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Data Science Course in Chennai

(4.6) 18215 Ratings
  • Enroll in the Best Data Science Course in Chennai to Gain Expertise in Data-Driven Insights.
  • Engage in Practical Assignments and Capstone Projects Supervised by Experienced Data Scientists.
  • Flexible Data Science Learning Options: Weekday, Weekend, and Accelerated Fast-Track Sessions Available.
  • Comprehensive and Hands-On Data Science Training with Career-Driven Placement Assistance and Mentorship.
  • Get Personalized Guidance with Technical Interview Preparation, and Career Roadmap Planning in Data Science.
  • Curriculum Covers Python, Machine Learning, Data Visualization, Statistics, SQL, Deep Learning, and Data Projects.

Course Duration

55+ Hrs

Live Project

3 Project

Certification Pass

Guaranteed

Training Format

Live Online (Expert Trainers)
WatchLive Classes
Course fee at
₹14500

₹18000

10987+

Professionals Trained

8+

Batches every month

2537+

Placed Students

165+

Corporate Served

What You'll Learn

Learn data analysis techniques using Python, R, and various machine learning libraries.

Understand data visualization with tools like Tableau and Matplotlib to interpret trends.

Data Science Training in Chennai Gain hands-on experience with data cleaning, preparation, and handling real-world data.

Explore statistical methods for data analysis to draw actionable insights from large datasets.

Master machine learning algorithms, including classification, regression, and clustering.

The Data Science Course in Chennai offers practical skills to excel in analytics industry.

A Comprehensive Overview to the Data Science Course

A comprehensive Data Science Course in Chennai provides a broad range of topics and skills necessary to excel in the field. It covers key areas like data cleaning and statistical analysis, enabling students to work effectively with large datasets. The course also includes machine learning principles, such as supervised and unsupervised learning and hyperparameter tuning. Data Science Training in Chennai Advanced topics like neural networks, deep learning frameworks (TensorFlow, PyTorch), natural language processing (NLP), and big data technologies are explored. Students gain expertise applicable across industries like finance, healthcare, and marketing. The course also includes hands-on projects that allow participants to apply their knowledge to real-world challenges. Additionally, students learn to use popular Data Science Certification Course in Chennai tools and programming languages like Python, R, and SQL, preparing them for diverse roles in the data-driven job market. .

Additional Info

Exploring Future Advancements in Data Science Course

  • Artificial Intelligence and Machine Learning: Data science will continue to be revolutionized by advances in AI and machine learning algorithms, which will make it possible to perform more complex reinforcement learning, natural language processing (NLP), computer vision, and predictive modeling. Breakthroughs in deep learning architectures, such as transformers and generative adversarial networks (GANs), will lead to more powerful and versatile AI models capable of handling complex and diverse datasets.
  • Big Data and Distributed Computing: Big data processing and analysis will place an increasing focus on scalable and distributed computing frameworks as the amount, velocity, and variety of data increase. Using distributed deep learning frameworks, Apache Spark, and Flink, among other technologies, enterprises will be able to quickly and efficiently extract insights from large datasets.
  • BI Developer: BI Developers design, develop, and maintain data warehouses and BI solutions. Your proficiency in database management systems, ETL processes, and BI tools will enable you to build robust data infrastructure and analytical solutions to support business operations.
  • Edge Computing and IoT Analytics: Real-time analytics at the edge and edge computing will receive more attention as Internet of Things (IoT) devices and sensors proliferate. Decentralized processing and analysis of IoT data will be made possible by data science techniques designed for edge devices, such as federated learning strategies and lightweight machine learning models. This will result in quicker insights and lower latency.
  • Hybrid Cloud: Hybrid cloud combines public and private cloud environments, allowing organizations to leverage the benefits of both. It provides flexibility, scalability, and control over sensitive data while utilizing public cloud resources for scalability and cost-efficiency.
  • Explainable AI and Responsible Data Science: There will be an increasing need for explainable AI (XAI) methods that offer interpretability and transparency in model predictions and decision-making procedures as AI systems become more commonplace. In order to ensure that AI systems meet ethical and legal requirements, responsible data science practices—such as ethical AI and fairness, accountability, and transparency (FAT) considerations—will be essential to their development and implementation.
  • Automated Machine Learning (AutoML): The democratization of machine learning through AutoML platforms will empower non-experts to build and deploy machine learning models with minimal manual intervention.AutoML tools will automate various aspects of the machine learning pipeline, including feature engineering, model selection, hyperparameter tuning, and model deployment, accelerating the development cycle and democratizing access to AI.
  • Domain-Specific Data Science Solutions: Industry-specific data science solutions tailored for domains such as healthcare, finance, retail, manufacturing, and energy will continue to emerge, addressing unique challenges and opportunities within each sector. These domain-specific solutions will leverage domain expertise, specialized datasets, and custom algorithms to deliver targeted insights and value-added analytics for specific use cases and applications.

