About Data Science Course
The Data Science course at ITkul is a career-focused program designed to help learners analyze data, build predictive models, and extract meaningful insights using modern tools and techniques.
Data Science Course Objectives
- Build strong foundations in statistics, Python, and data analysis.
- Learn machine learning and real-world data handling.
- Develop job-ready skills through hands-on projects.
- Learn machine learning and real-world data handling.
- Develop job-ready skills through hands-on projects.
Pre-Requisites To Learn Data Science
This course is beginner-friendly.
Basic programming or math knowledge is helpful but not mandatory.
A strong interest in data and problem-solving is essential.
Basic programming or math knowledge is helpful but not mandatory.
A strong interest in data and problem-solving is essential.
Top Career Roles after Data Science Course:
• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Intelligence Analyst
• Data Analyst
• Machine Learning Engineer
• Business Intelligence Analyst
Course Outline
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Module 1: Introduction to AI, Data Science & Machine Learning
View Details - • What is Artificial Intelligence
- • What is Data Science & Machine Learning
- • Machine Learning vs Data Science vs AI
- • How companies use Data Science with Python
- • Phases of Data Science projects
- • Role of Python in Analytics
- • Machine Learning workflow
- • Regression vs Classification
- • Features, Labels and Classes
- • Supervised, Semi-Supervised & Unsupervised Learning
- • Cost Functions and Optimizers
- • Overview of Machine Learning techniques
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Module 2: Python Essentials (Core)
View Details - • Anaconda Installation
- • Python Editors & IDEs (Spyder, Jupyter, Anaconda)
- • Jupyter Notebook usage and customization
- • Python Basics and Syntax
- • Python Primitive Data Types
- • Lists, Tuples and Dictionaries
- • Strings and String Methods
- • Operators and Control Structures
- • Loops and Arrays
- • Python User Defined Functions (def keyword)
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Module 3: Importing & Exporting Data using Python
View Details - • Python Packages and Libraries
- • NumPy, SciPy and Pandas overview
- • Importing data from CSV, TXT, Excel and Access
- • Exporting data to various formats
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Module 4: Data Manipulation & Preprocessing
View Details - • Data cleansing using Pandas
- • Sorting and Filtering Data
- • Handling duplicates and missing values
- • Merging, Appending and Subsetting data
- • Data type conversion and formatting
- • Scaling and Normalization
- • Feature Engineering
- • Feature Selection (RFE, Correlation)
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Module 5: Exploratory Data Analysis & Visualization
View Details - • Introduction to Exploratory Data Analysis
- • Descriptive Statistics
- • Frequency Tables and Summarization
- • Univariate Analysis
- • Bivariate Analysis
- • Bar Charts, Pie Charts, Line Charts
- • Histogram, Boxplot, Scatter Plot and Density Plot
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Module 6: Advanced Data Visualization & Mapping
View Details - • Data Visualization Concepts
- • Matplotlib
- • Seaborn
- • Plotly
- • Google Maps Visualization
- • COVID-19 World Map Visualization
- • Time Series Visualization
- • Comparison Graphs
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Module 7: Machine Learning Algorithms – Overview
View Details - • Introduction to Machine Learning Algorithms
- • Types of ML Algorithms
- • Use cases and applications
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Module 8: Linear Regression
View Details - • Regression Problem Analysis
- • Mathematical Model of Regression
- • Gradient Descent Algorithm
- • Use Cases
- • Simple and Multivariate Regression
- • L1 and L2 Regularization
- • Parameters and Hyperparameters
- • Cost Functions and Optimizers
- • R-Squared and Adjusted R-Squared
- • Model Prediction and Accuracy
- • Graphical Representation
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Module 9: Logistic Regression
View Details - • Logistic Regression Assumptions
- • Logit Transformation
- • Hypothesis Function
- • Maximum Likelihood Estimation
- • Odds Ratio Interpretation
- • ROC Curve Analysis
- • Model Specification
- • Threshold Optimization
- • Model Accuracy and Evaluation
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Module 10: Decision Trees, Random Forest & KNN
View Details - • Decision Tree Concepts
- • Entropy, Gini Index and Information Gain
- • CART and C5.0 Algorithms
- • Overfitting and Pruning
- • Random Forest Algorithm
- • K-Nearest Neighbors Algorithm
- • Classification and Regression use cases
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Module 11: Clustering – K-Means
View Details - • Unsupervised Learning
- • Introduction to Clustering
- • K-Means Clustering
- • Centroids and Distance Metrics
- • Handling Clustering Problems
- • Best cluster selection techniques
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Module 12: Artificial Intelligence & Neural Networks
View Details - • Introduction to Artificial Intelligence
- • Neural Network Fundamentals
- • Supervised Learning with Neural Networks
- • Classification and Regression
- • Basics of Statistics
- • Probability Distributions
- • Correlation, Normalization and Scaling
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Module 13: Deep Learning
View Details - • Artificial Neural Networks (ANN)
- • Convolutional Neural Networks (CNN)
- • Recurrent Neural Networks (RNN)
- • Modeling and Training with Datasets
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Module 14: MySQL for Data Science
View Details - • Database Concepts
- • SQL Queries
- • Data Manipulation
- • Data Handling with MySQL
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Module 15: Projects & Practical Training
View Details - • Hands-on Advanced Python Libraries
- • Data Processing Techniques
- • Applying ML Algorithms to Business Problems
- • Performance Benchmarking
- • 10 Real-time Industry Projects
Reviews
Course Content
Outstanding
Puncuality
Outstanding
Trainers
Outstanding
Rohit Verma
The real-time projects at ITKUL made a huge difference. By the time I attended interviews, I already had hands-on experience with Java full-stack development.
Anjali Mehta
I joined ITKUL with zero experience in data analytics. The structured modules and real-time projects boosted my confidence, and now I’m working in a data-driven role.
Smit Mahajan
The ethical hacking and network security labs at ITKUL were amazing. The trainers guided us through real-world scenarios, and I successfully cleared my first job interview