4.9 (45,512) reviews 110,000
55,451*
NO COST EMI : 9,242 p.m for 6 Months
*Offer valid till 14th December 2025
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
MODULE 1: DATA ANALYSIS FOUNDATION
• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain
MODULE 2: CLASSIFICATION OF ANALYTICS
• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics
MODULE 3: CRIP-DM Model
• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling, Evaluation, Deploying,Monitoring
MODULE 4: UNIVARIATE DATA ANALYSIS
• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.
MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS
• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot
MODULE 6: BI-VARIATE DATA ANALYSIS
• Scatter Plots
• Regression Analysis
• Correlation Coefficients
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
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 1: COMPARISION AND CORRELATION ANALYSIS
• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis
MODULE 2: VARIANCE AND FREQUENCY ANALYSIS
• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for Frequency Analysis
MODULE 3: RANKING ANALYSIS
• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis
MODULE 4: BREAK EVEN ANALYSIS
• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Manufacturing
MODULE 5: PARETO (80/20 RULE) ANALSYSIS
• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• Hands-on case study: Pareto Analysis
MODULE 6: Time Series and Trend Analysis
• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
MODULE 7: DATA ANALYSIS BUSINESS REPORTING
• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
MODULE 1: DATA ANALYTICS FOUNDATION
• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity
MODULE 2: OPTIMIZATION MODELS
• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION
• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel
MODULE 4: DECISION MODELING
• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
MODULE 4: ML ALGO: KNN
• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering
MODULE 6: ML ALGO: DECISION TREE
• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool
MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Hands-on: SVM with ML Tool
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
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 Functions: 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
• MongoDB data management
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!
Career options after a data analytics course include Data Analyst, Business Analyst, Marketing Analyst, Financial Analyst, HR Analyst, Operations Analyst, BI Analyst, and Product Analyst, offering strong opportunities across IT, BFSI, healthcare, retail, and e-commerce sectors.
Choosing a data analytics course in India provides access to affordable training, expert mentors, job-oriented curriculum, strong placement support, and rising industry demand. India's tech ecosystem makes it an ideal place to gain practical analytics skills for a fast-growing career.
The course duration for a data analytics course in India typically ranges from 3 to 6 months. Duration varies by institute, mode of learning, and curriculum. Most programs include hands-on tool training, real-world projects, and mentor-led sessions to build industry-ready skills.
The data analytics course fee in India ranges from INR 20,000 to INR 1,20,000 depending on institute reputation, training mode, certifications, and projects included. Premium programs may cost more but offer internships, tools training, and placement support for better career outcomes.
To find the best institute for a data analytics course in India, review curriculum depth, trainer experience, placement support, certifications, student reviews, hands-on projects, and flexibility of online or offline learning. Choose institutes offering strong industry-driven training.
The demand for data analytics courses in India is rising as industries like IT, BFSI, healthcare, retail, and e-commerce increasingly rely on data-driven decisions. With growing job opportunities for skilled analysts, the field is expanding rapidly. Globally, the data analytics market is expected to surge from USD 69.40 billion in 2024 to USD 877.12 billion by 2035, at a 25.93% CAGR.
The salary of a data analyst in India typically ranges from INR 4 LPA to INR 10 LPA (Source: Glassdoor), depending on experience, tools expertise, and location. Cities like Bangalore, Pune, and Hyderabad offer higher packages due to strong demand in IT, finance, e-commerce, and consulting domains.
Eligible candidates for a data analytics course include students, graduates, and working professionals from any educational background. Basic analytical skills, curiosity, and willingness to learn tools like Excel, SQL, and Python are sufficient to begin a data analytics career.
Essential tools used by data analysts include Excel, SQL, Python, Tableau, Power BI, MySQL, and Google Analytics. These tools support data cleaning, querying, visualization, and reporting, helping analysts generate insights and solve business problems effectively.
To start a data analytics career in India, learn essential tools like Excel, SQL, Python, and visualization platforms. Join a structured course, build real-world projects, create a portfolio, practice interviews, and apply for internships or entry-level roles across industries.
Yes, a data analytics course is suitable for non-technical students. Courses begin with basics and gradually build skills in Excel, SQL, and visualization. With practical projects and guided mentorship, non-tech learners can confidently transition into analytics roles in various industries.
SQL is essential for data analytics because it helps analysts extract, filter, clean, and manipulate data stored in databases. Most companies rely on SQL-based systems, making SQL a foundational skill required for analysis, reporting, dashboard building, and decision-making.
The future of data analytics is strong as industries adopt AI, automation, and cloud technologies. Companies increasingly rely on data-driven strategies, creating more demand for skilled analysts. Careers in business analytics, BI, and predictive analytics will continue to grow.
A data analytics course syllabus covers Excel, SQL, Python, statistics, MySQL, data cleaning, data visualization with Tableau or Power BI, dashboards, business analytics concepts, and real-world projects. The curriculum is designed to build strong analytical and problem-solving skills.
Choosing between data science or data analytics depends on career goals. Data analytics focuses on insights and reporting, while data science involves advanced modeling and machine learning. Analytics suits beginners, whereas data science requires deeper technical and statistical expertise.
The scope of a data analytics career in India is expanding across IT, BFSI, healthcare, retail, logistics, and startups. With companies adopting digital transformation, demand for skilled analysts is rising, offering strong salaries, career stability, and long-term growth opportunities.
