4.8 (17,100) reviews 110,000
77,726*
NO COST EMI : 12,954 p.m for 6 Months
*Offer valid till 24th November 2024
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 Data Analyst course. While there are no strict prerequisites, a background in mathematics or programming can be beneficial. The course is designed to accommodate both beginners and those looking to advance their skills, starting from the fundamentals. Various payment options are available to suit different needs.
The best course for a data analyst in Bangalore is the Certified Data Analyst course. It includes live projects, internships, and placement assistance, providing practical experience and support for job placement. This comprehensive approach helps you build skills and advance your career effectively.
A Data Analyst course teaches individuals how to collect, process, and analyze data to extract insights that inform decision-making. It covers essential tools like Excel, SQL, and Python, along with data visualization techniques. Such courses are designed to build skills for careers in data analytics.
A data analyst is a professional who collects, processes, and analyzes data to help businesses make better decisions. They find patterns, trends, and insights from data to solve problems. Their work helps companies improve efficiency and reach their goals.
No, coding is not strictly necessary for a career as a data analyst. Many data analysts successfully use Excel and tools like Power BI or Tableau for data analysis and visualization. Certified Data Analyst course is a no-coding course, focusing on practical skills and tools used in the industry.
Yes, you can switch to a data analyst career with a non-engineering background. Key skills like data analysis, statistical knowledge, and proficiency in tools like Excel and Python are more important than your previous field.
In Bangalore, data analysts are focusing on advanced analytics with AI and machine learning, and there's a growing demand for skills in big data technologies and real-time data processing. The city’s tech hub also emphasizes upskilling through data science certifications and internship experience.
The average salary for a data analyst in Bangalore is typically around ₹6 to ₹8 lakhs per annum. This can vary based on experience, skills, and the company. Entry-level positions might start lower, while experienced analysts could earn more.
The average salary for a data analyst in Bangalore is typically around ₹6 to ₹8 lakhs per annum. This can vary based on experience, skills, and the company. Entry-level positions might start lower, while experienced analysts could earn more.
Bangalore offers top courses for Data Analysts that cover essential skills like data visualization, statistical analysis, and machine learning. Many programs provide hands-on projects, internships, and placement assistance to help you secure a job. DataMites, a global leader with 10 years of trust, offers comprehensive support for internships and placements.
In Bangalore, data analysts have a wide scope with growing opportunities across tech, finance, healthcare, and retail due to the city’s vibrant tech industry and numerous startups.
The best way to learn data analyst courses in Bangalore is to enroll in a reputable training institute offering internships and practical insights. Look for courses with comprehensive curriculums, experienced instructors, and job assistance programs to ensure a thorough and effective learning experience.
Yes, data analysis is a high-demand field due to the growing need for data-driven decision-making across industries. Companies rely on data analysts to interpret data and provide insights. This has created many job opportunities in the field.
Learning data analysis in Bangalore is very useful due to the city's strong demand for data professionals across various industries. It opens up many career opportunities and growth potential in a thriving job market.
It usually takes around 6 months to 1 year to become a data analyst in Bangalore, depending on the course and how much time you can commit. Some people might take longer if they study part-time or need additional skills.
For data analyst courses in Bangalore, there are no formal prerequisites as the training starts from the basics. However, having some background in programming, databases, data structures, mathematics, and algorithms can be helpful.
The Data Analyst course covers foundational topics like data collection, cleaning, and exploratory analysis. It includes training in statistical methods, data visualization, and advanced analytics techniques. The curriculum also features hands-on projects to apply these skills in real-world scenarios.
Yes, a data analyst role is generally considered a high-paying job in Bangalore, especially with the city's growing tech industry and demand for skilled professionals. Salaries can be competitive and increase with experience and expertise.
Yes, a fresher can become a data analyst by completing relevant courses and gaining practical skills through internships or projects. Many entry-level positions are available for those with the right training and enthusiasm.
A data analyst focuses on interpreting data to provide actionable insights and support decision-making using tools like Excel or SQL. A data scientist, on the other hand, uses advanced techniques, including machine learning and statistical modeling, to build predictive models and uncover deeper insights from data
To enroll in the DataMites Certified Data Analyst course in Bangalore, visit the DataMites website and fill out the inquiry form. You can also contact our admissions team directly or visit our Bangalore center for more details. Enrollment can be completed online or in person.
To enroll in DataMites' Data Analyst course in Bangalore, visit the DataMites website and fill out the inquiry form or contact our admissions team directly. You can also visit our Bangalore center for detailed information and complete the registration process. Ensure you review the course details and schedule before enrolling.
Yes, DataMites offers a Data Analyst course in Bangalore with placement assistance. After completing the course, our dedicated Placement Assistance Team (PAT) will support you with job search and interview preparation. This ensures you have the best chance of securing a position in the field.
Yes, DataMites offers a Data Analyst course in Bangalore that includes internship opportunities. This hands-on experience helps students apply their skills in real-world settings and enhance their job prospects. For more details, check with DataMites directly or visit our website.
Yes, DataMites offers a Data Analyst course in Bangalore that includes live projects. This hands-on approach helps you apply what you've learned in real-world scenarios. It’s a great way to gain practical experience while studying.
The trainers for DataMites Data Analyst course in Bangalore are experienced industry professionals with expertise in data analytics. We bring practical knowledge and real-world experience to the training. The trainers are also skilled in using various tools and techniques relevant to the field.
Yes, you can switch from an offline to an online Data Analyst course at DataMites in Bangalore. Contact our support team to handle the transition smoothly.
Yes, you can switch from an offline Data Analyst course to an online course with DataMites. This allows you to continue your studies from any city in India. Contact DataMites for assistance with the transition.
During the Data Analyst course at DataMites in Bangalore, you will receive comprehensive study materials including textbooks, online resources, and access to practical exercises. The course also provides project work and case studies to enhance your learning experience.
With the Flexi-Pass for Data Analytics Certification Training in Bangalore, participants can attend relevant sessions for three months to address any questions or revisions they need.
Yes, DataMites offers EMI options for their Data Analyst training in Bangalore. This allows you to pay for the course in manageable monthly installments. For details, you can contact our admissions team or visit our website.
After completing the DataMites Data Analyst course in Bangalore, you will receive the Certified Data Analyst certification. This certification is accredited by IABAC and NASSCOM®. It demonstrates your expertise in data analysis and can boost your career opportunities.
The fees for the DataMites Certified Data Analyst course in Bangalore typically range from ?25,000 to ?1,00,000. The exact amount can vary based on any ongoing promotions or additional features included in the course. For the most accurate and current fee details, it's best to contact DataMites counselor.
DataMites offers comprehensive support during and after the Data Analyst course in Bangalore, including internships, project work, and mentorship. We also provide career guidance, job placement assistance, and access to a network of industry professionals to help you succeed in your career.
Pursuing data analyst training from DataMites in Bangalore offers internships with real-world projects and expert instructors. The certification is accredited by IABAC and NASSCOM®, adding significant value to your resume. This training also provides job assistance to help you kickstart your career.
Yes, DataMites offers demo classes for their data analyst courses in Bangalore. These sessions let you experience the course content and teaching style before enrolling. It helps you decide if the program meets your learning and career goals.
Yes, if you miss a session at DataMites, you can usually attend a make-up class or access recorded sessions. The institute provides options to ensure you don't fall behind. Check with your course coordinator for specific details.
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 will not be issued after 6 months or if more than 30% of the material has been accessed. Send refund requests to care@datamites.com from your registered email. Please refer to our refund policy.
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.
Bootcamp
+1-628-228-6062 
Drop A Query
