Master Python programming for data analysis, visualization, and machine learning with real-world projects.

What you'll learn

Understand the core concepts of data science and its applications.

Set up a professional environment with tools like Anaconda, Google Colab, and Git.

Master advanced Excel techniques, including formulas, PivotTables, and macros.

Learn Python fundamentals and leverage libraries like NumPy and Pandas for data manipulation.

Create static and interactive visualizations using Matplotlib, Seaborn, and Plotly.

Develop skills in Power BI for building dynamic dashboards and reports.

Preprocess, clean, and prepare data for machine learning applications.

Work on real-world projects like chatbot development and image classification.

Requirements

Basic knowledge of computers and mathematics.

Familiarity with programming concepts is helpful but not required.

A PC or laptop with internet connectivity to run the tools and code.

A willingness to learn and work on practical, hands-on projects.

Description

Data science is one of the most in-demand fields of the decade, and this course, Python for Data Science: From Basics to Advanced in 2025, offers an in-depth learning experience that caters to beginners and professionals alike. Covering a broad spectrum of topics, from data analysis to machine learning, this course equips you with the tools and skills needed to excel in the field of data science.We start with a solid introduction to data science, exploring its significance, workflow, and essential tools and skills required to thrive. You'll set up your environment with tools like Anaconda, Google Colab, and Git for version control, ensuring you have a seamless start to your journey. Next, we dive into Advanced Excel, where you'll master data cleaning, pivot tables, formulas, and even macros for automation.The course transitions into Python for Data Science, where you'll learn to manipulate data using NumPy and Pandas. Data visualization is a core skill, and you'll explore Matplotlib, Seaborn, and Plotly to create both static and interactive visualizations.You'll also learn to use Power BI for data modeling and creating dynamic dashboards. A thorough Statistics Deep Dive builds your foundation in advanced statistical measures, hypothesis testing, and inferential analysis.From data preprocessing and feature engineering to machine learning, you'll explore supervised, unsupervised, and neural network models. Finally, apply your skills with real-world projects like building a chatbot and an image classifier.This comprehensive course will help you confidently enter the field of data science!

Overview

Section 1: Introduction to Data Science

Lecture 1 Introduction to the Course

Lecture 2 Overview of Data Science and its Importance

Lecture 3 Introduction to the Data Science Workflow

Lecture 4 Key Skills and Tools in Data Science

Section 2: Setting Up Your Environment

Lecture 5 Anaconda Setup and Overview

Lecture 6 Google Colab Notebook Overview

Lecture 7 Version Control With Git/Gitlab

Section 3: Advanced Excel For Data Analysis

Lecture 8 Advanced Excel Introduction

Lecture 9 Advanced Formulas and Functions

Lecture 10 Data Cleaning and Preparation Techniques

Lecture 11 Time and Date Manipulation Functions

Lecture 12 Pivottables, Pivotcharts, and Power Query

Lecture 13 Introduction to Excel Macros and VBA for Automation

Section 4: Python For Data Science

Lecture 14 Python Fundamentals (variables, data types, conditionals, loops)

Lecture 15 Functions, Lambda Expressions, and Error Handling

Lecture 16 Data Manipulation and Analysis with Numpy

Lecture 17 Working with Data Using Pandas

Section 5: Data Visualization

Lecture 18 Principles of Data Visualization

Lecture 19 Introduction to Matplotlib and Seaborn

Lecture 20 Creating Interactive Visualizations with Plotly

Section 6: Introduction to Power BI

Lecture 21 Getting Started with Power Bi Desktop

Lecture 22 Creating Dashboards and Reports

Lecture 23 Data Modeling and Dax Basics

Section 7: Statistics Deep Dive

Lecture 24 Advanced Statistical Measures and Distributions

Lecture 25 Correlation Concept

Lecture 26 Hypothesis Testing and Inferential Statistics

Section 8: Data Preprocessing Concepts

Lecture 27 Data Cleaning and Normalization

Lecture 28 Handling and Removing Outliers

Lecture 29 Feature Engineering and Selection

Section 9: Major Machine Learning Algorithms

Lecture 30 Introduction to Machine Learning

Lecture 31 Supervised Learning

Lecture 32 Unsupervised Learning

Lecture 33 Introduction to Neural Networks and Deep Learning

Section 10: Project 1: Building a ChatBot

Lecture 34 Introduction to NLP and ChatBot Frameworks

Lecture 35 Designing and Training a Simple ChatBot

Lecture 36 Basics of Dash and Plotly Web Integration

Lecture 37 Integrating the ChatBot With Python or Web Applications

Section 11: Project 2: Image Classification Project

Lecture 38 Basics of Image Processing and Computer Vision

Lecture 39 Building and Training a Simple Image Classifier and Evaluating Model Performance

Aspiring data scientists or professionals looking to transition into the data science field.,Students and beginners eager to learn data science from scratch.,Individuals preparing for interviews or advancing in competitive programming.,Professionals seeking to upskill in data science, visualization, and machine learning.