4.9 (26,850) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 26th October 2025
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Evolution of Data Science
• Big Data Vs Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
MODULE 2: DATA SCIENCE DEMO
• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value.
MODULE 3: ANALYTICS CLASSIFICATION
• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• EDA and insight gathering demo in Tableau
MODULE 4: DATA SCIENCE AND RELATED FIELDS
• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to Generative Passive Models
MODULE 5: DATA SCIENCE ROLES & WORKFLOW
• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages.
MODULE 6: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS
• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1: OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2: HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3: EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4: HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7 : DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code Commits
• Pull, Fetch and Conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2 : HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
The Data Science course in Chennai is open to anyone with a basic understanding of mathematics and statistics. It suits fresh graduates, working professionals, and career switchers looking to upskill. While prior programming knowledge is beneficial, it is not mandatory for most courses. Enthusiasm to work with data and analytical thinking are key traits for aspiring learners.
As per AmbitionBox, the salary of a Data Scientist in Chennai ranges from INR4 Lakhs to INR22 Lakhs annually, with an average of INR15 Lakhs for professionals with 1 to 7 years of experience, based on 2,300 recent salary reports.
Essential technical skills include proficiency in Python, R, and SQL for data handling and analysis. A strong foundation in statistics, machine learning, and data visualization is critical. Familiarity with big data tools, cloud platforms, and AI frameworks can further enhance career opportunities.
The Certified Data Scientist Course in Chennai is highly recommended. It covers machine learning, deep learning, statistics, and includes practical, hands-on projects to ensure learners gain real-world experience. Completing this program prepares learners for diverse data-driven roles across industries.
As far as Data Scientist is concerned Python is the most effective programming language, with a lot of libraries available. Python can be deployed at every phase of data science functions. It is beneficial in capturing data and importing it into SQL. Python can also be used to create data sets.
Yes, Data Science roles in Chennai are growing steadily. Companies in IT, finance, healthcare, and e-commerce increasingly rely on analytics, AI, and machine learning, driving demand for skilled professionals.
In a Data Science course in Chennai, learners acquire comprehensive knowledge and practical experience across key areas:
Data Science courses in Chennai typically last 3 to 8 months, depending on the program’s depth, mode (online/offline), and project requirements. Advanced or certification courses may extend up to 1 year.
The Data Science Course fees in Chennai generally range from INR 15,000 to INR 2,50,000, depending on the institute, duration, and whether the course is online, classroom-based, or hybrid.
Freshers can pursue roles such as Data Analyst, Junior Data Scientist, Business Analyst, Machine Learning Intern, and AI Associate, with opportunities across IT, finance, healthcare, and analytics startups.
Yes, coding is essential for data manipulation, analysis, and predictive modeling. Python and SQL are the most widely used languages. Beginners can quickly learn coding skills through practical, project-based training.
After completing the Certified Data Scientist Course in Chennai, an individual will be well equipped with the following:-
Intense knowledge of the workflow, of a Data Science project.
Learn the basics of the use of Statistics in Data Science.
Gain knowledge of the various Machine Learning Algorithms.
Knowledge of Data Forecasting, Data Mining and Data Visualization.
Ways to deliver end to end Data Science projects.
Chennai is known is among the tech hubs of India, with lots of business opportunities and large corporate houses adorning the city. This, in turn, contributes to new employment opportunities being created. Hence opting for a Data Science course in Chennai will help an individual to leverage the available possibilities in the best manner, to land a career in Data Science.
Data Scientists have been in great demand in Chennai. As an acknowledgement to this rising demand, DataMites has come with the Certified Data Scientist course in Chennai. The course covers all the areas of Data Science, Machine Learning, basics of Mathematics and Statistics, etc. Also, the Certified Data Scientist course, covers all the practical aspects of the knowledge required to become a Data Scientist.
Chennai has several large companies, Banking and Financial institutions, Insurance companies, Automobile companies, Manufacturing enterprises, as a result, Chennai happens to be the most sought after city when it comes to career opportunities in Data Science.
Chennai is witnessing increasing interest in Data Science training. Institutes offer specialized programs in AI, machine learning, big data, and analytics, reflecting the rising demand for professionals capable of handling data-intensive projects.
Among the top choices in Chennai, DataMites is highly recommended. It offers comprehensive training with globally recognized certifications, hands-on projects, and expert-led sessions. Dedicated career support ensures learners are prepared for industry-ready roles in Data Science.
Core components include data collection, cleaning, analysis, applying machine learning models, statistical methods, and visualization to derive actionable insights and support decision-making.
Challenges include handling incomplete or messy datasets, ensuring data security and privacy, managing algorithmic bias, understanding complex models, and translating insights into practical business strategies.
Data Scientists design predictive models, build algorithms, and extract insights from large datasets, whereas Data Analysts primarily interpret trends, generate reports, and support decision-making. Scientists focus on creating solutions; analysts focus on interpreting data.
Common tools include Python, R, SQL, Excel, Tableau, Power BI, and cloud platforms. Machine learning libraries such as scikit-learn, TensorFlow, and PyTorch are widely used for predictive analytics and AI applications.
