4.8 (21,456) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 5th 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!
To study data science in Madurai, start with online courses covering Python, statistics, and machine learning. Join local tech communities or meetups to gain practical insights and network with professionals. Work on real-world projects and participate in hackathons to build hands-on experience.
The duration of data science courses in Madurai varies between 3 months and 1 year, depending on the course type and depth. Short-term programs focus on fundamentals, while longer ones provide in-depth training. Course length may also differ based on learning modes like online, offline, or hybrid.
To pursue data science in Madurai, candidates typically need a background in mathematics, statistics, or computer science. Basic programming skills and knowledge of analytical tools are often required. Eligibility may vary based on the course level, ranging from diploma to advanced degrees.
The Data Science course fee in Madurai typically falls between Rs 15,000 and Rs 2,50,000, varying based on the institute, duration of the program, and chosen learning format. Students can select from online, classroom, or self-paced options depending on their convenience and learning style.
To excel in data science in Madurai, professionals should have strong skills in programming (Python, R, or SQL) for data analysis and manipulation. Knowledge of machine learning, statistics, and data visualization is essential for deriving insights. Additionally, expertise in big data tools, cloud platforms, and business intelligence enhances career opportunities.
Data science in Madurai is growing steadily, with increasing demand across industries like healthcare, retail, and finance. Businesses are adopting data-driven strategies, creating job opportunities for skilled professionals. With technological advancements, the future looks promising for data science in the region.
Madurai offers several reputable institutions for data science education, each providing comprehensive curricula and practical training. These programs are designed to equip students with essential skills in data analysis, machine learning, and data visualization, preparing them for successful careers in the field. Among them, DataMites institute is considered the best for its quality training and industry-relevant curriculum.
According to AmbitionBox, the salary of a Data Scientist in Madurai ranges between INR 4 Lakhs to INR 16 Lakhs, with an average annual salary of INR 14.6 Lakhs.
Yes, coding is important for a data science career in Madurai, as it helps in data analysis, automation, and model building. Knowledge of programming languages like Python or R is often required by employers. However, some roles focus more on data interpretation and business insights, requiring minimal coding.
Anyone interested in data science can enroll, including students, professionals, and career changers. Basic knowledge of mathematics, statistics, and programming may be required. Some courses may have specific eligibility criteria, so checking prerequisites is recommended.
Madurai is witnessing rapid adoption of data science across healthcare, retail, finance, and education, supported by smart city initiatives and AI-driven public services. Local colleges and institutes are introducing specialized AI and data science programs, creating strong career opportunities for data scientists, analysts, and machine learning engineers in the region.
SQL plays a key role in data science by enabling efficient data extraction, manipulation, and management from databases. It helps in cleaning, filtering, and aggregating large datasets for analysis and visualization. Strong SQL skills enhance data-driven decision-making by ensuring accurate and optimized querying.
Key ethical concerns in data science include privacy, bias, and transparency. Protecting user data, ensuring fairness in algorithms, and clearly explaining data usage are essential. Responsible data practices help build trust and prevent unintended harm.
Yes, learning Python is important for a data science course in Madurai, as it is widely used for data analysis, machine learning, and visualization. It simplifies complex computations and helps in handling large datasets efficiently. While some alternatives exist, Python remains a preferred choice due to its vast libraries and community support.
The key components of data science include data collection, processing, and analysis to extract insights. It involves statistics, machine learning, and data visualization to interpret patterns effectively. Practical applications rely on programming, algorithms, and decision-making techniques for informed outcomes.
Yes, non-engineering graduates can switch to data science in Madurai by learning essential skills like programming, statistics, and machine learning. Many online courses and local training programs offer structured learning paths. Gaining hands-on experience through projects and internships can also improve career prospects.
To become a data scientist in Madurai, start by learning programming (Python, R) and key concepts in statistics and machine learning. Gain hands-on experience through projects, online resources, and real-world data analysis. Build a strong portfolio and connect with local or remote job opportunities to advance your career.
The Certified Data Scientist course is widely regarded as the premier data science program in Madurai. This comprehensive course offers in-depth training in statistics, programming, and data visualization, equipping participants with the essential skills to excel in the data science field. Designed to meet industry standards, the program ensures that learners are well-prepared for successful careers in data science.
Madurai is experiencing a notable rise in demand for data science professionals, driven by sectors such as healthcare, retail, manufacturing, and education. This trend is creating ample career opportunities for both new graduates and seasoned experts in the region.
