Beginner-Friendly Curriculum
You don't need to be an expert programmer to begin. The curriculum starts with programming, statistics, databases, and data fundamentals before progressing into machine learning and advanced topics.
Practical Data Science Learning
Build Data-Driven Skills with a Practical Data Science Course in Chennai
Learn how to transform raw data into meaningful insights through a practical, industry-focused Data Science Course in Chennai. Build skills in Python, statistics, SQL, data visualization, machine learning, AI, and real-world data analysis through hands-on projects.
Whether you are a beginner, working professional, graduate, or aspiring data analyst, this program helps you progress from fundamental concepts to advanced data science techniques.
Learn. Analyse. Build. Solve real-world problems with data.
- Beginner to Advanced Learning Path
- Python, SQL, Statistics & Machine Learning
- Hands-on Industry-Relevant Projects
- AI & Machine Learning Concepts
- Practical Data Visualization
- 2+ Certifications
- Flexible Learning Options
- Placement Support
Upcoming Data Science Course Batches in Chennai
Choose a learning schedule that fits your professional or academic commitments.
| Batch Type | Suitable For |
|---|---|
|
Weekday Batch
|
Students and professionals with flexible schedules |
|
Weekend Batch
|
Working professionals and college students |
|
Online Batch
|
Learners who prefer remote learning |
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Offline Batch
|
Learners looking for classroom-based data science classes in Chennai |
Looking for the latest batch details? Contact Codelyra for the latest batch dates, timings, course fees, and availability.
Why Choose Our Data Science Course in Chennai?
Choosing the right training program is important when you are building a career in data. Codelyra focuses on practical learning rather than limiting the experience to theoretical concepts.
Practical, Project-Based Learning
Work with realistic datasets and business scenarios to understand how data science is applied in real organizations.
Python-Centred Learning
Build practical programming skills with Python and learn widely used libraries and frameworks for data analysis, visualization, and machine learning.
AI & Machine Learning Exposure
The course introduces learners to machine learning and AI concepts, helping them understand how predictive models are created and evaluated.
Learn from Industry-Oriented Trainers
Learn through practical demonstrations, guided exercises, assignments, and project discussions. Trainers should bring relevant industry experience and practical examples into the classroom.
Career-Focused Learning
Along with technical skills, learners can work on resume preparation, interview-oriented exercises, project discussions, and career guidance.
Portfolio Development
Build multiple projects that demonstrate your ability to clean data, analyse patterns, visualize information, and develop predictive models.
Flexible Learning
Choose weekday, weekend, online, or classroom learning options based on your schedule.
Placement Support
Codelyra provides placement-oriented support such as interview preparation, resume guidance, job-search assistance, and career guidance. Placement support does not guarantee employment, as hiring depends on individual skills, interview performance, experience, and employer requirements.
Focused on practical learning. Build foundational knowledge, work on projects, and develop skills that support your data science learning journey.
Data Science Course Syllabus
Our syllabus follows a beginner-to-advanced learning path, allowing learners to gradually build their technical and analytical capabilities.
01 Introduction to Data Science
- What is Data Science?
- Data Science lifecycle
- Data analytics vs Data Science
- Roles of a Data Scientist
- Types of data
- Structured and unstructured data
- Business problems and data-driven decision-making
- Data Science applications across industries
- Understanding datasets and data sources
02 Python Programming Fundamentals
- Python introduction
- Variables and data types
- Operators
- Conditional statements
- Loops
- Functions
- Lists, tuples, sets, and dictionaries
- Strings
- Exception handling
- File handling
- Modules and packages
- Object-oriented programming basics
03 Python for Data Science
- NumPy fundamentals
- Arrays and operations
- Pandas Series and DataFrames
- DataFrame manipulation
- Filtering and sorting
- Grouping and aggregation
- Merging and joining datasets
- Handling missing values
- Data transformation
- Importing and exporting datasets
04 SQL & Database Fundamentals
- Database concepts
- Relational databases
- Tables and relationships
- SQL syntax
- SELECT queries
- WHERE, ORDER BY and GROUP BY
- Aggregate functions
- Joins
- Subqueries
- Views
- Data filtering and transformation
- SQL for analytical workloads
05 Statistics for Data Science
- Descriptive statistics
- Mean, median, and mode
- Variance and standard deviation
- Probability fundamentals
- Probability distributions
- Normal distribution
- Sampling
- Correlation
- Covariance
- Hypothesis testing
- Confidence intervals
- Statistical significance
06 Data Cleaning & Preprocessing
- Understanding data quality
- Missing data
- Duplicate records
- Outlier detection
- Data normalization
- Data standardization
- Encoding categorical variables
- Feature transformation
- Data validation
- Preparing datasets for machine learning
07 Exploratory Data Analysis
- Understanding EDA
- Univariate analysis
- Bivariate analysis
- Multivariate analysis
- Identifying trends and patterns
- Correlation analysis
- Outlier analysis
- Feature relationships
- Business-oriented data interpretation
08 Data Visualization
- Principles of effective visualization
- Matplotlib
- Seaborn
- Charts and plots
- Distribution visualizations
- Comparison charts
- Correlation visualizations
- Interactive visualization concepts
- Dashboard fundamentals
- Presenting data-driven insights
09 Machine Learning Fundamentals
- What is Machine Learning?
