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
Eligibility for a data analyst course generally includes a basic understanding of mathematics and statistics. Most programs accept candidates with a range of backgrounds, from recent graduates to working professionals. Some courses might require a bachelor’s degree, while others might be open to individuals with relevant skills or experience.
For aspiring data analysts in Gurgaon , the "Data Analytics Certification Program" by the Institute of Analytics is highly recommended. It offers comprehensive training in data visualization, statistical analysis, and practical tools, ensuring robust skills for professional success.
A Data Analyst course teaches how to collect, process, and analyze data to make informed business decisions. The curriculum typically covers data cleaning, statistical analysis, data visualization, and using tools like Excel, SQL, and Python.
A Data Analyst interprets data to help organizations make data-driven decisions. They analyze trends, create reports, and provide insights based on data to guide strategic planning and operational improvements.
While not always mandatory, coding skills are highly beneficial for data analysts. Proficiency in programming languages like Python or R can enhance data manipulation and analysis capabilities, though some roles may focus more on data visualization and reporting.
Yes, switching to a data analyst career with a non-engineering background is possible. Many data analyst courses are designed for individuals from diverse backgrounds, provided you have an interest in data and are willing to learn necessary skills.
Recent trends include increasing demand for skills in machine learning and artificial intelligence, data visualization advancements, and the integration of big data technologies. There’s also a growing emphasis on real-time data analysis and decision-making.
The average salary for a data analyst in Gurgaon ranges from ₹4,00,000 to ₹8,00,000 per year, depending on experience, skills, and the organization. Senior positions or those requiring specialized skills may offer higher compensation.
The duration of data analyst courses in Gurgaon typically ranges from 4 to 12 months. Some programs may offer accelerated options or part-time schedules, depending on the provider and course format.
To study data analytics, you need strong analytical skills, proficiency in statistical methods, knowledge of programming languages like Python or R, familiarity with data visualization tools, and the ability to interpret data trends to make informed decisions.
The best way to learn is by choosing a reputable course from a recognized institution, combining theoretical knowledge with practical experience. Online courses, bootcamps, and hands-on projects can also be effective.
You can train yourself by enrolling in a structured course, practicing with real datasets, and gaining hands-on experience. Online tutorials, workshops, and networking with professionals in Gurgaon can also be helpful.
Yes, there is a high demand for data analysts across various industries due to the increasing need for data-driven decision-making. This trend is expected to continue as organizations increasingly rely on data for strategic planning.
The best education path often involves obtaining a relevant degree or certification in data analytics, supplemented by hands-on experience through internships or projects. Specialized courses and workshops can further enhance your skills.
Yes, non-technical graduates in Gurgaon can enroll in data analyst courses. Many programs are designed to accommodate diverse educational backgrounds and focus on building essential data analysis skills from the ground up.
Certified data analyst course fees in Gurgaon typically range from ₹25,000 to ₹1,50,000, depending on the institution and course duration. Some courses may offer flexible payment options or discounts.
A typical syllabus includes data cleaning, exploratory data analysis, statistical methods, data visualization, and tools like Excel, SQL, and Python. Advanced courses may also cover machine learning and big data techniques.
Notable institutions in Gurgaon offering quality data analyst courses include Datamites, renowned for their comprehensive programs and industry-relevant training. These providers deliver in-depth education tailored to current industry standards and practices.
To obtain certification, enroll in a recognized data analyst course that offers certification upon completion. The certification process typically involves completing the course, passing exams or assessments, and meeting any other program requirements.
The curriculum typically includes data wrangling, statistical analysis, data visualization, and proficiency in tools like Excel, SQL, Python, and R. For detailed syllabus, please refer to DataMites’ course brochure.
Yes, DataMites provides placement assistance for the Data Analyst course in Gurgaon , including resume building, interview preparation, and job referrals.
Yes, DataMites offers a Data Analyst course with internship opportunities in Gurgaon . This program provides comprehensive training in data analysis and includes practical experience through internships to enhance real-world skills and job readiness.
Yes, DataMites offers a Data Analyst course in Gurgaon that includes live projects. The program is designed to provide practical experience and hands-on training to enhance skills and ensure proficiency in real-world data analysis.
The trainers at DataMites are seasoned professionals with extensive experience in data analysis and related fields. Detailed profiles of the instructors are available on the DataMites website or course materials. Ashok Veda, CEO of Rubixe, serves as the lead mentor, bringing valuable expertise to the program.
Yes, DataMites offers demo classes for the Data Analyst course. These sessions allow prospective students to experience the course content and teaching methodology before making a commitment. You can inquire about available demo sessions by contacting our admissions team directly.
If you miss a Datamites session, you may still attend subsequent classes. However, it is recommended to review any missed content and reach out to instructors for additional support to ensure you stay on track with the course material.
DataMites' Data Analyst course in Gurgaon provides comprehensive study materials including textbooks, interactive e-learning modules, case studies, practical exercises, and access to industry-standard tools. Participants also receive mentorship and support through webinars and discussion forums to enhance our learning experience.
The Flexi-Pass at DataMites provides a flexible training schedule for data science courses, enabling participants to attend any class session within a three-month period. This option offers the convenience of aligning learning with personal or professional schedules.
Yes, DataMites provides EMI options for the Data Analyst Training in Gurgaon , allowing candidates to pay in installments. This flexible payment plan is designed to make the training more accessible and manageable for prospective learners.
Upon completing the Data Analyst course with DataMites in Bangalore, you will earn the Certified Data Analyst (CDA) certification. Accredited by IABAC and NASSCOM®, this credential showcases your data analysis expertise and can enhance your career prospects.
The fees for the DataMites Certified Data Analyst course in Bangalore generally range from ?25,000 to ?1,00,000. The final amount may vary depending on current promotions or additional features offered. For the most up-to-date and precise fee information, it is advisable to contact a DataMites counselor.
DataMites provides comprehensive support during and after the Data Analyst course in Gurgaon , including personalized mentorship, hands-on projects, job placement assistance, resume building, and access to a robust alumni network to ensure ongoing career development and success.
DataMites provides a 100% money-back guarantee if a refund request is made within one week of the course start date, provided you attend at least two sessions in the first week. Refunds are not available after six months or if over 30% of the material has been accessed. To request a refund, email care@datamites.com from your registered email. Please review our refund policy for more details.
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.