Mastering Data Science Course Essential Tools for Developers

  • Python: Python is a flexible programming language which is broadly used in data science for data manipulation, statistical analysis, and machine learning. It has very strong libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib, which can be used to process the data effectively, build models, and make visualizations. Python is an essential tool for any data scientist with its easy syntax and high community support. It's suitable for beginners and experts alike because it's simple yet powerful.
  • R Programming: R is a programming language that is specifically designed for statistical computing and data analysis. It has a rich set of libraries for data visualization, such as ggplot2, and statistical modeling, which makes it suitable for exploratory data analysis (EDA). R is widely used in academia, healthcare, and research for its complete statistical tools. It is the first choice of statisticians and data analysts due to its specialized functions and visualization capabilities.
  • SQL: SQL is an important tool to query and administer relational databases. With SQL, a data scientist will be able to retrieve and filter information from a database like MySQL, PostgreSQL, or Oracle. SQL supports the process of efficient extraction and manipulation of data for clean-up purposes and preprocessing of data.Effective working with structured data can be assured only if the person has expertise in SQL and can thus perform tasks associated with that discipline.
  • Jupyter Notebooks: Jupyter Notebooks is an open-source web application that gives data scientists the ability to write and run code in an interactive manner, as well as explore and share insights. Languages supported by Jupyter Notebooks include Python, R, and Julia, making it a widely used tool in data exploration, model development, and visualization. Jupyter also has the ability to combine text and code. It's ideal for use in documentation and sharing, as it is commonly utilized in experimentation and collaboration.
  • TensorFlow: TensorFlow is an open-source deep learning framework developed by Google. It is widely used for building neural networks, image processing, and natural language processing (NLP). TensorFlow provides tools for both research and production environments, allowing developers to design complex machine learning models. Its flexibility, scalability, and support for GPU computation make it an essential technology for AI-driven applications.
  • PyTorch: PyTorch is a dynamic deep learning framework that Facebook developed and is known for easy usability and flexibility in modeling design. It allows its developers to build machine learning models with dynamic computation graphs, which makes it appropriate for research and rapid prototyping. PyTorch integrates with Python and has expansive support for neural networks, NLP, and computer vision. This makes it super popular among data scientists and researchers. It also offers support for GPU acceleration.
  • Apache Hadoop: Apache Hadoop is an open-source framework used for distributed storage and processing of large datasets across clusters of computers. It allows data scientists to work with big data by dividing tasks and distributing them, making it scalable and efficient for large-scale data processing. Hadoop includes tools like HDFS (Hadoop Distributed File System) and MapReduce for parallel computation. It's crucial for managing vast amounts of data that exceed the capabilities of traditional systems.
  • Apache Spark : Apache Spark is a powerful open-source framework for real-time data processing and analytics. It offers in-memory computing, making it faster than Hadoop for many operations. Spark supports machine learning (MLlib), SQL querying, and graph processing, which makes it highly versatile for big data applications. Its ability to process data in real-time and integration with various data sources make it a key tool in modern data science workflows.