Madurai features key areas like Anna Nagar (625020) and KK Nagar (625020), both prominent residential and commercial hubs, and Tallakulam (625002), which hosts several educational institutes and training centers. Neighborhoods such as Simmakkal (625001), Koodal Nagar (625018), and Arappalayam (625016) offer a mix of residential and commercial spaces, making access to data science training convenient. The branch is also easily reachable from nearby localities including K. Pudur (625007), Melur (625106), Thiruparankundram (625005), Palanganatham (625003), and Villapuram (625012). Major areas in Madurai are well connected, providing easy access for students and professionals pursuing data science courses with hands-on training and practical applications.
Anyone interested in learning data science can enroll, including students, working professionals, and career changers. There are no strict eligibility criteria, but basic analytical skills and a willingness to learn are beneficial. Courses are available for beginners as well as experienced individuals seeking advanced knowledge.
Yes, there are data science courses in Madurai that offer placement assistance. These programs provide comprehensive training, covering essential topics such as Python, statistics, and machine learning, and often include practical projects and internships to enhance real-world skills. Graduates receive globally recognized certifications, which can significantly boost their employability in the data science field.
The data science syllabus typically covers key areas such as data analysis, machine learning, statistical modeling, and data visualization. It includes programming languages like Python and R, along with hands-on tools for data manipulation and modeling. Additionally, it emphasizes problem-solving using real-world datasets to build practical expertise.
Yes, a free demo class is typically offered for DataMites courses in Madurai. This provides an opportunity to explore the content and teaching approach. It’s a great way to assess if the course aligns with your learning goals.
Yes, there are data science courses available in Madurai that include internship opportunities. These programs offer practical experience alongside theoretical learning. Internships help students apply their skills in real-world projects and enhance their career prospects.
The DataMites Data Science course fee in Madurai depends on the selected learning mode. The live virtual training is offered at INR 60,000, the blended learning option is priced at INR 35,000, and classroom training is available for INR 65,000. For the most up-to-date and accurate fee details, it is recommended to connect directly with the Madurai center.
Yes, EMI options are available for the data science course. These flexible payment plans allow students to pay in installments. Please inquire directly with the provider for more details on specific terms and conditions.
DataMites provides a 100% money-back guarantee if you request a refund within one week of the batch start date and have attended at least two sessions during that period. Refunds are not available after six months from the course enrollment date. To initiate a refund, please email care@datamites.com from your registered email address.
The data science course spans 8 months, offering a comprehensive learning experience. It includes a total of 700 hours of instruction and hands-on practice. The program is designed to provide in-depth knowledge and practical skills in data science.
Yes, course certification is provided upon successful completion, accredited by IABAC and NASSCOM FutureSkills. This certification is recognized within the industry. To receive the certificate, all course requirements must be met.
Choosing this course offers a comprehensive curriculum designed to cover all essential data science topics with hands-on experience. You will benefit from expert guidance and flexible learning options tailored to fit various schedules. Additionally, the course is structured to help you gain practical skills that are highly valued in the industry.
DataMites Madurai is conveniently accessible from key areas such as Anna Nagar (625020) and KK Nagar (625020), which are major residential and commercial hubs, along with Tallakulam (625002), known for its educational institutions. Neighborhoods like Simmakkal (625001), Koodal Nagar (625018), and Arappalayam (625016) provide easy access to training centers. The branch is also well connected to nearby localities including K. Pudur (625007), Thiruparankundram (625005), Palanganatham (625003), Villapuram (625012), and Melur (625106), ensuring smooth connectivity for students and professionals enrolling in DataMites courses.
The course includes 20 capstone projects and 1 client project, providing hands-on experience. These projects allow you to apply your learning in real-world scenarios. You’ll gain valuable exposure to industry-relevant work through these practical assignments.
Various payment methods are accepted, including credit/debit cards, net banking, PayPal, cash, and cheque. For more flexible payment options or assistance with installment plans, you can contact service@datamites.com or call 1800-313-3434. All transactions are processed securely for your convenience.
The Flexi-Pass offers learners a 3-month period to attend training sessions at their preferred times. It allows for revisiting topics, clearing doubts, and enhancing understanding. This flexible learning option ensures ongoing assistance and better retention of key concepts.
The trainers at DataMites Madurai are experienced industry professionals with deep expertise in data science. They provide comprehensive instruction and personalized mentorship to support each student’s learning journey.
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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