- Supervised learning
- Unsupervised learning
- Semi-supervised learning overview
- Training and testing datasets
- Features and target variables
- Model training
- Model prediction
- Overfitting and underfitting
- Bias and variance
- Model evaluation
10 Supervised Machine Learning
Regression
- Linear Regression
- Multiple Linear Regression
- Regression evaluation metrics
- Prediction use cases
Classification
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbours
- Support Vector Machines
- Classification metrics
- Confusion matrix
- Precision
- Recall
- F1-score
- ROC-AUC
11 Unsupervised Machine Learning
- Clustering concepts
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction
- Principal Component Analysis
- Customer segmentation
- Pattern discovery
- Evaluating clustering results
12 Feature Engineering
- Feature selection
- Feature extraction
- Feature transformation
- Encoding techniques
- Scaling
- Handling high-dimensional data
- Selecting useful features
- Improving model performance
13 Advanced Machine Learning
- Ensemble learning
- Bagging
- Boosting
- Gradient Boosting
- Random Forest optimization
- Hyperparameter tuning
- Cross-validation
- Model comparison
- Pipeline development
- Model optimization
14 AI & Data Science
- Introduction to Artificial Intelligence
- AI vs Machine Learning vs Data Science
- Generative AI concepts
- AI-assisted data analysis
- Machine learning applications
- AI-powered business use cases
- Responsible AI fundamentals
- Data privacy and ethical considerations
15 Deep Learning Fundamentals
- Introduction to neural networks
- Neurons and activation functions
- Neural network architecture
- Forward propagation
- Backpropagation concepts
- Training neural networks
- CNN fundamentals
- RNN fundamentals
- Deep learning use cases
16 Natural Language Processing
- Introduction to NLP
- Text preprocessing
- Tokenization
- Stop-word removal
- Stemming and lemmatization
- Text representation
- Sentiment analysis
- Text classification
- NLP use cases
17 R Programming for Data Science
- R fundamentals
- Variables and data structures
- Vectors and data frames
- Data manipulation
- Statistical analysis
- Data visualization in R
- Working with datasets
- Introduction to R-based analytics
18 Model Deployment & MLOps Fundamentals
- Introduction to model deployment
- Saving trained models
- Creating prediction services
- API fundamentals
- Flask/FastAPI concepts
- Deployment workflow
- Model monitoring concepts
- Version control
- Introduction to MLOps
19 Data Science Projects
- Problem identification
- Dataset selection
- Data collection
- Data cleaning
- Exploratory analysis
- Feature engineering
- Model development
- Model evaluation
- Insight generation
- Project documentation
- Presentation and portfolio development
20 Career & Interview Preparation
- Data Science resume preparation
- Portfolio development
- Technical interview questions
- SQL interview preparation
- Python interview preparation
- Statistics interview preparation
- Machine Learning interview preparation
- Project explanation techniques
- Mock interview practice
- Career guidance
Top Skills You Gain
By completing the Data Science Course in Chennai, learners can develop practical technical, analytical, and problem-solving skills.
Python programming
Data analysis
SQL
Statistics
Data cleaning
Data preprocessing
Exploratory Data Analysis
Data visualization
Machine Learning
Regression
Classification
Clustering
Feature engineering
Model evaluation
Predictive analytics
Deep learning fundamentals
Natural Language Processing
R programming
AI fundamentals
Model deployment
Problem-solving
Business data interpretation
Data storytelling
Top Tools & Technologies You Will Learn
A practical data science training in Chennai should expose learners to tools used across different stages of the data workflow.
