and Preparation4 lectures • 41min

Data Visualization with Matplotlib and Seaborn3 lectures • 36min

Statistical Analysis with Python1 lecture • 9min

Introduction to Machine Learning with Scikit Learn2 lectures • 19min

Requirements

Description

Who this course is for: