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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
After completing a data analyst course, career options include Data Analyst, Business Analyst, Data Visualization Specialist, Financial Analyst, and Marketing Analyst, with opportunities across IT, finance, healthcare, and e-commerce sectors.
India’s growing analytics market and demand for skilled professionals make data analyst courses highly valuable. With affordable training, global opportunities, and rising salaries, pursuing this course in India ensures a strong career foundation.
Certified Data Analyst courses in Mumbai typically range from 3 to 6 months. Some institutes also offer flexible weekend or fast-track options, allowing both students and working professionals to complete the program conveniently.
Data Analyst course fees in Mumbai usually range from INR 40,000 to INR 2,50,000 depending on the institute, curriculum, and inclusion of internships or placement support. EMI and scholarship options are also available at reputed institutes.
Look for data analyst institutes with IABAC-accredited courses, expert mentors, live projects, internship opportunities, and placement support. Reviews, alumni feedback, and offline centers in Mumbai also help identify the most trusted training provider.
Mumbai, India’s financial hub, drives demand for data analysts in BFSI, IT, healthcare, and retail, offering abundant career opportunities. According to Grand View Research, the global data analytics market is expected to rise from USD 69.54 billion in 2024 to USD 329.8 billion by 2030.
According to Glassdoor, Data Analysts in Mumbai earn an average salary of INR 5–8 LPA at entry-level, INR 8–12 LPA for mid-level, and INR 12–18 LPA for experienced professionals. Salaries vary based on skills, industry, and experience level.
Graduates from any stream, including commerce, science, or arts, can pursue the course. Basic knowledge of mathematics, statistics, and logical thinking is preferred, but non-technical professionals can also transition with proper training.
Key data analyst tools include Excel for data handling, SQL for database management, Python/R for analytics, and Tableau or Power BI for visualization. These tools help analysts clean, process, and interpret large datasets efficiently.
Start by enrolling in a certified course, mastering tools like SQL, Excel, Python, and Power BI. Gain practical experience through projects and internships, then apply for entry-level roles in Mumbai’s IT, BFSI, and consulting firms.
SQL is vital as it helps analysts query, manage, and manipulate large datasets from relational databases. It enables quick extraction of insights, making it a core skill for anyone pursuing a data analyst career.
Data Analytics focuses on interpreting existing data for decisions, while Data Science involves predictive modeling and AI. Beginners often start with Data Analytics, while Data Science suits those aiming for advanced roles in ML and AI.
Top IT companies hiring data analysts in Mumbai include TCS, Infosys, Accenture, Capgemini, Wipro, JPMorgan Chase, Morgan Stanley, ICICI Bank, and Reliance. Startups and fintech firms also offer dynamic analytics opportunities.
The syllabus covers Excel, SQL, Python/R, Statistics, Data Visualization (Tableau/Power BI), Data Cleaning, Business Intelligence, and real-world case studies. Hands-on projects ensure learners gain both technical and analytical skills.
The best data analyst courses are industry-recognized certifications like IABAC-accredited Data Analyst programs. Institutes offering live projects, internships, expert faculty, and placement support in Mumbai ensure job-ready skills and career growth.
Build strong skills in Excel, SQL, Python, and visualization tools. Create a portfolio with real projects, apply through job portals, LinkedIn, and institute placement cells like DataMites, which connects students with top internship opportunities.
A data analyst needs skills in SQL, Excel, Python/R, and visualization tools like Tableau or Power BI. Strong statistical knowledge, critical thinking, and business acumen are essential for analyzing data and presenting insights effectively to drive data-backed decisions.
Data analysts work on projects like sales forecasting, customer behavior analysis, fraud detection, and performance tracking. They also build dashboards, clean datasets, and create reports to help companies optimize processes, improve revenue, and enhance decision-making strategies.
AI can automate repetitive tasks like data cleaning and reporting, but it cannot replace the human judgment, contextual understanding, and business problem-solving that data analysts provide. Instead, AI empowers analysts by enhancing their efficiency and accuracy.
Yes, many institutes offer flexible part-time and weekend courses. These allow working professionals and students to upskill without affecting their jobs or studies, while still gaining hands-on training, internships, and certification opportunities.
DataMites is among the best institutes in India for data analyst training. With IABAC accreditation, industry-expert mentors, live projects, internships, placement support, and offline centres, it offers career-ready training for both freshers and professionals.
The IABAC-certified Data Analyst certification is highly recognized in India and globally. It validates core skills in SQL, Python, data visualization, and statistics, boosting credibility and employability for candidates aiming for data-driven roles across industries.
Yes, you can learn data analyst basics in 3 months with focused training. Short-term programs cover Excel, SQL, Python, and Power BI/Tableau. However, mastering advanced skills and gaining real-world experience through projects may take longer for job readiness.
Data analyst courses are highly relevant as companies rely on data-driven decisions. With India’s analytics market growing rapidly, trained analysts are in demand across IT, finance, healthcare, retail, and more, making it one of the most promising career paths.
Yes, experienced data analysts with advanced skills in SQL, Python, and machine learning can earn INR 1 lakh per month. Salaries vary by company, location, and expertise, but professionals in top firms or leadership roles often cross this benchmark in India.
DataMites is a top choice in Mumbai due to its IABAC and NASSCOM FutureSkills-accredited curriculum, expert trainers, hands-on projects, internships, and placement support. Its flexible learning modes and industry relevance make it ideal for aspiring data analysts.
Yes. DataMites offers Certified Data Analyst Course with internships Course in Mumbai, helping students gain real-world exposure, strengthen practical skills, and boost their chances of securing jobs in top companies.
Yes. DataMites provides flexible EMI options, making it easier for students and professionals in Mumbai to pursue the Certified Data Analyst Course without financial burden.
The Certified Data Analyst Course fees at DataMites in Mumbai typically range from INR 40,000 to INR 70,000, depending on the course format. For the latest offers and batch-specific pricing, contact the institute directly..
DataMites offers a 100% money-back guarantee for refund requests made within one week of the course start date, provided at least two sessions are attended. Refunds are unavailable after six months or if over 30% of the material has been accessed. To request a refund, email care@datamites.com from your registered address.
Yes. DataMites provides a Certified Data Analyst Course with placements assistance including job referrals, resume building, and interview preparation, ensuring students in Mumbai are career-ready upon completing the course.
Students receive comprehensive learning materials including study guides, case studies, datasets, recorded sessions, and project access, ensuring both theoretical knowledge and practical exposure.
DataMites Mumbai centre is strategically located in Andheri East, ensuring easy accessibility for students across the city. DataMites institute in Pune is situated at 10th Floor, Crescent Plaza, Teli Gali, Bima Nagar, Andheri East, Mumbai, Maharashtra - 400053.
The course is taught by industry experts with strong academic backgrounds and years of experience in analytics. Trainers guide students through practical projects and real-world case studies.
Yes. The program includes live projects and case studies that allow students to apply analytics tools in real-world scenarios, ensuring job-ready skills.
The course duration typically ranges from 4 to 6 months, depending on the chosen learning mode. It combines theory, hands-on projects, and internship opportunities.
DataMites offers flexible payment options, including credit/debit cards, net banking, UPI, EMI, and wallet payments. This ensures learners can choose a convenient method, making quality data analytics education accessible to all.
Yes, DataMites provides flexibility to switch from offline to online classes in Mumbai if needed. This helps learners balance personal and professional commitments while continuing their journey toward becoming a skilled Data Analyst seamlessly.
Enrollment is simple visit the official DataMites website, choose the Data Analyst course in Mumbai, fill out your details, and proceed with payment. You can also contact the Mumbai centre for direct assistance and offline registration.
After completing the course, learners receive an IABAC-accredited Data Analyst certification. This globally recognized credential adds credibility, enhancing career prospects in India and abroad with strong validation of skills.
The Flexi Pass at DataMites allows learners to access multiple sessions within three months. It ensures flexibility to re-attend classes, clarify doubts, and strengthen understanding, making learning adaptive and student-friendly.
Learners gain expertise in Excel, SQL, Python, Tableau, and Power BI, along with skills in data cleaning, visualization, and statistical analysis. The program also builds problem-solving and decision-making abilities essential for analysts.
DataMites currently has one offline centre in Mumbai, located at Andheri East. The strategic location ensures easy accessibility for students across the city, supported by expert mentors and placement guidance.
Yes, DataMites ensures learners never miss out. If you skip a session, you can re-attend classes with the Flexi Pass or access recorded sessions, ensuring consistent learning and progress without disruption.
Yes, DataMites offers free demo classes where you can experience the teaching style, curriculum, and learning environment before enrolling. This helps you make an informed decision with complete confidence.
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.