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Data Science Certification Course in Chennai with AI & ChatGPT
- Earn Industry-recognized certification
- Build real-time projects with industry-aligned tools
- Live Interactive sessions from industry veterans
- Updated curriculum designed for AI-era
- Dedicated Job Assistance and Resume Building
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Success Stories – They Believed, Learned & Achieved!
Success Isn’t Luck – It’s Learning. They Chose to Upskill – Now They’re in the World’s Best Companies!
“Transitioning into analytics felt challenging at first, but steady learning and hands-on work made the shift smooth and achievable.”
Veeresh Kottargi 
“Focusing on real-world tasks made learning clearer for me — that practical approach shaped my career direction.”
Aman Bind
“I stopped doubting my potential the day I started building projects — that’s when everything began to change for me.”
Avina Garg
“I spent time understanding data workflows in depth, and that clarity helped me grow steadily in my career.”
Aniket Singh
“Every phase of my journey taught me one thing — when you keep upgrading yourself, new leadership opportunities naturally open up.”
Aditya Agrawal
“I focused on building practical skills and kept improving a little every day — that consistency made all the difference in my journey.”
Gopi Kiran
Our learners are working in leading organizations
Gain industry-ready skills and earn an official certificate from DataFlair.
Online Data Science Course in Chennai Curriculum
Python
Stats
Data Science and ML with AI
1. Python Introduction
- What is Python?
- Why Python is used in Data Science and AI
- Features of Python
- Python applications in real life
- History and evolution of Python
- Python limitations
2. Writing your first Program in Python
- Installing Python
- Setting up Python
- IDLE introduction
- Understanding Python execution flow
- Writing and running the first program
3. Python Basics
- Python syntax
- Indentation in Python
- Comments in Python
- Variables and constants
- Identifiers and naming rules
- Data Types in Python
- Type checking
- Type conversion
4. Python Input and Output
- Input and Output Functions
- Reading Data in a Single Line
- Using the print() Function in Python
- Python Formatted Print Statements
- Replacement Operator
- Printing with the format() function
5. Operators in Python
- Arithmetic operators
- Assignment operators
- Comparison operators
- Logical operators
- Membership operators
- Identity operators
- Bitwise operators
- Operator precedence
- Practice programs using operators
6. Python Loops & Control Statements
- if statement
- if-else statement
- if-elif-else ladder
- Nested if conditions
- Decision-making programs
- Real-life examples using conditions
- Loops in Python
- for loop, while loop
- Nested loops
- break & continue statement in Python
- Python pass statement
7. Python String
- Creating strings
- Accessing string characters
- String indexing, slicing
- String methods
- String formatting
- Splitting and joining strings
8. Python Collections
- Understanding Python Collections
- Types of Collections: Lists, Tuples, Sets, and Frozen Sets
- Dictionaries in Python
- Understanding Bytes and Bytearray Collections
9. Functions in Python
- What is a function?
- Types of functions
- Creating user-defined functions
- Function arguments
- Return statement
- Default arguments
- Keyword arguments
- Variable-length arguments
- Returning Values from Python Functions
- Local and global variables
- eval() Function in Python
10. Recursion using Python
- What are Recursive Functions
- Factorial with Recursion
- Reversing Numbers using Recursion
- Fibonacci Series using Recursion
11. Lambda Functions in Python
- Anonymous Functions using lambda
- filter() Function with lambda
- map() Function with lambda
12. Python Arrays
- Introduction of Arrays
- Arrays Operations in Python
- Types of Arrays
- Using Array Methods in Python
- Array creation with NumPy
- Comparison of Arrays in Python
- Object References in Python
- View Vs Copy
- NumPy Array Dimensions and Attributes
- Multidimensional Arrays in Python
- Using Matrices in Python
13. Search and Sort in Python
- Implementation of Binary Search
- Bubble Sort Algorithm using Python
14. Object-Oriented Programming
- Introduction to OOP
- Classes and objects
- self keyword
- Instance & Class variables
- Instance, Class and Static methods
- __init__() Method in Python
- Constructor Overloading
- Polymorphism and Operator Overloading in Python
- Relational Operator Overloading in Python
- Overloading & Overriding in Python
- Understanding Getters and Setters
15. Advance Object-Oriented Programming
- Understanding Static Variables and Methods
- Inner Classes in Python
- Difference between Is-A & Has-A Relationships
- Inheritance and Use of super() Method
- Types of Inheritance in Python
- Role of Python Constructors in Multiple Inheritance
- Abstract Classes and Methods in Python
- Interfaces in Python
16. Python Exception Handling
- What are errors?