Exploring Career Paths in Data Science Course

  • Data Scientist:As a data scientist, you will use your expertise in statistical modeling, machine learning, and data analysis to draw conclusions from data and resolve challenging business issues. Working with huge datasets, creating prediction models, and informing stakeholders of findings are all part of the job.
  • Data Analyst: The primary goal of data analysis and interpretation for data analysts is to find patterns, trends, and insights that can guide business choices. Their approach involves utilizing statistical methods, data visualization software, and subject expertise to convert data into meaningful insights.
  • DevOps Engineer: Integrates development and operations teams to streamline software delivery pipelines, leveraging cloud technologies for continuous integration, deployment, and monitoring to enhance agility and collaboration.
  • Machine Learning Engineer: In production settings, machine learning models and algorithms are designed, constructed, and implemented by machine learning engineers. Scalable and effective solutions for practical applications are created in close collaboration with data scientists and software engineers.
  • Business Intelligence Analyst: Business intelligence analysts assist firms in making decisions by gathering, analyzing, and presenting data. They create dashboards, reports, and visualizations that offer insights into company metrics and key performance indicators (KPIs).
  • Data Engineer: Data engineers focus on designing and maintaining data pipelines, databases, and infrastructure to support data-driven initiatives. They work with big data technologies, ETL (extract, transform, load) processes, and data warehousing solutions to ensure data availability and reliability.
  • AI Research Scientist: AI research scientists conduct research and development in the field of artificial intelligence (AI), exploring new algorithms, techniques, and methodologies to advance the state-of-the-art in machine learning and AI.
  • Quantitative Analyst (Quant): AQuants create models and strategies for trading, risk management, and investment analysis by applying mathematical and statistical techniques to financial data. Their areas of expertise include financial modeling, algorithmic trading, and quantitative finance.

Companies Looking for Data Science Professionals

  • Amazon: Amazon utilizes data science and machine learning across its e-commerce platform, cloud computing services (Amazon Web Services), digital content (Amazon Prime Video, Kindle), and logistics and supply chain operations. Its data scientists work on projects such as personalized recommendations, demand forecasting, and fraud detection.
  • Microsoft: Microsoft hires data scientists and analysts to work on projects related to Microsoft Azure, Office 365, Bing, and other products and services. Microsoft's data science professionals use machine learning and analytics to drive product innovation, improve user experiences, and solve complex problems.
  • Google: Google employs data scientists and machine learning engineers across various teams and departments, including Google AI, Google Cloud, and Google Research. Google data scientists work on projects ranging from search algorithms to natural language processing and computer vision.
  • Facebook: Facebook employs data scientists and machine learning engineers to analyze user data, improve content recommendations, enhance advertising targeting, and develop new features and products. Data science professionals at Facebook work on projects related to social network analysis, natural language processing, and computer vision.
  • Apple: Apple utilizes data science and analytics to improve user experiences across its products and services, including iPhone, iPad, Mac, Apple Watch, and Apple Music. Data scientists at Apple work on projects such as user behavior analysis, personalization, and optimization of algorithms.
  • Uber: Uber utilizes data science and analytics to optimize ride-hailing operations, improve driver and rider experiences, and develop new products and services. Its data scientists work on projects such as dynamic pricing, route optimization, and demand forecasting.
  • Airbnb: Airbnb employs data scientists and analysts to analyze user behavior, optimize search and booking experiences, and develop pricing strategies. Data science professionals at Airbnb work on projects related to user segmentation, market analysis, and recommendation systems.
  • General Electric (GE): General Electric utilizes data science and analytics to optimize operations across its various business units, including aviation, healthcare, power, and renewable energy. Data science professionals at GE work on projects related to predictive maintenance, asset optimization, and industrial IoT.
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Data Science Course Objectives

