Beginner to Advanced

Data Science with Python Tutorial for Beginners to Expert

15 Chapters
150 Lessons
16+ Hours
120+ Code Examples
15K+ Learners
Start Learning Practice Questions
96%

Completion Rate

4.9★

Rating

92%

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What You'll Learn

Prerequisites

  • No prior programming experience needed
  • Basic computer knowledge
  • A laptop with internet access
  • Enthusiasm to learn!

Tools & Setup

📖 Tutorial Chapters & Curriculum

Follow the structured learning path from beginner to advanced

1

Introduction to Data Cleaning in Data Science

1 Lessons 6 min Beginner
Learn why data cleaning is an essential step in data science and how messy datasets affect analysis.
2

Introduction to Exploratory Data Analysis (EDA)

2 Lessons 7 min Beginner
Learn what exploratory data analysis is and why it is an essential step in data science projects.
3

Introduction to Data Visualization in Python

3 Lessons 7 min Beginner
Learn what data visualization is and why it is important for analyzing and communicating insights in data science.
4

Introduction to Feature Engineering in Data Science

4 Lessons 7 min Beginner
Learn what feature engineering is and why it plays a crucial role in machine learning model performance.
5

Introduction to Machine Learning

5 Lessons 7 min Beginner
Learn the fundamentals of machine learning and how algorithms learn patterns from data.
6

Introduction to Supervised Machine Learning

6 Lessons 7 min Beginner
Learn what supervised learning is and how labeled datasets help train predictive models.
7

Introduction to Unsupervised Machine Learning

7 Lessons 7 min Beginner
Learn what unsupervised learning is and how it helps discover hidden patterns in datasets.
8

Introduction to Model Evaluation in Machine Learning

8 Lessons 6 min Beginner
Learn why model evaluation is essential for measuring machine learning performance.
9

Introduction to Real World Data Science Projects

9 Lessons 7 min Beginner
Learn why real-world projects are essential for becoming a professional data scientist.
10

Introduction to Data Science Careers

10 Lessons 6 min Beginner
Learn about different career paths in data science and how to start your journey.
11

What is Data Science? Complete Beginner Guide

11 Lessons 8 min Beginner
Learn what Data Science is, why it is important, and how companies use data science to solve real-world problems.
12

Introduction to Python for Data Science

12 Lessons 6 min Beginner
Learn why Python is the most popular programming language for data science and how it is used for data analysis and machine learning.
13

Installing Python for Data Science

13 Lessons 6 min Beginner
Learn how to install Python on Windows, Linux, and macOS for data science development.
14

Introduction to NumPy for Data Science

14 Lessons 7 min Beginner
Learn what NumPy is and why it is one of the most important libraries for scientific computing and data science.
15

Introduction to Pandas for Data Analysis

15 Lessons 7 min Beginner
Learn what Pandas is and why it is one of the most important libraries for data analysis in Python.
16

Handling Missing Data in Python

16 Lessons 6 min Beginner
Learn how to detect and handle missing values in datasets using Python.
17

Descriptive Statistics in Python

17 Lessons 6 min Beginner
Learn how to calculate statistical measures like mean, median, and standard deviation.
18

Introduction to Matplotlib

18 Lessons 6 min Beginner
Learn the fundamentals of Matplotlib for creating visualizations in Python.
19

Feature Scaling Techniques

19 Lessons 6 min Intermediate
Learn how feature scaling improves machine learning models by normalizing numerical features.
20

Types of Machine Learning

20 Lessons 6 min Beginner
Understand supervised, unsupervised and reinforcement learning methods.
21

Linear Regression Algorithm

21 Lessons 7 min Beginner
Learn how linear regression predicts continuous values using mathematical relationships.
22

Clustering in Machine Learning

22 Lessons 6 min Beginner
Learn how clustering groups similar data points together.
23

Confusion Matrix Explained

23 Lessons 6 min Beginner
Learn how confusion matrices evaluate classification models.
24

Customer Segmentation using Clustering

24 Lessons 8 min Intermediate
Learn how to segment customers using machine learning clustering algorithms.
25

Skills Required for Data Science

25 Lessons 6 min Beginner
Learn the technical and analytical skills needed to become a data scientist.
26

Understanding the Data Science Lifecycle

26 Lessons 7 min Beginner
Learn the complete lifecycle of a data science project from data collection to model deployment.
27

Python Variables and Data Types

27 Lessons 7 min Beginner
Understand Python variables and data types such as integers, strings, lists and dictionaries.
28