- What are exceptions?
- try and except block
- Handling multiple exceptions
- finally block in Python
- Custom exceptions
- Assertions in Python
- Raising Exceptions
17. File Handling
- Opening and closing files
- Reading files
- Writing files
- Appending data
- Using File modes
- File Exception Handling
- Working with text files
- Working with CSV files
- Using readlines() and writelines() Methods
- Binary file handling
- Pickle module
- seek() and tell() method
- Practical file handling programs
18. Libraries in Python
- Concepts of NumPy Arrays
- Creating Arrays using NumPy
- Comparison of Arrays in NumPy
- Arithmetic Operations on Arrays
- Data Analysis using Pandas
- DataFrames in Pandas
- Inserting, Deleting, and Updating Data in Pandas
- Creating Graphs with Matplotlib
- Using Pie Charts, Bar Graphs, Scatter Plots, Histograms, etc.
- Creating Plot using Seaborn
- Working with Bar, Histogram, Scatter and Heatmap Plots.
1. Basics of Statistics
- What is statistics?
- Why Data Scientists need statistics
- Statistics in business decisions
- Difference between Descriptive vs. Inferential
- Qualitative vs. Quantitative
- Difference between Populations vs. Samples
2. Descriptive Statistics
- Understanding Mean, Median & Mode
- Concept of Range, Variance & Standard Deviation
- Coefficient of Variation
3. Data Distribution
- What is data distribution?
- Skewness
- Kurtosis
- Concept of Normal Distribution
- Using Visualization Techniques
4. Using Graphs and Plots in Statistics
- Histograms
- Box Plots
- Bar & Pie Charts
- Scatter Plots
- Correlation in Statistics
5. Probability
- What is probability?
- Probability axioms
- Conditional probability
- Understanding Bayes’ theorem
- Practical Application of Bayes theorem
6. Probability Distribution
- Random & Discrete Variables
- Understanding Probability Distributions: PMF, PDF and CDF
- Using Binomial, Poisson, Exponential, Uniform, Log-Normal
7. Statistical Inference
- What are Sampling
- Understanding Sampling Distributions
- Implementing Hypothesis Testing
- What is Null and Alternative Hypotheses
- Type I and Type II Errors
- Understanding p-Value and Significance Levels
- Implementation of Z-test, T-test, Chi-Square Test, and ANOVA
- Confidence Intervals
- Margin of Error
8. Regression
- Introduction of Regression
- Types of regression
- What is Root Mean Square Error
- Understanding the K-Nearest Neighbor Algorithm
- Using Support Vector Machine
- Random Forest Algorithm
- Clustering in regression
1. Introduction to Data Science
- What is Data Science?
- Why Data Science is important
- Important terminologies in Data Science
- Data Science vs Data Analytics
- Data Science vs Machine Learning
- Data Science vs Artificial Intelligence
- Data Science project lifecycle
- Skills required for Data Science
2. Career role in Data Science
- Demand for Data Science
- Data Science Career Opportunities
- Building a career in Data Science
- Skill Set required to become a Data Scientist
- Roadmap for AI & Data Science
- Salary Trends & Career Growth in Data Science
- Data Science Future
3. Data Science in detail
- Real-World Use Cases of Data Science
- Data Preparation for Analysis
- Machine Learning Model Building
- Predictions using the Model
- Integrating Business Value with Data Science
- Workflow of Data Science Project
- Different Stages of a Data Science Project
- Roles in Data Science: Data Engineer, Data Scientist, ML Engineer
4. Classification of Analytics
- Types of Analytics
- Understanding Descriptive Analytics
- What is Diagnostic Analytics
- Using Predictive Analytics
- Implementing Prescriptive Analytics
5. Industry Applications of Data Science
- Data Science in Finance and Banking
- Data Science in Retail
- Data Science in Healthcare
- Data Science in Logistics and Supply Chain
- Data Science in the Technology Industry
- Data Science in Manufacturing
- Data Science in Agriculture
6. Exploratory Data Analysis
- What is EDA?
- Why EDA is important
- Numerical & Categorical Data
- Continuous & Discrete Data
- Feature Engineering in ML
- Handling of Missing Values
- Handling Outliers in ML
- Univariate, Bivariate & Multivariate Analysis
- Correlation in ML
- Essentials of Data Cleaning
- Creating Business Insights from EDA
7. Machine Learning Introduction
- What is Machine Learning?