Prerequisites for a Data Science course typically includes, Basic knowledge of R or Python, two programming languages, Familiarity with fundamental mathematical concepts such as statistics, linear algebra, and calculus, Some courses may also require prior knowledge of data analysis techniques and tools.
  • Programming Languages: Python, R, SQL
  • Data Manipulation and Analysis: pandas, NumPy.
  • Data Visualization: Matplotlib, Seaborn, ggplot2
  • Machine Learning: sci-kit-learn, TensorFlow, PyTorch
  • Big Data Processing: Apache Spark, Hadoop
Data science courses are becoming more and more important in today's job market due to the volume and complexity of data that businesses and organizations are producing at an increasing rate. Organizations in a range of industries need data science courses in order to stay competitive, learn new things, and make data-driven choices. This suggests that there is a high demand for data science courses as well as a wide range of career growth options.
  • Exploratory Data Analysis
  • Machine Learning Algorithms
  • Data Visualization Techniques
  • Deep Learning and Neural Networks
  • Model Evaluation and Validation
The future scope of data science courses is promising, with continued advancements in technology, data analytics, and artificial intelligence. Data science courses are expected to play an increasingly critical role in driving innovation, solving complex problems, and informing decision-making across various sectors, including healthcare, finance, retail, manufacturing, and beyond.
  • Proficiency with r or python as a programming language
  • Comprehension of statistical ideas and methods for data analysis
  • Understanding of predictive modeling methods
  • Data visualization skills to communicate insights effectively
  • Problem-solving abilities and critical thinking skills
Yes, students work on real-world projects as part of our data science training program. These assignments give students actual experience and let them use their knowledge to address real-world issues with reliable data. Students who work on real-world projects develop a portfolio of work that shows prospective employers.
  • Comprehensive Curriculum
  • Hands On project
  • Expert Teachers
  • Job Placements
  • Flexible Learning Options
The certified data analysts are on high demand in various industries including technology, finance and banking, retail and e-commerce, manufacturing and supply chain, marketing and advertising.
  • Video lectures and tutorials covering various topics in data science.
  • Hands-on exercises and coding assignments to practice skills.
  • Reading materials such as textbooks, articles, and research papers.
  • Access to datasets for analysis and experimentation.
  • Support from instructors and mentors to assist with learning and project work.
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Data Science Course Benefits

The Data Science Certification Course in Chennai provides essential skills to analyze large dataset, uncover insight and solve complex problems using advanced analytic and machine learning technique. It offers hands-on experience with tools like Python, R and Tableau, empowering you to build data-driven solutions. Upon completion you gain a strong foundation for a successful career in the growing Data Science Internship in Chennai.

  • Designation
  • Annual Salary
    Hiring Companies
  • 4.7L
    Min
  • 7.9L
    Average
  • 16.7L
    Max
  • 5.10L
    Min
  • 8.3L
    Average
  • 17.1L
    Max
  • 4.52L
    Min
  • 8.3L
    Average
  • 17.2L
    Max
  • 4.9L
    Min
  • 8.2L
    Average
  • 18.0L
    Max

About Your Data Science Certification Training

Our Data Science Training Institute in Chennai offers a comprehensive learning path to master data analysis machine learning, and statistical techniques. With hands-on experience in tools like Python, R, and Tableau, you will develop practical skills to solve real-world problems. Our 100% placement support connects you with top employers, ensuring you have a successful career in the rapidly growing Data Science Projects in Chennai.

Top Skills You Will Gain
  • Data Analysis
  • Machine Learning
  • Statistical Modeling
  • Data Visualization
  • Data Preprocessing
  • Python Programming
  • Model Evaluation

12+ Data Science Tools

Online Classroom Batches Preferred

Weekdays (Mon - Fri)
14-July-2025
08:00 AM (IST)
Weekdays (Mon - Fri)
16-July-2025
08:00 AM (IST)
Weekend (Sat)
19-July-2025
11:00 AM (IST)
Weekend (Sun)
20-July-2025
11:00 AM (IST)
Can't find a batch you were looking for?
₹18000 ₹14500 10% OFF Expires in

No Interest Financing start at ₹ 5000 / month

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

Why Data Science Course From Learnovita ? 100% Money Back Guarantee

Data Science Course Curriculum

Trainers Profile

At Learnovita, our Data Science Course in Chennai instructors are certified professionals who possess extensive industry expertise and are actively employed by reputable multinational firms. Committed to equipping learners with advanced knowledge and industry-standard practices in Data science, they ensure that students are well-prepared for success in today's competitive job market.