Installing Anaconda for Data Science

28 Lessons 7 min Beginner
Learn how to install Anaconda distribution which provides a complete data science environment.
29

Understanding NumPy Arrays

29 Lessons 6 min Beginner
Learn how NumPy arrays work and why they are faster than Python lists.
30

Understanding Pandas Series

30 Lessons 6 min Beginner
Learn how Pandas Series works and how it stores one dimensional data.
31

Filling Missing Values in Data

31 Lessons 6 min Beginner
Learn how to fill missing data using statistical methods.
32

Understanding Data Distribution

32 Lessons 6 min Beginner
Learn how to analyze distributions of numerical data.
33

Creating Line Charts in Python

33 Lessons 6 min Beginner
Learn how to create line charts to visualize trends in data.
34

Normalization Techniques in Machine Learning

34 Lessons 6 min Intermediate
Learn how normalization scales data into a fixed range.
35

Machine Learning Workflow

35 Lessons 6 min Beginner
Learn the step-by-step workflow used in machine learning projects.
36

Logistic Regression for Classification

36 Lessons 6 min Beginner
Learn how logistic regression predicts categorical outcomes.
37

K-Means Clustering Algorithm

37 Lessons 7 min Beginner
Learn how the K-Means algorithm groups similar data points.
38

Accuracy Precision Recall and F1 Score

38 Lessons 6 min Beginner
Learn how accuracy, precision, recall and F1 score measure model performance.
39

House Price Prediction using Machine Learning

39 Lessons 8 min Intermediate
Learn how to build a regression model to predict house prices.
40

Complete Data Science Learning Roadmap

40 Lessons 7 min Beginner
Learn a step-by-step roadmap to become a data scientist.
41

Types of Data in Data Science

41 Lessons 6 min Beginner
Understand structured, semi-structured, and unstructured data used in data science.
42

Python Control Statements

42 Lessons 6 min Beginner
Learn how conditional statements like if, else and elif work in Python programming.
43

Installing Jupyter Notebook

43 Lessons 6 min Beginner
Learn how to install and launch Jupyter Notebook for interactive Python programming.
44

NumPy Array Operations

44 Lessons 6 min Beginner
Learn mathematical operations that can be performed on NumPy arrays.
45

Working with Pandas DataFrame

45 Lessons 6 min Beginner
Learn how Pandas DataFrames store tabular data similar to SQL tables.
46

Removing Duplicate Records in Data

46 Lessons 5 min Beginner
Learn how to identify and remove duplicate records in datasets.
47

Histogram Analysis in Python

47 Lessons 6 min Beginner
Learn how histograms help visualize numerical distributions.
48

Creating Bar Charts in Python

48 Lessons 6 min Beginner
Learn how bar charts help compare categorical data.
49

Standardization Techniques for Machine Learning

49 Lessons 6 min Intermediate
Learn how to standardize features using statistical scaling methods.
50

Training and Testing Datasets

50 Lessons 6 min Beginner
Learn how datasets are divided for training and evaluating machine learning models.
51

Decision Tree Algorithm

51 Lessons 6 min Beginner
Learn how decision trees split data to make predictions.
52

Elbow Method for Optimal Clusters

52 Lessons 6 min Intermediate
Learn how the elbow method determines the optimal number of clusters.
53

ROC Curve and AUC Score

53 Lessons 6 min Intermediate
Learn how ROC curves evaluate classification models.
54

Fraud Detection System using Machine Learning

54 Lessons 8 min Intermediate
Learn how machine learning can detect fraudulent transactions.
55

How to Build a Data Science Resume

55 Lessons 6 min Beginner
Learn how to build a professional resume for data science roles.
56

Popular Data Science Tools and Technologies

56 Lessons 6 min Beginner
Explore the most popular tools used by professional data scientists.
57

Python Loops

57 Lessons 6 min Beginner
Understand how loops work in Python including for loops and while loops.
58

Using Jupyter Notebook Interface

58 Lessons 6 min Beginner
Learn how to use the Jupyter Notebook interface for writing Python code and analyzing data.
59

NumPy Array Indexing and Slicing

59 Lessons 6 min Beginner
Learn how to access elements in NumPy arrays using indexing and slicing.
60

Reading Data from CSV and Excel using Pandas

60 Lessons 6 min Beginner
Learn how to load CSV and Excel datasets using Pandas.
61

Data Formatting and Standardization

61 Lessons 6 min Beginner
Learn how to standardize and format dataset columns.
62

Box Plot Analysis in Python

62 Lessons 6 min Beginner
Learn how box plots help detect outliers and visualize distributions.
63

Scatter Plot Visualization in Python

63 Lessons 6 min Beginner
Learn how scatter plots show relationships between variables.
64

Encoding Categorical Features

64 Lessons 6 min Beginner
Learn how to convert categorical variables into numerical features.
65

Overfitting vs Underfitting

65 Lessons 6 min Intermediate
Understand the difference between overfitting and underfitting in machine learning.
66

Random Forest Algorithm

66 Lessons 6 min Intermediate
Learn how random forest improves decision tree models using ensemble learning.
67

Hierarchical Clustering Algorithm

67 Lessons 6 min Intermediate
Learn how hierarchical clustering builds nested clusters.
68

Cross Validation Techniques

68 Lessons 6 min Intermediate
Learn how cross validation improves model reliability.
69

Movie Recommendation System

69 Lessons 8 min Intermediate
Learn how recommendation systems suggest content to users.
70

Building a Data Science Portfolio

70 Lessons 6 min Beginner
Learn how to create a portfolio showcasing your projects.
71

Role of a Data Scientist

71 Lessons 7 min Beginner
Learn what a data scientist does and the skills required for the role.
72

Python Functions Basics

72 Lessons 7 min Beginner
Learn how to create reusable functions in Python programs.
73

Installing Python Libraries for Data Science

73 Lessons 6 min Beginner
Learn how to install essential data science libraries such as NumPy, Pandas, and Matplotlib.
74

