Hi-Tech Institution | Software Training Institute Chennai Pondicherry

data science TRAINING IN puducherry & chennai

Data science Training, AI Developer Training, Industry-Ready Data science Professional Training

INTRO TO data science

Data Science is one of the most in-demand fields today, helping organizations turn raw data into meaningful insights and smarter decisions. It combines statistics, programming, data analysis, and machine learning to solve real-world business problems. Data Science is widely used across industries such as healthcare, finance, banking, retail, e-commerce, marketing, manufacturing, and government sectors. From predicting customer behavior to detecting fraud and optimizing operations, Data Science plays a critical role in modern decision-making. Learn Data Science from the best Data Science Training Institute in Puducherry with real-time, project-based training aligned with current industry needs.

Data Science focuses on collecting, cleaning, analyzing, and visualizing data to uncover hidden patterns and trends. The Data Science ecosystem includes data preprocessing, exploratory data analysis (EDA), statistics, data visualization, machine learning, and big data concepts. Using powerful tools like Python, SQL, Excel, Pandas, NumPy, Matplotlib, Seaborn, and industry-standard machine learning libraries, learners gain hands-on experience working with real-world datasets. With practical projects, case studies, and end-to-end data workflows, students develop the skills required to build data-driven solutions and succeed as Data Analysts and Data Scientists in today’s competitive job market.

By applying Data Science techniques, enterprises can harness big data, statistical analysis, data visualization, and predictive modeling to automate decision-making, enhance operational efficiency, and drive scalable business growth.

data science Learning Course – 30 Sessions Curriculum

Session 1: Introduction to Data Science

What is Data Science?
Data Science vs AI vs Machine Learning
Data Science lifecycle
Real-world Data Science applications
Career paths & job roles in Data Science

Session 2: Python Basics for Data Science

Python installation & environment setup
Variables & data types
Operators
Conditional statements
Loops

Session 3: Python Data Structures

Functions & modules
Lists, Tuples
Dictionaries & Sets
Practical coding exercises

Session 4: Functions & Modules

User-defined functions
Lambda functions
Built-in functions
Importing modules
Error handling

Session 5: File Handling & Exception Handling

Reading & writing files
CSV & text files
Try–except blocks
Handling runtime errors

Session 6: Introduction to NumPy

NumPy arrays
Array operations
Indexing & slicing
Mathematical functions
Performance advantages

Session 7: Advanced NumPy

Broadcasting
Array reshaping
Statistical functions
Random number generation
Practical exercises

Session 8: Introduction to Pandas

Pandas Series & DataFrames
Loading datasets (CSV, Excel)
Basic operations
Data inspection

Session 9: Data Cleaning & Preprocessing

Handling missing values
Removing duplicates
Data type conversion
Outlier detection

Session 10: Exploratory Data Analysis (EDA)

Descriptive statistics
Data distributions
Correlation analysis
Insights from data

Session 11: Data Visualization – Matplotlib

Line plots
Bar charts
Scatter plots
Histograms
Plot customization

Session 12: Data Visualization – Seaborn

Statistical plots
Heatmaps
Pair plots
Distribution plots
Real dataset visualization

Session 13: SQL for Data Science

Introduction to databases
SELECT, WHERE, ORDER BY
Filtering data
Aggregate functions

Session 14: Advanced SQL

Joins (INNER, LEFT, RIGHT)
Subqueries
Group By & Having
SQL case studies

Session 15: Statistics for Data Science

Mean, median, mode
Variance & standard deviation
Probability basics
Data distributions

Session 16: Inferential Statistics

Hypothesis testing
Confidence intervals
Normal distribution
Z-test & T-test

Session 17: Introduction to Machine Learning

What is Machine Learning?
Supervised vs Unsupervised learning
ML workflow
Use cases

Session 18: Linear Regression

Regression concepts
Simple & multiple linear regression
Model training
Prediction

Session 19: Logistic Regression

Classification concepts
Sigmoid function
Binary classification
Model evaluation

Session 20: Model Evaluation Metrics

Accuracy
Precision & recall
F1-score
Confusion matrix

Session 21: Decision Trees

Tree structure
Splitting criteria
Overfitting & pruning
Hands-on practice

Session 22: Random Forest

Ensemble learning
Random forest algorithm
Feature importance
Model comparison

Session 23: Unsupervised Learning – K-Means

Clustering concepts
K-Means algorithm
Elbow method
Practical implementation

Session 24: Feature Engineering

Feature selection
Feature scaling
Encoding categorical variables
Data transformation techniques

Session 25: Introduction to Time Series

Time series data concepts
Trend & seasonality
Time-based features
Basic forecasting

Session 26: Introduction to NLP

Text data preprocessing
Tokenization
Stop words removal
Text vectorization

Session 27: Mini Project – Data Analysis

Dataset selection
Data cleaning
EDA & visualization
Insight generation

Session 28: Mini Project – Machine Learning

Model selection
Training & testing
Performance evaluation
Result interpretation

Session 29: Deployment & Reporting

Model saving
Basic deployment overview
Report writing
Presentation techniques

Session 30: Final Project & Career Guidance

End-to-end Data Science project
Resume & portfolio guidance
Interview preparation
Real-time industry expectations

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Want more information about Datascience?

datascience FAQ

What is Artificial Intelligence (AI) and Machine Learning (ML)?

Artificial Intelligence is the simulation of human intelligence in machines that can think, learn, and make decisions. Machine Learning is a subset of AI that enables systems to automatically learn and improve from data without being explicitly programmed.

Who can enroll in the AI & ML training course?

This course is suitable for students, fresh graduates, working professionals, software developers, data analysts, and anyone interested in building a career in AI and Machine Learning. Basic knowledge of programming and mathematics is helpful but not mandatory.

What are the prerequisites for learning AI & ML?

Basic understanding of Python programming, statistics, and mathematics (linear algebra & probability) is recommended. However, our training starts from fundamentals, making it beginner-friendly.

What topics are covered in the AI & ML course?

The course covers Python for AI, data preprocessing, statistics, Machine Learning algorithms, Deep Learning, Neural Networks, NLP, Computer Vision, real-time projects, and deployment techniques.

Is this AI & ML course practical or theoretical?

This is a 100% practical, project-based training. Students work on real-world datasets, live use cases, hands-on labs, and industry-relevant projects.

Will I get real-time project experience?

Yes. You will work on multiple real-time projects such as predictive analytics, recommendation systems, image classification, chatbots, and sentiment analysis.

What tools and technologies are used in this course?

You will learn Python, NumPy, Pandas, Scikit-learn, TensorFlow, Keras, PyTorch, Jupyter Notebook, OpenCV, NLP libraries, and deployment tools.

Do you provide placement assistance?

Yes, we provide 100% placement assistance, including resume preparation, interview training, mock interviews, and job referrals.

Will I get a certification after course completion?

Yes, you will receive an industry-recognized AI & ML course completion certificate, which adds value to your resume.

Is online and classroom training available?

Yes, we offer both online and classroom training with flexible batch timings.

About Us

Industry-oriented Data Science training with continuous access to a dedicated student portal, including comprehensive study materials, recorded sessions, real-time datasets, and top MNC interview questions. Get end-to-end guidance for certification preparation, strong subject fundamentals, and complete pre & post training support. Learn from 8+ years of experienced Data Science professionals with extensive hands-on industry exposure. Become interview-ready through practical Data Science projects, case studies, and real-world data analysis. Recognized as one of the leading Data Science training institutes in Chennai and Puducherry, offering an affordable fee structure with an industry-aligned curriculum. Enroll now for the upcoming Data Science batch starting this week.