Syllabus of Data Science Course Download syllabus

  • Basics of Data Science
  • Application Significance
  • Python Overview
  • R Overview
  • Data Manipulation
  • SQL Fundamentals
  • Data Cleaning Methods
  • Preprocessing Techniques
  • Handling Missing Data
  • Data Normalization
  • Data Transformation
  • R and Python Libraries
  • EDA Concepts
  • Descriptive Statistics
  • Data Visualization Techniques
  • EDA with Python
  • EDA with R
  • Visualization Libraries
  • Probability Basics
  • Probability Distributions
  • Hypothesis Testing
  • Regression Analysis
  • ANOVA & MANOVA
  • R and Python Analysis
  • Machine Learning Overview
  • Evaluation Metrics
  • Supervised Learning
  • Unsupervised Learning
  • Hyperparameter Tuning
  • Feature Engineering
  • Advanced Techniques
  • Dimensionality Reduction
  • Deep Learning Basics
  • Gradient Boosting
  • Support Vector Machines
  • Decision Trees
  • TensorFlow Basics
  • Neural Networks
  • Building Networks
  • Convolutional Networks
  • Recurrent Networks
  • Transfer Learning
  • NLP Overview
  • Text Preprocessing
  • Sentiment Analysis
  • Word Embeddings
  • Text Classification
  • Named Entity Recognition
  • Big Data Introduction
  • Apache Hadoop
  • Apache Spark
  • Distributed Datasets
  • Spark MLlib
  • Machine Learning
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Need customized curriculum?

Industry Projects

Project 1
Customer Segmentation Project

In this project, you'll analyze customer data to identify distinct segments using clustering techniques. By applying algorithms like K-Means, you’ll classify customers based on their purchasing behavior. The goal is to provide actionable insights for targeted marketing strategies and product recommendations.

Project 2
Predictive Maintenance Project

Using historical machine data, this project focuses on predicting equipment failure before it occurs. By implementing machine learning models, you’ll analyze patterns in sensor data to forecast maintenance needs. This reduces downtime and increases operational efficiency for manufacturing industries.

Project 3
Sentiment Analysis Project

In this project, you'll apply natural language processing (NLP) to analyze customer reviews content. By training a sentiment analysis model, you can classify text data into positive, negative, or neutral sentiments. This helps businesses understand customer opinions and improve products or services.

Career Support

Our Hiring Partner

Exam & Certification

  • Vendor-specific Certifications
  • Professional Certifications
  • Academic Certifications
  • Specialized Certifications
  • Technology and IT analyst
  • Assistance with drugs and medical
  • Manager of financial services
  • Manufacturer of Digital commerce
  • Production manager for telecommunications analysis
Although earning a data science certification can improve your credentials and increase your marketability. On the other hand, if you can successfully showcase your abilities and knowledge during the employment process, a certification can greatly boost your chances of getting interviews and job offers.
Video lectures and tutorials covering various topics in data science, Hands-on exercises and coding assignments to practice skills, Reading materials such as textbooks, articles, and research papers, Access to datasets for analysis and experimentation, Support from instructors and mentors to assist with learning and project work.
Obtaining a Data Science certification can significantly enhance your career growth by opening up new job opportunities in the rapidly growing field of Data Science and keeping you updated with the latest advancements in cloud technologies through ongoing certification maintenance.

Our Student Successful Story

checkimage Regular 1:1 Mentorship From Industry Experts checkimage Live Classes checkimage Career Support

How are the Data Science Course with LearnoVita Different?

Feature

LearnoVita

Other Institutes

Affordable Fees

Competitive Pricing With Flexible Payment Options.

Higher Data Science Fees With Limited Payment Options.

Live Class From ( Industry Expert)

Well Experienced Trainer From a Relevant Field With Practical Data Science Training

Theoretical Class With Limited Practical

Updated Syllabus

Updated and Industry-relevant Data Science Course Curriculum With Hands-on Learning.

Outdated Curriculum With Limited Practical Training.