Working with Multidimensional Arrays

74 Lessons 7 min Beginner
Learn how NumPy supports multidimensional arrays for scientific computing.
75

Selecting and Filtering Data in Pandas

75 Lessons 6 min Beginner
Learn how to select rows and columns in a Pandas DataFrame.
76

Outlier Detection Techniques in Python

76 Lessons 6 min Intermediate
Learn how to detect unusual values in datasets.
77

Correlation Analysis in Data Science

77 Lessons 6 min Intermediate
Learn how to measure relationships between variables.
78

Histogram Visualization in Python

78 Lessons 6 min Beginner
Learn how histograms visualize numerical distributions.
79

Label Encoding in Python

79 Lessons 6 min Beginner
Learn how label encoding converts categories into numeric labels.
80

Bias Variance Tradeoff

80 Lessons 6 min Intermediate
Learn how bias and variance affect machine learning model performance.
81

K Nearest Neighbors Algorithm

81 Lessons 6 min Intermediate
Learn how the KNN algorithm classifies data based on nearest neighbors.
82

DBSCAN Clustering Algorithm

82 Lessons 6 min Intermediate
Learn how DBSCAN identifies clusters based on density.
83

Hyperparameter Tuning in Machine Learning

83 Lessons 6 min Intermediate
Learn how to optimize model parameters to improve performance.
84

Sentiment Analysis using NLP

84 Lessons 8 min Intermediate
Learn how to analyze text sentiment using natural language processing.
85

Common Data Science Interview Questions

85 Lessons 7 min Intermediate
Learn common questions asked in data science interviews.
86

Data Science vs Machine Learning

86 Lessons 6 min Beginner
Understand the difference between data science and machine learning.
87

Python Lists and Tuples

87 Lessons 6 min Beginner
Learn how to store multiple values using lists and tuples in Python.
88

Python Virtual Environments

88 Lessons 6 min Beginner
Learn why virtual environments are important for managing Python dependencies.
89

NumPy Array Shape and Reshaping

89 Lessons 7 min Beginner
Learn how to check and modify the shape of NumPy arrays.
90

Data Cleaning with Pandas

90 Lessons 7 min Intermediate
Learn how to clean datasets by handling missing values and duplicates.
91

Data Normalization Techniques

91 Lessons 6 min Intermediate
Learn how to normalize numerical features in machine learning datasets.
92

Correlation Heatmap Visualization

92 Lessons 6 min Intermediate
Learn how to visualize correlations using heatmaps.
93

Introduction to Seaborn Library

93 Lessons 6 min Beginner
Learn how Seaborn simplifies statistical visualization in Python.
94

One Hot Encoding in Python

94 Lessons 6 min Intermediate
Learn how one hot encoding represents categorical features using binary vectors.
95

Training Machine Learning Models in Python

95 Lessons 6 min Beginner
Learn how to train machine learning models using Python libraries.
96

Support Vector Machine Algorithm

96 Lessons 6 min Intermediate
Learn how support vector machines classify complex datasets.
97

Dimensionality Reduction Techniques

97 Lessons 6 min Intermediate
Learn how dimensionality reduction simplifies datasets.
98

Grid Search Optimization

98 Lessons 6 min Intermediate
Learn how grid search finds the best parameters for machine learning models.
99

Sales Forecasting using Time Series

99 Lessons 8 min Intermediate
Learn how time series models predict future sales trends.
100

Python Interview Questions for Data Science

100 Lessons 7 min Intermediate
Learn common Python interview questions for data science roles.
101

Data Science vs Data Analytics

101 Lessons 5 min Beginner
Understand the key differences between data science and data analytics.
102

Python Dictionaries

102 Lessons 6 min Beginner
Understand key value data structures using Python dictionaries.
103

Pip vs Conda Package Management

103 Lessons 5 min Beginner
Understand the difference between pip and conda package managers.
104

NumPy Broadcasting Explained

104 Lessons 6 min Intermediate
Learn how broadcasting allows NumPy to perform operations on arrays of different sizes.
105

Grouping Data with Pandas GroupBy

105 Lessons 6 min Intermediate
Learn how to group and aggregate data using the powerful groupby method.
106

Data Standardization Techniques

106 Lessons 6 min Intermediate
Learn how to standardize features using statistical scaling.
107

Pair Plot Analysis with Seaborn

107 Lessons 6 min Intermediate
Learn how to visualize relationships between multiple variables.
108

Creating Heatmaps with Seaborn

108 Lessons 6 min Intermediate
Learn how heatmaps visualize correlation matrices.
109

Feature Selection Techniques

109 Lessons 6 min Intermediate
Learn how to select the most relevant features for machine learning models.
110

Making Predictions using Machine Learning Models

110 Lessons 6 min Beginner
Learn how trained machine learning models make predictions.
111

Naive Bayes Algorithm

111 Lessons 6 min Intermediate
Learn how Naive Bayes classifiers work using probability theory.
112

Principal Component Analysis (PCA)