- Why Machine Learning is used
- How machines learn from data
- Features and target variable
- Types of Machine Learning
- Training data and testing data
- Important Terminologies in ML
- Overfitting vs. Underfitting in ML
- Workflow of Machine Learning
- Important Machine Learning Algorithms
- AI vs ML vs Data Science
- AI & ML Case Studies across Industry
- AI & ML Applications in the Real World
- Reinforcement Learning in ML
- Understanding the Architecture of a Data Science Project
- Data Science & Data Engineering Flow
- Data Platform Strategy in ML
- Architecture of Data Ecosystem Project
8. Supervised Learning – Regression
- What is Regression?
- Types of Regression
- Indtroduction to Linear Regression
- Understanding Evaluation Metrics
- Challenges while using Regression
- Linear vs Polynomial vs Ridge vs Lasso Regression
- Applications of Regression Industry
- Real-World Use Cases of Regression
9. Classification Algorithms
- What is Classification?
- Difference between Regression & Classification
- Real-World Applications of Classification
- Types of Classification Problems
- Preparing Data for Classification
- Label Encoding & One-Hot Encoding
- Understanding Feature Scaling
- Train-Test Split data in ML
- Handling Imbalanced Data using Classification
- Understanding Model Evaluation Metrics in Classification
- Accuracy, Precision, Recall, and F1-Score in Classification
- Implementation of the Confusion Matrix in Classification
- Understanding ROC Curve and AUC Score in Classification
- Overfitting & Underfitting
- Regularization Techniques for Classification
- What is Classification?
- Difference between Regression & Classification
- Real-World Applications of Classification
- Types of Classification Problems
- Preparing Data for Classification
- Label Encoding & One-Hot Encoding
- Understanding Feature Scaling
- Train-Test Split data in ML
- Handling Imbalanced Data using Classification
- Understanding Model Evaluation Metrics in Classification
- Accuracy, Precision, Recall, and F1-Score in Classification
- Implementation of the Confusion Matrix in Classification
- Understanding ROC Curve and AUC Score in Classification
- Overfitting & Underfitting
- Regularization Techniques for Classification
10. K-Nearest Neighbors
- KNN concept
- How KNN works
- Understanding the right value of ‘K’ in KNN
- Preparing data for KNN
- Handling of categorical features in KNN
- Benefits & limitations of KNN
- Evaluating Metrics in KNN
- Model Tuning using KNN
- Cross-Validation in KNN
11. Decision Tree
- Decision Tree concept
- Key Components of a Decision Tree
- Splitting data
- Gini impurity
- Entropy
- Information gain
- Advantages & Disadvantages of Decision Tree
- Pre-Pruning and Post-Pruning
- Evaluation Metrics in Decision Tree
- Hyperparameter Tuning in Decision Tree
- Cross-Validation in Decision Tree
12. Random Forest
- What is Random Forest?
- Why use Random Forest over a single tree?
- Key Concepts of Random Forest
- Working of Random Forest
- Hyperparameters in Random Forest
- Advantages and Limitations in Random Forest
- Use Cases of Random Forest
13. Unsupervised Learning
- What is unsupervised learning?
- Difference between Supervised & unsupervised learning
- Use of Unsupervised learning
- Unsupervised learning Application
- Algorithms in Unsupervised Learning
- Use cases of Unsupervised learning
- Evaluation Metrics in Unsupervised learning
14. Unsupervised Learning – Clustering
- Understanding Clustering Algorithms
- Introduction to Clustering
- Difference between Classification & Clustering
- K-means Clustering in ML
- Real-world industry use cases of K-means Clustering
- Understanding the elbow method in K-means Clustering
- Using Hierarchical Clustering
- Agglomerative vs Divisive Approach
- Real-world industry use cases of Hierarchical Clustering
- Implementation of Dimensionality Reduction
15. Machine Learning in Business
- ML in Enterprise Applications
- Predictive Modeling & Segmentation using ML
- ML Personalization Engines
- Time Series & Anomaly Detection in ML
16. Scikit-Learn
- Introduction of Scikit-Learn and its Importance
- Features of Scikit-Learn
- Installation of Scikit-Learn
- Setting Up the Environment
- Workflow of Machine Learning with Scikit-Learn
17. Natural Language Processing
- Introduction to Natural Language Processing?