Programming & Analysis
- Python
- R
- Jupyter Notebook
- Google Colab
Python Libraries
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
Databases
- MySQL
- PostgreSQL
Machine Learning
- Scikit-learn
- XGBoost
- ML model pipelines
Visualization & Reporting
- Power BI
- Tableau
- Matplotlib
- Seaborn
Development & Collaboration
- Git
- GitHub
- VS Code
Deployment
- Flask
- FastAPI
- Cloud deployment concepts
The exact tool stack may be updated based on current industry practices and the latest course curriculum.
6 Industry-Relevant Data Science Capstone Projects
Hands-on projects help learners connect concepts with practical business problems.
01
E-Commerce Customer Churn Prediction
Objective: Predict customers who are likely to stop using an e-commerce platform.
Activities
- Clean customer data
- Analyse purchasing behaviour
- Perform EDA
- Engineer customer-level features
- Train classification models
- Compare model performance
- Generate business recommendations
Skills Applied
02
House Price Prediction System
Objective: Build a machine learning model to estimate property prices based on relevant features.
Activities
- Analyse property datasets
- Handle missing values
- Identify influential features
- Perform correlation analysis
- Build regression models
- Evaluate prediction accuracy
- Present insights through visualizations
Skills Applied
03
Customer Segmentation for Retail
Objective: Group customers based on purchasing behaviour to support targeted marketing strategies.
Activities
- Prepare customer transaction data
- Perform exploratory analysis
- Create customer behavioural features
- Apply clustering algorithms
- Identify customer segments
- Visualize segment characteristics
- Recommend marketing strategies
Skills Applied
04
Healthcare Patient Risk Prediction
Objective: Develop a predictive model that identifies patients belonging to different risk categories based on available data.
Activities
- Prepare healthcare datasets
- Perform data quality checks
- Analyse patient attributes
- Engineer relevant features
- Build classification models
- Evaluate model performance
- Communicate findings responsibly
Skills Applied
05
Sentiment Analysis for Customer Reviews
Objective: Analyse customer reviews and classify them according to sentiment.
Activities
- Collect or prepare review data
- Clean textual data
- Tokenize and preprocess text
- Convert text into machine-readable features
- Train classification models
- Evaluate predictions
- Visualize sentiment trends
Skills Applied
06
Sales Forecasting & Business Analytics
Objective: Analyse historical sales data and develop a forecasting solution to support business planning.
Activities
- Analyse historical sales
- Identify trends and seasonal patterns
- Clean and transform data
- Create analytical features
- Develop forecasting models
- Evaluate predictions
- Build an executive-friendly dashboard
- Present actionable business insights
Skills Applied
Key Features of the Data Science Course
Flexible Timing
Choose learning schedules designed to accommodate students, professionals, and career switchers.
2+ Certifications
Receive course-related certification opportunities based on the program structure and successful completion requirements. Third-party professional certifications, where applicable, may have separate eligibility or examination requirements.
EMI Options
Flexible EMI options may be available to make professional training more manageable. Contact Codelyra for the latest eligibility and payment details.
Placement Supports
Get career-oriented assistance including resume guidance, interview preparation, job-search support, and career counselling. Placement support is intended to improve employability and does not guarantee a job offer.
Industry-Relevant Capstone Projects
Build practical projects around e-commerce, healthcare, retail, forecasting, customer analytics, and NLP to strengthen your portfolio.
Data Science Course Fees in Chennai
The data science course fees in Chennai can vary depending on the learning format, curriculum coverage, batch type, project components, certification structure, and available offers.
Instead of choosing a course based only on price, compare:
- Curriculum depth
- Trainer experience
- Hands-on projects
- Learning format
- Tools covered
- Certification
- Placement support
- Interview preparation
- Project portfolio opportunities
For the latest Codelyra Data Science Course fees in Chennai, contact the training team for current pricing, payment options, and available offers.
Data Science Course with Placement in Chennai
Learners looking for a data science course with placement in Chennai should consider both technical training and career support.
