4.8 (18,200) reviews 110,000
55,451*
NO COST EMI : 9,242 p.m for 6 Months
*Offer valid till 15th 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
Data Analyst courses are open to recent graduates, career switchers, and skill enhancers. No specific background is required, but basic knowledge of statistics and computer applications can be helpful. Courses cater to all experience levels.
To study data analytics effectively, strong analytical skills and proficiency in statistical software and programming languages like Python or R are crucial. Additionally, a solid grasp of data visualization tools is essential for presenting insights clearly and compellingly.
A Data Analyst course teaches the essentials of data collection, processing, and analysis to extract insights. It covers statistical methods, data visualization, and analytical tools. The goal is to equip learners with skills to interpret data and support decision-making across industries.
A Data Analyst collects, processes, and performs statistical analyses on data to help organizations make informed decisions. They interpret complex datasets and create reports to provide actionable insights. Their role involves identifying trends, patterns, and anomalies to support business strategies.
No, coding knowledge is not strictly necessary to become a data analyst. Many tools and software used in data analysis have user-friendly interfaces that allow for data manipulation without extensive programming skills. However, having some coding knowledge can enhance your analytical capabilities and expand your job opportunities.
Yes, you can transition to a data analyst career without an engineering background. Many data analysts come from various fields, using tools and skills learned through specialized courses. A solid foundation in data analysis, statistics, and tools like Excel or SQL is key.
In Hyderabad, data analysts are harnessing AI and machine learning to boost predictive analytics, emphasizing real-time data processing and visualization for agile decision-making. With increasing regulatory demands and cyber threats, data privacy and security are also gaining significant focus.
The average pay for a data analyst in Hyderabad ranges from ₹5 lakh to ₹10 lakh per annum. According to Glassdoor, this range reflects the varying levels of experience and expertise within the field. Data analysts in this city can expect competitive compensation based on their skills and qualifications.
The Certified Data Analyst course in Hyderabad is highly regarded for its comprehensive curriculum and hands-on training. It equips participants with essential skills in data analysis, visualization, and tools like Excel and SQL. This course is ideal for those looking to build a strong foundation in data analytics.
Becoming a data analyst in Hyderabad usually takes 4 to 12 months, depending on prior experience and training intensity. Enrolling in a comprehensive course or certification can speed up this process. Gaining practical experience through projects or internships is also essential.
Hyderabad's booming tech sector offers a bright future for data analysts, driven by increasing demand for data-driven decisions. Advancements in AI and machine learning further enhance career opportunities and specialization. Data-centric industries' growth guarantees a steady need for skilled professionals.
To excel in data analyst courses in Hyderabad, choose a reputable institute that offers hands-on internships and practical experience. Opt for programs with a robust curriculum, seasoned instructors, and job placement support. This approach ensures a comprehensive and impactful learning journey.
Starting a career as a data analyst at 40 is entirely feasible, especially in a vibrant city like Hyderabad. Many professionals transition into data analytics later in their careers by leveraging prior experience and acquiring relevant skills. With the right training and certifications, age is not a barrier.
DataMites is widely recognized for its comprehensive data analyst training in Hyderabad. Their programs offer practical insights and hands-on experience, making it a strong choice for aspiring data professionals. Additionally, their industry-aligned curriculum and expert instructors enhance the learning experience.
Data analysts must be skilled in programming languages like Python and R for data manipulation and analysis, and SQL for managing and querying databases. Mastery of these tools is essential for effective data-driven decision-making.
Learning data analytics sharpens decision-making by turning data into actionable insights and enhances career prospects across industries. DataMites excels in providing top-notch training for these valuable skills.
Yes, you can study data analysis after completing your 12th standard with PCB. Many data analysis programs and courses are open to students from diverse academic backgrounds. It may be helpful to gain some foundational knowledge in mathematics and statistics to support your studies.
Yes, Hyderabad offers several entry-level opportunities in data analysis for freshers. Companies across various sectors, including IT and finance, frequently seek junior data analysts. Job portals and company career pages are good places to find these positions.
To start a career as a Data Analyst in Hyderabad, begin by acquiring relevant skills through a reputable course or certification in data analytics. Build a strong portfolio by working on real-world projects or internships. Networking with professionals and attending industry events can also enhance your job prospects.
DataMites offers Data Analyst courses both online and offline in Hyderabad. You can choose the mode that best fits your schedule and learning preference. For more details, please visit the DataMites website or contact our support team.
To enroll in the Certified Data Analyst course in Hyderabad with DataMites, visit our website and complete the online registration form. You can also contact our admission team directly for guidance. For additional details, check our course offerings and schedule on our website.
Yes, DataMites offers demo classes for our Data Analyst course. These sessions allow prospective students to experience the course content and teaching style before making a commitment. To schedule a demo class, please contact our support team for availability and details.
Yes, DataMites offers a Data Analyst course with placement assistance in Hyderabad. The program is designed to equip participants with the necessary skills and support for job placement. For more details, please visit our official website or contact our support team.
A Flexi Pass from DataMites provides three months of access to various training sessions. This flexible option allows participants to attend multiple sessions at their convenience. It is designed to enhance learning while accommodating individual schedules.
Yes, DataMites offers Data Analyst classroom training in Hyderabad. The program is designed to provide comprehensive hands-on experience and theoretical knowledge. For specific details on scheduling and enrollment, please visit the DataMites website or contact our support team.
At DataMites, our instructors are seasoned professionals with extensive industry experience. Ashok Veda, CEO of Rubixe, leads as our principal mentor. Each trainer contributes valuable expertise to deliver top-notch education.
The Data Analyst course at DataMites covers core concepts in data analysis, statistical methods, and data visualization. It includes training on tools like Excel, SQL, and Python. Students gain practical experience through hands-on projects and case studies.
DataMites offers a money-back guarantee if you request a refund within one week of the course start date and attend at least two sessions. Refunds are not available after 6 months or if over 30% of the material has been accessed. Submit requests to care@datamites.com from your registered email.
Yes, you can transfer your offline Data Analyst course to another city in India. Please contact DataMites' customer support for assistance with the transfer process. We will provide you with the necessary steps and guidance.
Enrolling in the DataMites Data Analyst course in Hyderabad provides detailed course materials, including lecture notes, practical exercises, and industry case studies. You'll also receive lifetime access to the course portal and benefit from support by experienced instructors and hands-on projects.
Yes, DataMites offers a Data Analyst course in Hyderabad that includes live projects. This course is designed to provide practical experience and hands-on learning. For more details on the curriculum and enrollment, please visit the DataMites website or contact our local office.
Yes, DataMites offers EMI options for our Data Analyst training in Hyderabad, allowing for manageable monthly payments. For more information, contact our admissions team or visit our website.
Upon completing the DataMites Data Analyst course in Hyderabad, You will receive the Certified Data Analyst certification, accredited by IABAC and NASSCOM®. This credential highlights your data analysis expertise and enhances your career prospects.
The fees for the DataMites Certified Data Analyst course in Hyderabad generally range from ?25,000 to ?1,00,000. The final amount may vary depending on promotions or additional features. For the most up-to-date information, contact a DataMites counselor directly.
Yes, DataMites offers a Data Analyst course in Hyderabad that includes an internship component. The program combines theoretical knowledge with practical experience to help students gain hands-on skills. For more details on the course and internship, please visit our official website.
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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