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
- • Applications of Data Science in industries
- • Phases of Data Science projects
- • Role of Python in Data Analytics
- • Machine Learning workflow
- • Regression vs Classification
- • Features, Labels and Classes
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Module 2: Python Essentials (Core)
View Details - • Anaconda Installation
- • Python IDEs (Jupyter Notebook, Spyder)
- • Python Basics and Syntax
- • Data Types (int, float, string, boolean)
- • Lists, Tuples, Dictionaries
- • Strings and String Methods
- • Operators and Control Structures
- • Loops and Functions
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Module 3: Data Handling with Python
View Details - • Introduction to NumPy
- • Introduction to Pandas
- • Importing data (CSV, Excel, TXT)
- • Exporting data to various formats
- • Basic data operations using Pandas
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Module 4: Data Preprocessing
View Details - • Data cleaning techniques
- • Handling missing values
- • Removing duplicates
- • Data transformation and normalization
- • Merging and joining datasets
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Module 5: Exploratory Data Analysis (EDA)
View Details - • Descriptive statistics
- • Data summarization techniques
- • Frequency distribution
- • Identifying patterns and trends
- • Correlation and covariance
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Module 6: Data Visualization
View Details - • Visualization basics
- • Matplotlib library
- • Seaborn library
- • Bar, Line, Pie charts
- • Histogram, Boxplot, Scatter plot
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Module 7: Machine Learning Fundamentals
View Details - • Introduction to Machine Learning
- • Types of Machine Learning (Supervised, Unsupervised)
- • Model training and evaluation
- • Overfitting and underfitting
- • Train-test split
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Module 8: Linear Regression
View Details - • Simple and multiple regression
- • Regression model concepts
- • Gradient descent basics
- • Model evaluation metrics (R-squared)
- • Assumptions of linear regression
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Module 9: Logistic Regression
View Details - • Classification problems
- • Logistic function and sigmoid curve
- • Model training and prediction
- • Confusion matrix
- • ROC and AUC curve
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Module 10: Decision Trees, Random Forest & KNN
View Details - • Decision tree algorithm
- • Gini index and entropy
- • Random forest ensemble method
- • K-Nearest Neighbors (KNN)
- • Model comparison
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Module 11: Clustering (K-Means)
View Details - • Unsupervised learning basics
- • K-Means clustering algorithm
- • Centroids and distance measures
- • Cluster evaluation techniques
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Module 12: AI & Neural Networks
View Details - • Introduction to neural networks
- • Perceptron model
- • Basics of deep learning
- • Activation functions
- • Applications of AI
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Module 13: SQL for Data Science
View Details - • Database concepts
- • Basic SQL queries (SELECT, WHERE, JOIN)
- • Data manipulation (INSERT, UPDATE, DELETE)
- • Using SQL with datasets
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Module 14: Projects & Practical Training
View Details - • Real-world datasets handling
- • End-to-end Data Science project
- • Applying ML models to business problems
- • Model deployment basics
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