112 Lessons 6 min Intermediate
Learn how PCA reduces feature dimensions.
113

Random Search Optimization

113 Lessons 6 min Intermediate
Learn how random search efficiently explores hyperparameter space.
114

Customer Churn Prediction

114 Lessons 8 min Intermediate
Learn how companies predict which customers may leave a service.
115

Machine Learning Interview Questions

115 Lessons 7 min Intermediate
Prepare for machine learning interview questions.
116

Data Science Project Workflow

116 Lessons 6 min Beginner
Learn how a professional data science project is executed step by step.
117

Python File Handling

117 Lessons 7 min Beginner
Learn how to read and write files in Python programs.
118

Data Science Project Structure

118 Lessons 5 min Beginner
Learn how to organize professional data science projects.
119

NumPy Mathematical Functions

119 Lessons 6 min Beginner
Learn built-in NumPy mathematical functions for scientific computing.
120

Merging and Joining DataFrames in Pandas

120 Lessons 6 min Intermediate
Learn how to combine multiple datasets using merge and join operations.
121

Encoding Categorical Variables

121 Lessons 6 min Intermediate
Learn how to convert categorical data into numerical form.
122

Statistical Testing in Data Science

122 Lessons 6 min Intermediate
Learn how statistical tests help validate hypotheses.
123

Pair Plot Visualization using Seaborn

123 Lessons 6 min Intermediate
Learn how pair plots visualize relationships between multiple variables.
124

Dimensionality Reduction Techniques

124 Lessons 6 min Intermediate
Learn how to reduce dataset complexity using dimensionality reduction techniques.
125

Evaluation Metrics in Machine Learning

125 Lessons 6 min Intermediate
Learn how to evaluate machine learning model performance.
126

Ensemble Learning Techniques

126 Lessons 6 min Intermediate
Learn how ensemble learning combines multiple models to improve predictions.
127

Cluster Evaluation Metrics

127 Lessons 6 min Intermediate
Learn how to evaluate clustering performance.
128

Comparing Machine Learning Models

128 Lessons 6 min Intermediate
Learn how to compare multiple machine learning models.
129

Stock Price Prediction Project

129 Lessons 9 min Advanced
Learn how machine learning models predict stock price trends.
130

Data Science Salary Trends

130 Lessons 6 min Beginner
Learn about data science salaries across industries.
131

Future of Data Science

131 Lessons 5 min Beginner
Explore trends shaping the future of data science including AI and automation.
132

Python Exception Handling

132 Lessons 6 min Beginner
Understand how to handle errors and exceptions in Python programs.
133

Running Your First Data Science Script

133 Lessons 6 min Beginner
Learn how to run your first Python script for data analysis.
134

NumPy Random Module

134 Lessons 6 min Beginner
Learn how to generate random numbers using NumPy for simulations and machine learning.
135

Data Visualization with Pandas

135 Lessons 6 min Beginner
Learn how Pandas integrates with visualization libraries to create charts.
136

Time Series Analysis with Pandas

136 Lessons 7 min Intermediate
Learn how Pandas handles time series data for financial and forecasting applications.
137

Building a Data Preprocessing Pipeline

137 Lessons 7 min Intermediate
Learn how to build a preprocessing pipeline for machine learning workflows.
138

Feature Relationship Analysis

138 Lessons 7 min Intermediate
Learn how to analyze relationships between dataset features.
139

Interactive Data Visualization with Plotly

139 Lessons 7 min Intermediate
Learn how Plotly creates interactive charts for dashboards.
140

Understanding Feature Importance in Machine Learning

