4.8 (17,600) reviews 110,000
55,451*
NO COST EMI : 9,242 p.m for 6 Months
*Offer valid till 22nd June 2025
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
The entire training includes real-world projects and highly valuable case studies.
IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.
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
Anyone interested in data analysis can enroll in a DataMites Data Analyst course. No strict prerequisites are required, though a background in mathematics or programming is beneficial. The course caters to both beginners and those looking to enhance their skills. Flexible payment options are available.
To study data analytics effectively, you need strong analytical skills to interpret data, proficiency in programming languages like Python or R, and a solid grasp of statistical methods. Familiarity with data visualization tools like Tableau or Power BI is also essential for presenting findings clearly.
A Data Analyst course provides training in interpreting complex data to help make informed business decisions. It typically covers data collection, analysis techniques, and data visualization tools. The course aims to equip individuals with the skills needed to extract actionable insights from data.
A Data Analyst collects, processes, and performs statistical analyses on data to help organizations make informed decisions. They create reports and visualizations to communicate their findings clearly. Their work often involves identifying trends, patterns, and insights to support business strategies.
No, Coding is not strictly required for a data analyst, but it is highly beneficial. Proficiency in languages like Python or SQL can enhance data manipulation and analysis skills. Familiarity with coding can lead to more efficient data processing and insightful analysis.
Yes, many successful data analysts come from non-engineering backgrounds, provided they develop the required analytical and technical skills.
In Chennai, data analytics trends are focusing on integrating AI and machine learning for deeper insights, increasing demand for data-driven decision-making across industries, and prioritizing real-time data processing and visualization to boost operational efficiency.
The average salary for a data analyst in chennai is ₹4L - ₹8L per annum according to Glassdoor reports. Entry-level positions may offer a lower starting salary, while experienced analysts tend to command higher earnings.
A data analyst course in Chennai typically spans from 4 to 12 months, depending on the program's depth and intensity. Courses may offer flexible schedules, including part-time and full-time options. The duration can vary based on the institution and course content.
Yes, freshers can enroll in a data analyst course in Chennai. Many institutions offer beginner-level courses designed for those new to the field. It's advisable to check the course prerequisites and content to ensure it aligns with your career goals.
In Chennai, Data Analysts can explore careers in IT, finance, healthcare, and retail. They analyze data to drive business decisions, optimize operations, and enhance customer experiences. The city's expanding tech industry offers opportunities in both established firms and startups.
To excel in data analyst courses in Chennai, choose a reputable institute that offers internships and practical experience. Opt for programs with a thorough curriculum, skilled instructors, and job placement support. This approach ensures a comprehensive and effective learning journey.
Yes, it is possible to become a data analyst within a year with focused effort. Enrolling in intensive courses, gaining practical experience through projects, and developing skills in data analysis tools can accelerate the process. Consistent dedication and learning are key to achieving this goal.
Python is a powerful tool for data analysis, providing a broad range of libraries and functionalities. However, combining it with other skills and tools, such as SQL or data visualization platforms, enhances overall effectiveness and versatility.
Yes, you can take a Data Analyst course without a technical background. Many programs are designed for beginners and cover fundamental concepts. They often include support and resources to help you build the necessary skills.
The duration of a Data Analyst course typically ranges from 40 to 200 hours, depending on the program's depth and complexity. Some courses offer intensive, short-term options, while others provide comprehensive, extended training. The exact number of hours will vary based on the institution and course structure.
Yes, individuals without prior data experience can join a Data Analyst course. These courses often start with foundational concepts and progressively cover more advanced topics. It’s designed to accommodate beginners and provide the necessary skills for the field.
Learning data analysis in Chennai is valuable because of the city's expanding tech industry and rising demand for data-driven decisions. It equips professionals with skills to boost their careers across various sectors. Additionally, Chennai offers accessible educational institutions and training programs for aspiring analysts.
Yes, you can pursue data analysis after completing your 12th standard with PCB. Data analysis programs are open to students from various academic backgrounds, including those with a science focus.
Learning data analytics in Chennai is crucial due to the city's growing tech and business sectors. It equips professionals with skills to make data-driven decisions, enhancing their career prospects. Additionally, it supports local industries in optimizing operations and gaining competitive advantages.
To sign up for the DataMites Certified Data Analyst course in Chennai, visit the DataMites website and fill out the inquiry form. Alternatively, you can contact our admissions team directly or visit our Bangalore center for more information. Enrollment is available both online and in person.
After completing the DataMites Data Analyst course in Chennai, you will receive the Certified Data Analyst certification. Accredited by IABAC and NASSCOM®, this credential highlights your data analysis expertise and can enhance your career prospects.
Yes, DataMites offers job placement assistance for our Data Analyst course in Chennai. We will provide support to help students find relevant job opportunities in the field. This assistance includes resume building, interview preparation, and job search guidance.
With the Flexi-Pass for Data Analytics Certification Training in Chennai, participants can access relevant sessions for three months, allowing them to address questions and make revisions as needed.
DataMites offers a 100% money-back guarantee if you request a refund within one week of the course start date and attend at least 2 sessions in the first week. Refunds are not available after 6 months or if over 30% of the material has been accessed. To request a refund, email care@datamites.com from your registered email.
Yes, DataMites offers demo classes for the Data Analyst course in Chennai. You can attend a demo session to get a preview of the course content and teaching style before enrolling. Please contact our support team for scheduling and availability.
The Data Analyst course at DataMites covers foundational data analysis concepts, including data cleaning, visualization, and statistical analysis. It also provides training in tools like Excel, SQL, and Python for effective data manipulation and reporting. The course aims to equip participants with skills necessary for analyzing and interpreting complex data sets.
Yes, you can switch from an offline Data Analyst course to an online format with DataMites in Chennai. Please contact our support team to discuss the available options and ensure a smooth transition. We will assist you in updating your course format according to your preferences.
Yes, you can attend make-up sessions if you miss a class at DataMites. We offer options to help you catch up on missed content. Please contact our support team to arrange a suitable session.
When you enroll in the Data Analyst course at DataMites in Chennai, you'll receive comprehensive course materials, including detailed lecture notes, practical assignments, and access to industry-relevant case studies. You'll also get hands-on experience with data analysis tools and software. Additionally, you'll have access to online resources and support to enhance your learning journey.
DataMites offers live projects as part of our Data Analyst course in Chennai. This practical component allows students to gain hands-on experience with real-world data. For more detailed information, you can contact DataMites directly.
Yes, DataMites provides EMI options for our Data Analyst training in Chennai, allowing payment in convenient monthly installments. For more information, contact our admissions team or visit our website.
The DataMites Data Analyst course covers essential topics including data visualization, statistical analysis, and data management. The curriculum includes hands-on training with tools like Excel, SQL, and Python. It also provides real-world project experience to ensure practical understanding.
The cost of the DataMites Data Analyst course in Chennai varies based on the program's duration and features. Typically, fees range from ?25,000 to ?1,00,000. For the most accurate and up-to-date pricing, please consult the DataMites website or contact our local office.
DataMites' Data Analyst course in Chennai does not typically include an internship as part of the program. However, We will provide comprehensive training and career support to help students secure relevant opportunities. For specific details on internships or placements, please contact DataMites directly.
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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