- NLP Applications
- Text Preprocessing using NLP
- Text Classification using NLP
- Sentiment Analysis in NLP
18. Deep Dive in AI
- Introduction to AI
- Understanding Gen AI
19. Deep Learning
- What is Deep Learning?
- Machine Learning vs Deep Learning
- Neural network basics
- Artificial neuron
- Input layer
- Hidden layer
- Output layer
- Activation functions
- Loss function
- Gradient descent
- Backpropagation
- Optimizers
- Deep Learning applications
- ResNet50
- Vanishing gradients
- Transfer Learning
- DenseNet121
- RNN in deep learning
- ANN vs CNN vs RNN
- LSTM in deep learning
- RNN vs LSTM
- LSTM in deep learning
20. Deep Learning Frameworks
- Understanding TensorFlow
- Understanding with Keras
- Understanding PyTorch
- Difference between PyTorch & TensorFlow
- Key Features of PyTorch
- Use Cases of PyTorch
21. Types of Neural Networks
- Fully connected networks
- Convolutional Neural Networks in ML
- Recurrent Neural Networks in ML
22. Prompt Engineering
Module 1: Introduction to Prompt Engineering
- Understanding Prompt Engineering
- Working with AI models like GPT and DALL·E.
- Applications in industries
Module 2: Fundamentals of Prompt Designing
- Types of prompts: Instruction-based, Conversational & Contextual prompts
- Main Components of effective prompts: Clarity, specificity, and relevance.
Module 3: Challenges in Prompt Engineering
- Limitations of AI models.
- Ethical considerations while designing and using pro
23. Generative AI
Module 1: Generative AI Basics
- Introduction to Generative AI
- Traditional AI and Generative AI.
- Applications of GenAI:
- Chatbots and conversational AI.
- Image generation (e.g., DALL·E).
- Video synthesis and editing using AI.
Module 2: Generative AI Fundamentals
- Generative Models:
- How Generative Models create new data.
- Types of Generative AI:
- GANs (Generative Adversarial Networks).
- VAEs (Variational Autoencoders).
- Transformers (foundation of GPT, DALL·E, and other tools).
Module 3: Tools and Frameworks for GenAI
- Deep learning frameworks
- Essential APIs and tools
- Working with pre-trained models to simplify tasks
24. Large Language Models (LLMs)
- Introduction of Large Language Models
- Definition and important features.
- Examples: ChatGPT, Bard, Claude, GPT-4.
- Why are LLMs important in AI and Data Science?
- Role of LLMs in modern AI applications.
- Real-world use in various industries.
- Foundations of LLMs
- Working with LLMs
- Understanding transformer architecture.
- Attention mechanism overview
- Dive into the Tokenization process.
- Training the Large Language Models
- Data preprocessing
- Understanding Training datasets (Common Crawl, Wikipedia).
- Real World Applications of LLMs
- Task of Natural Language Processing
- Use Cases of LLMs across the industry
- Healthcare, education, and finance.
- Research and scientific discoveries.
25. Agentic AI & AI Agents
- What is Agentic AI?
- Evolution of Agentic AI: From reactive systems to proactive agents
- Agentic AI Working
- Agentic AI Applications
- Agent vs. Model vs. Tool: Understanding the key difference
- Distinction between Agentic AI & AI Agents
- Understanding Multi-Agent Systems
- Key characteristics of AI agents: autonomy, proactivity, adaptability
- Use cases of AI agents
- Understanding the Architecture of an Agent
- Open-source agent framework
26. Data Engineering
- Basics of Data Engineering
- Big Data Essentials
- Big Data Vs Data Science
- Key components of Big Data
- Five Vs
- Importance of Big Data
- History of Big Data
- Big Data role in Business
- Use Cases of Big Data in Industries
27. Fundamentals of Hadoop
- Hadoop Introduction
- Hadoop Installation
- Knowing the Architecture of Hadoop
- Understanding Hadoop Ecosystem: Hive, Pig, HBase, and Sqoop
- Working on HDFS and MapReduce
- Understanding Setting Up and Navigating HDFS
- Essential HDFS Commands for File Management
- MapReduce Working
28. PySpark
- Spark Introduction
- What are RDDs?
- Implementation of Data Processing with PySpark
- Understanding Spark SQL
Course Objectives
Why
What
Prerequisites
Who
Course Benefits
Jobs
Companies
Objectives of the Data Science Course Chennai
- Learners can collect, analyse, and interpret complex datasets to extract meaningful insights.
- Students are taught statistical techniques and visualization tools essential for interpreting data and effectively communicating findings.