Technical skills required to become a data analyst include SQL, Excel, Python, statistics, data cleaning, Tableau, Power BI, database management, and basic business analytics. These skills enable analysts to process data, build dashboards, and deliver meaningful business insights.
Data analysts work on projects like sales forecasting, customer segmentation, HR analytics, financial reporting, market research, operational optimization, and dashboard creation. They analyze datasets, identify trends, and deliver insights that support business decisions and strategy.
Yes, you can pursue a data analytics course on a part-time basis, as many institutes offer weekend, evening, and self-paced online classes. These flexible formats help students and working professionals learn analytics skills while managing existing academic or job commitments.
Beginners can build data analytics projects by selecting simple datasets, performing data cleaning, exploring patterns, creating visualizations, and presenting insights. Tools like Excel, SQL, Python, Tableau, and Power BI make it easy to practice and develop real-world project experience.
The best course to learn data analyst skills includes training in Excel, SQL, Python, Tableau, Power BI, statistics, and real projects. Choose a reputable institute offering industry-aligned curriculum, tools training, internships, and strong placement support for career success.
The best institute for data analytics training in India provides an industry-oriented curriculum, expert trainers, hands-on projects, internships, globally recognized certifications, and placement assistance. Look for institutes with strong reviews and a proven track record of successful learners.
To learn AI for data analytics, start with Python, statistics, and machine learning basics. Enroll in a structured course covering AI-driven tools and models. Practice using real datasets to understand how AI enhances data analysis, automation, forecasting, and business decision-making.
The best course for data analytics covers Excel, SQL, Python, statistics, Tableau, Power BI, and real-world projects. Choose programs offering hands-on learning, mentor support, recognized certifications, and placement assistance to build strong analytics expertise and career readiness.
Data analytics refers to the process of collecting, cleaning, analyzing, and interpreting data to uncover patterns and insights that help organizations make informed decisions. It involves tools like Excel, SQL, Python, and visualization platforms to solve business problems effectively.
DataMites is a top choice for a data analytics course in India due to its expert trainers, hands-on projects, global certifications, flexible learning modes, internships, and strong placement support that help learners build industry-ready analytics skills.
Yes, the DataMites Data analytics Course with internships in India includes internship opportunities where learners work on real datasets to gain practical exposure and build job-ready data analytics experience.
Yes, DataMites provides flexible EMI options for the Data analytics Course in India, allowing students to split the fees into affordable monthly payments, making the program budget-friendly and accessible.
The data analytics course fees at DataMites in India range from INR 40,000 to INR 60,000 depending on course level, learning format, certifications, and internship options included in the program.
Yes, DataMites provides a Data analytics Course with placements in India through its PAT program, offering resume support, mock interviews, and job assistance to help learners secure analytics roles.
The refund policy for canceling the data analytics course at DataMites allows refunds within a specific period based on terms agreed at enrollment, ensuring transparency for learners requesting cancellation.
The course duration for the data analytics course at DataMites is typically 4–6 months, covering live classes, self-paced modules, projects, assessments, and internship opportunities for practical training.
DataMites operates several offline centers in India, including Bangalore, Pune, Chennai, Hyderabad, Mumbai, Coimbatore, Ahmedabad, Kolkata, Noida, and other major cities for classroom-based analytics training.
The DataMites offline training center in Bangalore is located in Marathahalli, Kudlu Gate, and BTM Layout, well-connected areas, offering classroom-based data analytics training with expert guidance and hands-on project sessions.
The DataMites Pune offline training center is situated in a prime commercial location, providing accessible offline data analytics training with modern classrooms and experienced instructors.
The DataMites offline centre in Hyderabad is positioned in Madhapur, offering convenient access to offline data analytics training, interactive sessions, and practical project learning.
You can attend the DataMites offline data analytics course in Coimbatore at Peelamedu local training center, featuring classroom sessions, hands-on practice, and expert-led learning.
DataMites Coimbatore: First floor, 1326/1, Avinashi Rd, Peelamedu, Coimbatore, Tamil Nadu 641004
The DataMites offline training centres in Chennai are located in key educational and IT zones of Guindy, Perungudi, and Anna Nagar, offering easy access to classroom-based data analytics training for students and professionals.
The DataMites Mumbai branch is located in a central business area, providing offline data analytics classes with hands-on learning, expert trainers, and project-based training sessions.
DataMites Andheri East: 10th Floor, Crescent Plaza, Teli Gali, Bima Nagar, Andheri East, Mumbai, Maharashtra 400069
The DataMites offline centre in Ahmedabad is situated in Navrangpura, offering in-person data analytics training with interactive classrooms and practical project work.
DataMites Ahmedabad: 7th Floor, Manor Maxx, SV Desai Marg, Vasant Vihar, Navrangpura, Ahmedabad, Gujarat 380009
DataMites provides extensive materials for the data analytics course in India, including study guides, video recordings, datasets, project templates, case studies, and toolkits for hands-on learning.
Yes, DataMites offers a data analytics course with live projects in India, giving learners real-world exposure through industry datasets, practical assignments, and guided capstone projects.
The DataMites Flexi Pass allows learners to attend extra classes, revisit sessions for revision, and access extended support throughout their data analytics training for better clarity and practice.
DataMites offers multiple payment methods, including UPI, debit/credit cards, net banking, EMI plans, and wallet payments, making enrollment in data analytics courses convenient for all learners.
Yes, DataMites allows students to switch from offline to online data analytics courses, offering flexibility based on learning needs, schedules, or personal preferences without disrupting progress.
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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