Hands-on projects

Real-world Data Science Projects With Live Case Studies and Collaboration With Companies.

Basic Projects With Limited Real-world Application.

Certification

Industry-recognized Data Science Certifications With Global Validity.

Basic Data Science Certifications With Limited Recognition.

Placement Support

Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

Basic Placement Support

Industry Partnerships

Strong Ties With Top Tech Companies for Internships and Placements

No Partnerships, Limited Opportunities

Batch Size

Small Batch Sizes for Personalized Attention.

Large Batch Sizes With Limited Individual Focus.

Additional Features

Lifetime Access to Data Science Course Materials, Alumni Network, and Hackathons.

No Additional Features or Perks.

Training Support

Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.

Limited Mentor Support and No After-hours Assistance.

Data Science Course FAQ's

Certainly, you are welcome to join the demo session. However, due to our commitment to maintaining high-quality standards, we limit the number of participants in live sessions. Therefore, participation in a live class without enrollment is not feasible. If you're unable to attend, you can review our pre-recorded session featuring the same trainer. This will provide you with a comprehensive understanding of our class structure, instructor quality, and level of interaction.
All of our instructors are employed professionals in the industry who work for prestigious companies and have a minimum of 9 to 12 years of significant IT field experience. A great learning experience is provided by all of these knowledgeable people at LearnoVita.
  • LearnoVita is dedicated to assisting job seekers in seeking, connecting, and achieving success, while also ensuring employers are delighted with the ideal candidates.
  • Upon successful completion of a career course with LearnoVita, you may qualify for job placement assistance. We offer 100% placement assistance and maintain strong relationships with over 650 top MNCs.
  • Our Placement Cell aids students in securing interviews with major companies such as Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, Sports One , Infosys, MindTree, and MPhasis, among others.
  • LearnoVita has a legendary reputation for placing students, as evidenced by our Placed Students' List on our website. Last year alone, over 5400 students were placed in India and globally.
  • We conduct development sessions, including mock interviews and presentation skills training, to prepare students for challenging interview situations with confidence. With an 85% placement record, our Placement Cell continues to support you until you secure a position with a better MNC.
  • Please visit your student's portal for free access to job openings, study materials, videos, recorded sections, and top MNC interview questions.
LearnoVita Certification is awarded upon course completion and is recognized by all of the world's leading global corporations. LearnoVita are the exclusive authorized Oracle, Microsoft, Pearson Vue, and Data Science I exam centers, as well as an authorized partner of Data Science . Additionally, those who want to pass the National Authorized Certificate in a specialized IT domain can get assistance from LearnoVita's technical experts.
As part of the training program, LearnoVita provides you with the most recent, pertinent, and valuable real-world projects. Every program includes several projects that rigorously assess your knowledge, abilities, and real-world experience to ensure you are fully prepared for the workforce. Your abilities will be equivalent to six months of demanding industry experience once the tasks are completed.
At LearnoVita, participants can choose from instructor-led online training, self-paced training, classroom sessions, one-to-one training, fast-track programs, customized training, and online training options. Each mode is designed to provide flexibility and convenience to learners, allowing them to select the format that best suits their needs. With a range of training options available, participants can select the mode that aligns with their learning style, schedule, and career goals to excel in Data Science .
LearnoVita guarantees that you won't miss any topics or modules. You have three options to catch up: we'll reschedule classes to suit your schedule within the course duration, provide access to online class presentations and recordings, or allow you to attend the missed session in another live batch.
Please don't hesitate to reach out to us at contact@learnovita.com if you have any questions or need further clarification.
To enroll in the Data Science at LearnoVita, you can conveniently register through our website or visit any of our branches in India for direct assistance.
Yes, after you've enrolled, you will have lifetime access to the student portal's study materials, videos, and top MNC interview questions.
At LearnoVita, we prioritize individual attention for students, ensuring they can clarify doubts on complex topics and gain a richer understanding through interactions with instructors and peers. To facilitate this, we limit the size of each Data Science Service batch to 5 or 6 members.
The average annual salary for Data Science Professionals in India is 6 LPA to 7 LPA.
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