Industry Skills Classroom Training

Data Science

This course enables you to walk out not just with knowledge — but with the confidence, skills, and real project experience to land a Data Scientist role. You will be fully prepared to crack interviews, handle real-world data problems, and deliver results from Day 1 on the job.

4.8 (43 ratings)
65 enrolled 6 months | 4 hours/day Professional Tamil

What You'll Learn

  • Master Python & SQL for Data Science
  • Analyze & Manipulate Data with Confidence
  • Build & Deploy Real-World ML Models
  • Solve Complex Problems with Advanced Algorithms
  • Work on Industry-Based Case Studies & Live Projects
  • Go from Job Seeker to Job-Ready Data Scientist

Requirements

  • Basic Computer Operating Skills
  • No need of previous coding knowledge
  • Suitable for both IT & Non-IT students

Tools & Technologies

PostgreSQL PostgreSQL
Python Python
Pandas Pandas
NumPy NumPy
Matplotlib Matplotlib
Seaborn Seaborn
Scikit-Learn Scikit-Learn
Fast API Fast API
Streamlit Streamlit

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about breaking into the Data Science field. This course is structured to take you from zero to job-ready — giving you the confidence to crack interviews, walk into any company, and start performing from your very first day at work.

Right for You?

Who This Course is For

  • Fresh Graduates Looking to Start Their Career in Data Science
  • Job Seekers Who Want to Build Real Data Science Skills Beyond Online Courses
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready Data Scientist

Course Curriculum

16 modules • 153 lessons
Module 1: Python for Data Scientist 11 lessons
  • Introduction to Python & Environment Setup
  • Python Syntax & Data Types
  • Variables, Operators & Expressions
  • Conditional Statements & Loops
  • Functions & Modules
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object Oriented Programming
  • Python Libraries Overview for Data Science
  • Python Interview Preparation
  • Module 1 Clearance Test
Module 2: SQL for Data Scientist 11 lessons
  • Introduction to Databases & SQL
  • SQL Syntax & Query Structure
  • Filtering & Sorting Data
  • Aggregate Functions & Grouping
  • Join Operations
  • Subqueries & Nested Queries
  • Window Functions
  • Common Table Expressions (CTEs)
  • Data Cleaning & NULL Handling
  • Extracting Analysis-Ready Datasets from SQL
  • Module 2 Clearance Test
Module 3: Statistics & Probability 12 lessons
  • Introduction to Statistics for Data Science
  • Descriptive Statistics - Mean, Median, Mode & Variance
  • Probability Basics & Rules
  • Probability Distributions - Normal, Binomial & Poisson
  • Central Limit Theorem
  • Hypothesis Testing - Z Test & T Test
  • Chi-Square Test & ANOVA
  • Confidence Intervals & P-Values
  • Correlation & Covariance
  • A/B Testing for Business Decisions
  • Bayesian Statistics Basics
  • Module 3 Clearance Test
Module 4: Data Manipulation with Pandas & NumPy 15 lessons
  • Introduction to Pandas & NumPy
  • Series & DataFrame Basics
  • NumPy Array Operations & Statistical Functions
  • Indexing, Selection & Slicing
  • Handling Missing Values & Null Data
  • Handling Duplicates & Inconsistent Data
  • Sorting, Ranking & Replacing Values
  • Grouping & Aggregation
  • Merging, Joining & Concatenating DataFrames
  • Pivot Tables & Cross Tabulation
  • String Operations & Text Cleaning
  • Date & Time Operations
  • Apply, Map & Lambda Functions
  • Window Functions & Rolling Calculations
  • Module 4 Clearance Test
Module 5: Exploratory Data Analysis 10 lessons
  • Introduction to EDA & Its Role in Data Science
  • Data Profiling & Summary Statistics
  • Univariate & Bivariate Analysis
  • Multivariate Analysis
  • Outlier Detection & Treatment
  • Distribution & Skewness Analysis
  • Correlation & Relationship Analysis
  • Automated EDA Tools - AutoViz & Sweetviz
  • EDA Report Generation & Storytelling
  • Module 5 Clearance Test
Module 6: Data Visualization with Matplotlib & Seaborn 10 lessons
  • Data Visualization Principles & Best Practices
  • Choosing the Right Chart for the Right Data
  • Line, Bar & Area Charts with Matplotlib
  • Scatter, Pie & Histogram Charts with Matplotlib
  • Subplots, Customization & Styling in Matplotlib
  • Distribution & Categorical Plots with Seaborn
  • Heatmaps, Pair Plots & Joint Plots with Seaborn
  • Facet Grid & Multi-Plot Grids with Seaborn
  • Storytelling with Data Visualizations
  • Module 6 Clearance Test
Module 7: Feature Engineering with Scikit-Learn 9 lessons
  • Introduction to Feature Engineering
  • Encoding Categorical Variables
  • Feature Scaling & Normalization
  • Feature Selection Techniques
  • Feature Extraction & Dimensionality Reduction - PCA
  • Imbalanced Data Handling - SMOTE & Resampling
  • Scikit-Learn Preprocessing Pipeline
  • Creating Domain-Specific Features
  • Module 7 Clearance Test
Module 8: Supervised Modeling - Regression & Classification 13 lessons
  • Introduction to Supervised Learning
  • Linear & Multiple Linear Regression
  • Polynomial, Ridge & Lasso Regression
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier
  • Decision Tree - Regressor & Classifier
  • Random Forest - Regressor & Classifier
  • Gradient Boosting & XGBoost
  • Model Evaluation - Regression & Classification Metrics
  • Cross Validation, Bias-Variance Tradeoff & Hyperparameter Tuning
  • Module 8 Clearance Test
Module 9: Unsupervised Modeling - Clustering & Recommendation 11 lessons
  • Introduction to Unsupervised Learning
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN Clustering
  • Gaussian Mixture Models
  • Cluster Evaluation - Silhouette Score & Elbow Method
  • Content-Based Filtering
  • Collaborative Filtering
  • Matrix Factorization & Hybrid Recommendation Systems
  • Real-World Clustering & Recommendation Use Cases
  • Module 9 Clearance Test
Module 10: NLP & LLM Foundation 12 lessons
  • Introduction to Natural Language Processing
  • Text Preprocessing & Cleaning
  • Tokenization, Stemming & Lemmatization
  • Bag of Words & TF-IDF
  • Word Embeddings - Word2Vec & GloVe
  • Sentiment Analysis
  • Text Classification
  • Named Entity Recognition (NER)
  • Introduction to Large Language Models (LLMs)
  • Prompt Engineering Basics
  • Using Pre-trained LLMs via API
  • Module 10 Clearance Test
Module 11: Time Series Algorithms 9 lessons
  • Introduction to Time Series Analysis
  • Time Series Components & Decomposition
  • Stationarity & Differencing
  • ARIMA & SARIMA Models
  • Exponential Smoothing Methods
  • Prophet Model
  • LSTM for Time Series Forecasting
  • Model Evaluation for Time Series
  • Module 11 Clearance Test
Module 12: Building Data Transformation & Inference Pipeline 7 lessons
  • Introduction to ML Pipelines
  • Building End-to-End Preprocessing Pipeline with Scikit-Learn
  • Building Inference Pipeline for Predictions
  • Model Saving & Loading - Pickle & Joblib
  • Automating Data Transformation Workflows
  • Pipeline Testing & Validation
  • Module 12 Clearance Test
Module 13: Model Deployment with FastAPI & Streamlit 8 lessons
  • Introduction to Model Deployment
  • Building ML API with FastAPI
  • Testing & Documenting FastAPI Endpoints
  • Building Interactive Data Science App with Streamlit
  • Connecting Streamlit to ML Models & SQL
  • Deploying Streamlit Application
  • Introduction to Docker for Deployment
  • Module 13 Clearance Test
Module 14: Model Monitoring & Performance 6 lessons
  • Introduction to Model Monitoring
  • Data Drift & Concept Drift Detection
  • Model Performance Tracking & Logging
  • Retraining Strategies & Model Versioning
  • Introduction to MLflow for Experiment Tracking
  • Module 14 Clearance Test
Module 15: Version Control & ML Project Structure 7 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • Data Science Project Folder Structure Best Practices
  • Writing README & Documenting DS Projects
  • Building a GitHub Portfolio for Data Science
  • Module 15 Clearance Test
Module 16: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • End-to-End Data Science Training, Not Just Theory
  • Classroom Training with Real Mentor Guidance
  • Build a Portfolio That Gets You Hired

