Master Data Analysis With Python - From Beginner To Pro

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

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

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 tasksHow to use data manipulation and analysisWorking with numerical operationsCreating stunning data visualizations Data cleaning techniques for real-world messy datasetsImporting, transforming, and exporting data Applying exploratory data analysis (EDA) to uncover insightsBuilding end-to-end mini-projects to reinforce your learningWho 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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Variables & Data Types

Lecture 3 List & Dictionary

Lecture 4 Loops & Conditionals

Lecture 5 Simple Python calculations and list Iteration

Lecture 6 Introduction to pandas and Data Frames

Lecture 7 Load and Explore a Simple Dataset (e.g., Titanic or Iris)

Lecture 8 Data Cleaning and Preparation

Lecture 9 Handling Missing Data (NaN) & Filtering Data using Conditions

Lecture 10 Sorting and Indexing and Creating new Columns

Lecture 11 Data Type Conversion & Cleaning Messy Dataset

Lecture 12 EDA and Data Distribution

Lecture 13 Grouping and Aggregating (groupby) & Value Counts and Unique Values

Lecture 14 Simple Narrative Using Markdown and Code

Lecture 15 Capstone Project

Lecture 16 Class Project 1

Lecture 17 Class Project 2

Anyone who wants to analyze and visualize data using Python