Codelyra's Placement-Oriented Support Can Include:
- Resume building
- LinkedIn/profile guidance
- Technical interview preparation
- Mock interviews
- SQL and Python interview practice
- Machine Learning interview preparation
- Project explanation guidance
- Job-search assistance
- Career counselling
The objective is to help learners become more confident and job-ready. Employment ultimately depends on the learner's skills, experience, interview performance, and the hiring company's requirements.
Who Can Join This Data Science Course?
The course can be suitable for:
- Fresh graduates
- College students
- Working professionals
- Software professionals
- Data analysts
- Business analysts
- Developers
- QA professionals interested in analytics
- Professionals planning a career transition
- Entrepreneurs interested in data-driven decision-making
- Anyone interested in building practical data science skills
Basic computer knowledge and an interest in analytical problem-solving are helpful. Programming experience is useful but not mandatory for beginners.
Frequently Asked Questions
What is a Data Science course?
A Data Science course teaches learners how to collect, clean, analyse, visualize, and model data to generate useful insights and support data-driven decisions. A comprehensive program generally covers Python, SQL, statistics, visualization, machine learning, and practical projects.
Is this Data Science Course in Chennai suitable for beginners?
Yes. A beginner-friendly learning path starts with Python, SQL, statistics, and data fundamentals before progressing to machine learning, AI, and advanced data science concepts.
What programming languages are covered?
The course primarily focuses on Python, with exposure to R programming for data analysis and statistics.
Is Python necessary for Data Science?
Python is one of the most widely used programming languages in data science. Learning Python helps you work with datasets, perform analysis, visualize information, build machine learning models, and automate data workflows.
Does the course include Machine Learning?
Yes. The curriculum progresses from data analysis and statistics to supervised learning, unsupervised learning, feature engineering, model evaluation, and advanced machine learning concepts.
Does the course cover AI?
Yes. The curriculum introduces AI concepts and their relationship with Data Science and Machine Learning, along with practical AI-oriented use cases.
Do you provide a Data Science certification?
Certification depends on the course completion requirements and certification structure. Learners should verify whether a certificate is an institute-issued course completion certificate or an external professional certification.
What is the difference between a Data Science course and Data Analytics course?
Data Analytics primarily focuses on examining existing data to identify trends, patterns, and insights. Data Science covers analytics along with programming, statistics, machine learning, predictive modelling, and more advanced data-driven applications.
Do you offer Data Science training with placement in Chennai?
Codelyra provides placement-oriented support such as resume preparation, interview training, career guidance, and job-search assistance. Placement support does not guarantee employment.
Can I learn Data Science with Python?
Yes. Python is a major part of the curriculum, covering programming fundamentals, Pandas, NumPy, visualization, data preprocessing, machine learning, and practical projects.
Is R programming included in the course?
Yes. The curriculum includes R programming fundamentals and its application in data analysis, statistics, and visualization.
What tools will I learn?
Depending on the current curriculum, learners can work with Python, R, SQL, Jupyter Notebook, Google Colab, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Power BI, Tableau, Git, GitHub, and deployment tools.
How many projects will I complete?
The program includes multiple practical exercises and 6 industry-relevant capstone projects covering areas such as customer analytics, retail, healthcare, NLP, prediction, and forecasting.
What are the career opportunities after Data Science training?
Depending on skills, experience, and specialization, learners can explore roles such as:
- Data Scientist
- Junior Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Analyst
- Data Science Intern
- Python Data Analyst
- AI/ML Associate
- Reporting & Visualization Analyst
What are the Data Science course fees in Chennai?
Fees vary between institutes and depend on factors such as course duration, delivery mode, curriculum, projects, certifications, and placement support. Contact Codelyra for the latest course fee details.
How long does it take to learn Data Science?
The learning time depends on your existing programming knowledge, study schedule, course depth, and practice. A structured beginner-to-advanced program can help you learn progressively through guided lessons and projects.
Why choose a Data Science institute in Chennai?
A structured Data Science institute in Chennai can provide guided learning, trainer interaction, practical projects, peer learning, career preparation, and a clear progression from fundamentals to advanced concepts.
Is online Data Science training available?
Online learning options may be available depending on the current batch schedule. Contact Codelyra to check the latest online and classroom options.
Is there a weekend Data Science batch?
Weekend batches may be available for working professionals and students. Check with Codelyra for current schedules and availability.