Yes, non-engineering graduates can pursue Data Science careers in Chennai. Strong analytical skills, basic programming knowledge, and relevant certifications can bridge skill gaps and prepare candidates for industry-ready roles.
Yes, DataMites Chennai provides placement assistance for its Data Science courses. Students gain practical experience through live projects and receive guidance to secure industry-ready roles.
DataMites Chennai offers a structured curriculum, experienced trainers, and hands-on projects. The institute emphasizes industry-relevant skills and provides career guidance, mentorship, placement support, internship opportunities, and recognized certifications, making it ideal for learners seeking practical, job-ready training.
Fresh graduates, working professionals, and individuals seeking a career switch can enroll in DataMites data science courses. A basic understanding of mathematics and programming is sufficient, though advanced courses may have additional prerequisites.
The Data Science course fee in Chennai varies based on the learning mode: live online training costs around INR 60,000, classroom programs are approximately INR 65,000, and blended learning options are available for about INR 35,000. For exact details, it’s recommended to contact the DataMites Chennai center directly.
The DataMites Data Science course in Chennai spans approximately 8 months, consisting of around 700 learning hours. The program offers a well-balanced mix of theoretical learning and practical, hands-on training, covering all essential areas of data science.
Yes, students can gain internship experience through real-world projects, helping build practical skills and enhancing career opportunities.
DataMites Chennai provides a full refund if cancellation is requested within one week of the course commencement, provided the student has attended at least two sessions. Refunds are typically processed within 5–7 business days, and no refunds are applicable after six months from the date of enrollment.
Enrolling for online training online is very simple. The payment can be done using your debit/credit card that includes Visa Card, MasterCard; American Express or via PayPal. You will receive the receipt after the payment is successful. In case of more queries you can get in touch with our educational counselor who will guide you with the same.
You have access to the online study materials from 6 months upto 1 year.
Yes, DataMites Chennai offers EMI options for its Data Science courses, allowing students to pay the fees through easy monthly installments. This makes it convenient for learners to manage costs while pursuing their studies. For detailed information on EMI plans, it’s best to contact the DataMites Chennai center directly.
Yes, DataMites Chennai offers a free demo class for its Data Science courses, allowing prospective students to explore the course structure and teaching methodology before enrolling and make an informed decision about their learning journey.
Payments can be made via debit/credit cards (Visa, MasterCard, American Express) and PayPal. Enrollment confirmation and course materials are provided after payment.
The syllabus covers Python or R programming, statistics, machine learning, data visualization, data mining, and optionally big data technologies, depending on the course level.
Yes, at DataMites Chennai, students work on live projects, gaining practical experience with real-world problems and solutions. This hands-on training bridges the gap between classroom learning and industry requirements, effectively preparing them for careers in data science.
DataMites Chennai provides both live and recorded online classes for its Data Science courses. Live sessions facilitate real-time interaction with instructors, while recorded sessions give students the flexibility to review lessons anytime, ensuring a convenient and thorough learning experience.
A Certified Data Scientist program includes programming, statistical analysis, machine learning, data visualization, and hands-on projects, often with exposure to big data tools, equipping learners with industry-ready skills.
DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-
Industry aligned courses
Online sessions that ensure good engagement.
Expert Trainers, who possess a vast knowledge of the subject matter.
Case studies approach, which delved deep into the practical application of the concepts.
Opportunity to get connected with a network of Data Science professionals.
Career Guidance
Opportunity to work on projects
DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The flexi pass is specific to one particular course. Therefore if you have a flexi pass for one particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a flexi pass is valid for a particular period.
Yes, learners receive certification recognized by IABAC® and NASSCOM® FutureSkills, validating their expertise and improving career prospects.
All the online sessions are recorded and will be shared with the candidates. If you miss any of the online sessions, you can still have access to the recordings later.
DataMites Chennai is easily accessible from key areas such as Guindy (600032) and Perungudi (600096), prominent commercial and residential hubs, and Vadapalani (600026), a well-known central locality. Neighborhoods like Thiruvanmiyur (600041), Adyar (600020), Velachery (600042), Saligramam (600093), and Koyambedu (600107) offer convenient access to the training centers. The branches are also well connected to nearby localities including Anna Nagar (600040), T. Nagar (600017), Alandur (600016), and OMR corridor areas, making it easy for students and professionals to enroll in DataMites courses.
Trainers are experienced industry professionals who provide personalized guidance, mentorship, and practical insights to ensure students gain both conceptual and hands-on expertise.
No, DataMites doesn’t guarantee a job, but it will provide all the support and guidance needed, in getting a job, Resume Building, Interview preparations. DataMites internships offer a candidate to work with industry experts, which helps in knowing the corporate way of working. This proves as a stepping stone to an individual’s professional life.
DataMites Chennai offers both online and offline Data Science courses, allowing students to choose the learning mode that best fits their schedule and preferences. The curriculum and quality of instruction remain consistent across both formats. The Chennai center is located at Door No. SP, Spero Primus, Primus Building, Awfis, 7A, Guindy Industrial Estate, SIDCO Industrial Estate, Guindy, Chennai, Tamil Nadu 600032, providing easy access for students and working professionals.
DataMites has three offline training centers in Chennai:
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.
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