Master Data Analysis with Python - From Beginner to Pro

Name: Master Data Analysis with Python - From Beginner to Pro
Rating: 4.3 (212 reviews)

Learn Python for data analysis from scratch build practical skills to land your first job in data science or analytics

Created byLearnify IT

Last updated 4/2026

English

English [Auto],

What you'll learn

Python Fundamentals for Data Science
Numerical Computing
Data Manipulation & Analysis
Clean, prepare, and transform messy datasets with ease
Stunning Data Visualization
Exploratory Data Analysis (EDA) Techniques

Course content

1 section • 17 lectures • 6h 4m total length

Introduction12:27
Explore data analysis with Python basics, learning to gather, clean, and analyze data using pandas, numpy, seaborn, and matplotlib, and set up Python and Jupyter for hands-on work.
Variables & Data Types24:20
Discover Python basics for data analysis by exploring variables, data types, and simple operations in a Jupyter notebook, using pandas and numpy for arrays, data frames, and filtering.
List & Dictionary24:54
Explore lists and dictionaries in Python, learning loops, conditionals, indexing, and basic data manipulation to build a solid Python data analysis foundation.
Loops & Conditionals New Update13:01
Simple Python calculations and list Iteration16:18
Master data analysis with Python by learning simple arithmetic operations, list creation, iteration with for loops, and storing results in new lists using Jupyter notebook.
Introduction to pandas and Data Frames30:11
Master pandas and data frames to read csv files, inspect data with head and describe, and manipulate rows, columns, filtering, and missing data in Jupyter.
Load and Explore a Simple Dataset (e.g., Titanic or Iris)25:10
Load and explore a simple dataset such as iris or Titanic using Python in Jupyter notebook. Analyze shape, info, and basic statistics, and visualize relationships with seaborn and matplotlib.
Data Cleaning and Preparation29:45
Learn data cleaning and preparation in Python using pandas and numpy in Jupyter Notebook, handling missing values, removing duplicates, fixing data types, encoding categoricals, and applying scaling and train-test split.
Handling Missing Data (NaN) & Filtering Data using Conditions30:02
Master data analysis with Python teaches handling missing data (NaN) and filtering data with conditions using pandas, including detecting, dropping, filling, interpolating, and querying data frames.
Sorting and Indexing and Creating new Columns30:12
Explore sorting and indexing in pandas, create new columns, compute averages, and assign pass/fail grades, then apply lambda, np.where, and map for data analysis.
Data Type Conversion & Cleaning Messy Dataset29:52
Master data analysis with Python by performing data type conversion and cleaning tasks. Work in a Jupyter Notebook using pandas to transform data, handle missing values, and deduplicate.
EDA and Data Distribution new update17:50
Learn exploratory data analysis and data distribution with Python using iris dataset; import libraries, inspect data, visualize distributions, and test normality with Shapiro, skewness, and kurtosis.
Grouping and Aggregating (groupby) & Value Counts and Unique Values23:19
Learn how to group and aggregate data with pandas groupby, perform sum, mean, and count, and explore value counts and unique values for categorical analysis.
Simple Narrative Using Markdown and Code17:50
Use markdown and code in a Jupyter notebook to analyze students performance data. Leverage pandas, seaborn, and matplotlib to reveal patterns by gender across math, reading, and writing.
Capstone Project11:10
Design and complete a capstone project analyzing Covid-19 data using a Jupyter notebook. Clean, explore, and visualize global trends with pandas, numpy, matplotlib, and seaborn, then export results.
Class Project 110:25
Build practical data analysis skills in a Jupyter notebook by importing libraries, loading and cleaning data, analyzing and visualizing, and drawing insights with a five-step framework.
Class Project 217:23
Visualize key patterns in the data set using seaborn, matplotlib, and pandas; learn to spot trends, relationships, and outliers with histograms, scatter plots, heat maps, and box plots.

Requirements

No experience required

Description

Are you ready to transform raw data into powerful insights? Do you want to master the most sought-after skills in today's data-driven world? This comprehensive course is your complete roadmap to becoming a highly proficient data analyst using Python, even if you've never written a line of code before!

What You'll Learn:

The fundamentals of Python programming with a focus on data tasks
How to use data manipulation and analysis
Working with numerical operations
Creating stunning data visualizations
Data cleaning techniques for real-world messy datasets
Importing, transforming, and exporting data
Applying exploratory data analysis (EDA) to uncover insights
Building end-to-end mini-projects to reinforce your learning

Who is this course for?

Absolute Beginners: No prior programming or data analysis experience required. We start from scratch!

Why Choose This Course?

Hands-on Learning: Engaged of practical lectures, coding exercises, and real-world projects.
Clear & Concise Explanations: Complex concepts broken down into easily digestible lessons.

By the end of this course, you'll not only understand the core concepts of data analysis — you’ll have the confidence and skills to work with real-world datasets, solve practical problems, and pursue roles in data analytics or data science. Don't just look at data – understand it, interpret it, and master it! Enroll now and embark on your journey to becoming a Master Data Analyst with Python!

Who this course is for:

Anyone who wants to analyze and visualize data using Python