Your Instructors

Leelavathi Ramesh

Leelavathi Ramesh

Business Intelligence Analyst

9 Courses

4+ years of experience as a Business Intelligence Analyst, with hands-on expertise at Variablz Technology and Schneider Electric. Statistics graduate with practical experience across Marketing, Production, and Sales Analytics.

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

17 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

Course Outcomes

What Our Students Build

Articles, projects, research papers and more — real work by our learners applied beyond the classroom.

View All Outcomes
FAQ

Frequently Asked Questions

Is this course suitable for freshers with no Data Science experience?

Yes. This course starts from the basics and gradually takes you to advanced Data Science concepts u2014 no prior experience is required to join.

What are the eligibility criteria to join this course?

Any graduate from any degree is eligible, provided their degree includes Mathematics as a subject.

Is this course suitable for college students or working professionals?

No. This course is exclusively designed for job seekers who are seriously looking to start their career as a Data Scientist.

Is this an online or offline course?

This is a completely classroom-based training program conducted at our Cuddalore location.

Will I work on real projects during this course?

Yes. You will work on a Mentored Project with trainer guidance and an independent Capstone Project u2014 both built around real-world business problems.

Will this course help me crack Data Science interviews?

Absolutely. Every module is structured around what recruiters and companies actually test u2014 from statistics and ML algorithms to model deployment and GitHub portfolio building.

What kind of companies can I apply to after this course?

You will be job-ready for service-based companies, product startups, MNCs, and analytics firms across Tamil Nadu and South India hiring for Data Scientist, Junior Data Scientist, ML Analyst, and AI Engineer roles.

Do I need to know programming before joining?

No. Python is covered from scratch in the first module u2014 you will be guided step by step from the basics to advanced Data Science coding.

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