- Become proficient in programming languages that are important for data manipulation and analysis, like Python and R.
- To create models for classification, regression, clustering, and recommendation systems, combine your knowledge of machine learning algorithms and predictive modelling.
- Prepares students for getting employed by providing them with the skills and knowledge to handle innovation and solve real-world problems using data.
Why should you learn Data Science?
Learning Data Science is highly valuable due to the huge demand for skills that convert raw data into actionable and meaningful insights, leading to high-paying, versatile career opportunities across industries like technology, healthcare, and finance. It drives innovation, enables better decision-making, and allows for solving complex, real-world problems.
What is Data Science?
Data science is the field of study that uses scientific methods, algorithms, and programming to gain knowledge and insights from data. It’s the process of collecting, analysing, and interpreting large amounts of data to find hidden patterns and trends. Data scientists build questions around specific data sets and then use tools such as data analytics, machine learning, and artificial intelligence (AI) to find patterns, create predictive models, and develop insights that guide business decision-making.
What to do before you begin a Data Scientist Course in Chennai?
Having prior knowledge about the following will help you learn at a good pace during this Data Science training in Chennai:
- A basic knowledge of mathematics and statistics is required, as these concepts are fundamental to machine learning and data analysis.
- Applicants should have a basic understanding of any programming language, which will help them quickly grasp Python.
- A curious mind and a desire to learn about data science will help make this course interesting.
Who should choose this Data Scientist training in Chennai?
This best Data Science course in Chennai is beneficial for a wide range of individuals who are willing to work with data and get meaningful insights.
- IT Graduates
- Aspiring Analysts
- Statistical Research Enthusiasts
- Data Engineers
- Aspiring Economists
- Business Owners and Entrepreneurs
- Marketers
You can get the following benefits by enrolling in our online Data Science Institute in Chennai:
- Unlock doors of immense career opportunities with industries and companies looking for data experts.
- Get along with real-world projects that bring practical experience and build a professional portfolio.
- Increase your efficiency by becoming capable of handling data and interpreting it.
- Gain expertise in widely used software and programming languages like Python.
- Learn to solve business-related problems by implementing data techniques.
- Boost your earnings and increase your value in the job market with Data Science skills.
Job opportunities after this Data Science Course in Chennai
Gain the skills to acquire various job roles, some of which are mentioned here:
- Data Consultant
- AI/ML Researcher
- Data Product Manager
- Healthcare Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer
- Quantitative Analyst (Quant)
Our students are working in leading organizations
Data Science Online Training in Chennai FAQs
Why is data science important?
Data science is the field of study that uses scientific methods, algorithms, and programming to gain knowledge and insights from data. It is important because it allows businesses to make informed decisions based on data, solve complex problems, and gain a competitive advantage.
Who should consider enrolling in the data scientist course Chennai?
This online Data Science course Chennai can benefit anyone interested in working with data, including students, professionals from various industries like IT, finance, and healthcare, and those willing to switch careers.
What fundamental skills are covered in this Data Scientist training in Chennai?
The Data Science course Chennai covers data analysis, visualization, statistical analysis, machine learning, programming (primarily in Python), and domain knowledge. It also focuses on critical thinking and problem-solving skills.
How long does an average Data Science course last?
The online data science training in Chennai is designed to be self-paced. You can complete the course according to your schedule because of the flexibility & feature provided in this course.
Do I need programming or math experience to enroll?
Even though it is not compulsory to have programming or math experience, having these skills will help you in smooth learning.
What career opportunities can I pursue after learning Data Science training in Chennai?
You can choose from several jobs, such as becoming a data scientist, machine learning engineer, business intelligence analyst, and many more. This best data science course in Chennai unlocks various fields and exciting job roles.
Does the course include real-world projects?
Yes, this online data scientist course in Chennai includes real-world projects with hands-on training and practical exercises to make you ready for jobs. These projects will help build a strong portfolio and attract potential employers.
What prerequisites are needed before enrolling?
This Data Science training in Chennai is designed for beginners, so that you can learn from the basics and build foundational skills. But having some knowledge about programming and basic math, like algebra and statistics, will be helpful.
Is the schedule suitable for working professionals?
Yes, the Data Science course is suitable for working professionals, and the recorded classes provide the flexibility to manage the course while working.
Is this course suitable for beginners from non-technical backgrounds?
Students from non-technical background can also opt for this course as this course starts from the basic concepts to the advanced concepts.