140 Lessons 7 min Intermediate
Learn how to identify the most important features influencing machine learning predictions.
141

Cross Validation in Machine Learning

141 Lessons 7 min Intermediate
Learn how cross validation improves model reliability.
142

Regression vs Classification in Machine Learning

142 Lessons 7 min Beginner
Learn the difference between regression and classification models.
143

Real World Applications of Clustering

143 Lessons 7 min Beginner
Learn how clustering is used in real-world data science projects.
144

Overview of Model Deployment

144 Lessons 7 min Intermediate
Learn how trained models are deployed into production environments.
145

Building a Data Science Portfolio

145 Lessons 7 min Beginner
Learn how to showcase projects and build a professional data science portfolio.
146

Career Growth Opportunities in Data Science

146 Lessons 6 min Beginner
Learn how data science careers grow over time.
147

Why Learn Data Science in 2026

147 Lessons 6 min Beginner
Learn why data science is one of the most valuable skills for modern careers.
148

Python Modules and Packages

148 Lessons 7 min Beginner
Learn how Python modules and packages help organize large data science projects.
149

Best Development Tools for Data Science

149 Lessons 6 min Beginner
Explore the best tools used by data scientists including Jupyter, VS Code, and Google Colab.
150

NumPy Performance vs Python Lists

150 Lessons 7 min Intermediate
Learn why NumPy is faster than Python lists for numerical computations.
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🎯 Interview Preparation

Top Python interview questions organized by difficulty

Easy 70 Questions

Freshers / Entry Level

  • Explain List Comprehensions in Python Data Science. Provide implementation details and real-world applications. (Q1)
  • Explain Functions & Lambda in Python Data Science. Provide implementation details and real-world applications. (Q2)
  • Explain OOP in Python in Python Data Science. Provide implementation details and real-world applications. (Q3)
  • Explain Exception Handling in Python Data Science. Provide implementation details and real-world applications. (Q4)
  • Explain NumPy Arrays in Python Data Science. Provide implementation details and real-world applications. (Q5)
View All 70 Questions →
Medium 80 Questions

Experienced / Mid-Level

  • Explain Neural Networks in Python Data Science. Provide implementation details and real-world applications. (Q71)
  • Explain Backpropagation in Python Data Science. Provide implementation details and real-world applications. (Q72)
  • Explain TensorFlow Basics in Python Data Science. Provide implementation details and real-world applications. (Q73)
  • Explain Keras Model Building in Python Data Science. Provide implementation details and real-world applications. (Q74)
  • Explain NLP with Python in Python Data Science. Provide implementation details and real-world applications. (Q75)
View All 80 Questions →
Hard 70 Questions

Senior / Lead Level

  • Explain Neural Networks in Python Data Science. Provide implementation details and real-world applications. (Q151)
  • Explain Backpropagation in Python Data Science. Provide implementation details and real-world applications. (Q152)
  • Explain TensorFlow Basics in Python Data Science. Provide implementation details and real-world applications. (Q153)
  • Explain Keras Model Building in Python Data Science. Provide implementation details and real-world applications. (Q154)
  • Explain NLP with Python in Python Data Science. Provide implementation details and real-world applications. (Q155)
View All 70 Questions →

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