4.8 (20,100) reviews 110,000
55,451*
NO COST EMI : 9,242 p.m for 6 Months
*Offer valid till 22nd December 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 with an interest in data analysis can sign up for a Data Analyst course. Typically, the course is suitable for beginners, professionals, or graduates from any field. Basic knowledge of mathematics or programming may be helpful but is not always required.
To study data analysis, you need strong analytical skills to interpret data patterns effectively. Proficiency in statistical tools and programming languages like Python or R is essential for data manipulation. Additionally, a solid understanding of data visualization techniques helps in presenting insights clearly.
A Data Analyst course typically covers data collection, cleaning, and analysis techniques. It includes training on tools like Excel, SQL, and Python, along with data visualization methods. The course also focuses on interpreting data to support business decisions.
A Data Analyst is a professional who collects, processes, and analyzes data to help businesses make informed decisions. They identify patterns, trends, and insights from data to support organizational goals. Their work often involves using tools like Excel, SQL, and data visualization software.
Coding is not always required for a Data Analyst role, but it can be highly beneficial. Many tasks involve using software like Excel, Tableau, or SQL, while coding skills in Python or R can enhance analysis capabilities. The need for coding depends on the job's complexity and tools used.
Yes, you can switch to a Data Analyst career without an engineering background. Many skills required, such as data analysis, statistics, and tools like Excel or SQL, can be learned through courses or training. Practical experience and a strong understanding of data are key to success in this field.
In Pune, data analysis trends are increasingly driven by AI and machine learning for extracting insights from unstructured data, enhancing accuracy and decision-making. Real-time analytics are vital for sectors like telecom and logistics, enabling quick, data-driven actions. Cloud-native platforms and data observability further improve scalability and real-time monitoring.
The average salary of a Data Analyst in Pune typically ranges from ₹5 lakh to ₹8 lakh per annum, according to Glassdoor. Factors such as experience, skills, and industry can influence individual salaries. This range reflects the current job market trends for data analytics professionals in the region.
The duration of a Data Analyst course in Pune varies depending on the program and institution. Typically, courses can range from 4 to 6 months for short-term certifications, while more comprehensive programs, including diplomas, may last up to 12 months. It's advisable to check with individual institutes for specific timelines.
The fees for a certified Data Analyst course in Pune generally range from ₹25,000 to ₹1,50,000, depending on the institute and course duration. It is advisable to check with specific training providers for accurate and up-to-date pricing.
Yes, a Data Analyst role is generally well-compensated in Pune, thanks to the city's booming tech industry and high demand for skilled professionals. Salaries are competitive and tend to rise with experience and expertise.
To learn data analysis effectively in Pune, enroll in a well-regarded training institute that offers internships and hands-on experience. Choose programs with extensive curriculums, knowledgeable instructors, and strong job placement support to ensure a comprehensive and impactful learning journey.
A career as a Data Analyst in Pune offers ample growth opportunities due to the city's thriving tech industry and burgeoning startup ecosystem. Additionally, Pune's competitive job market provides attractive salary packages and career advancement potential.
A Data Analyst course typically covers data collection and cleaning techniques, statistical analysis, and data visualization. It also includes training on using analytical tools and software to derive insights and support decision-making.
Becoming a Data Analyst in Pune typically takes 6 to 12 months, depending on the individual's background and the intensity of the training or education pursued. Completing a formal course or certification can expedite the process.
Data Analysts should be proficient in Python for its versatility in data manipulation and analysis, and R for its extensive statistical packages and visualization capabilities. Additionally, familiarity with SQL is crucial for managing and querying relational databases effectively.
Yes, there are job openings for freshers in data analysis in Pune. Companies are actively seeking entry-level candidates with relevant skills and educational backgrounds. You can find opportunities through job portals, company career pages, and recruitment agencies.
In Pune, Data Analysts can pursue roles such as Business Intelligence Analyst, Data Scientist, or Data Engineer. Opportunities also include positions in data visualization, statistical analysis, and database management. The city's growing tech industry offers a variety of career paths in both established firms and startups.
The DataMites Certified Data Analyst course in Pune is an excellent choice, offering live projects, internships, and placement assistance. This hands-on program provides practical experience to enhance your skills. With strong job placement support, it’s ideal for advancing your data analytics career.
Yes, the Data Analyst role in Pune is generally well-paying, with competitive salaries compared to many other cities in India. Compensation can vary based on experience, industry, and company size. Overall, it offers a promising career path with attractive financial rewards.
To sign up for DataMites' Certified Data Analyst course in Pune, visit the DataMites website and navigate to the course registration page. Fill out the application form with your details and submit it. After that, you will receive further instructions on payment and course access.
Upon finishing the DataMites Data Analyst course in Pune, you will receive the Certified Data Analyst certification, accredited by IABAC and NASSCOM®. This credential highlights your data analysis skills and enhances your career prospects.
Yes, DataMites offers job placement assistance as part of our Data Analyst course in Pune. We will provide support through resume building, interview preparation, and connecting students with potential employers. The program aims to enhance job readiness and increase employment opportunities.
A Flexi Pass from DataMites provides three months of access to a variety of training sessions. This flexible option allows participants to attend multiple classes at their convenience. It's designed to enhance learning while accommodating busy schedules.
Yes, DataMites offers demo classes for our Data Analyst course in Pune. You can attend these sessions to get an overview of the course content and teaching methods before enrolling. Please contact DataMites directly for the schedule and registration details.
At DataMites, our instructors are top-tier professionals with extensive industry experience. Ashok Veda, CEO of Rubixe and our lead mentor, exemplifies this expertise. Each trainer at DataMites delivers exceptional education through their valuable insights.
The Data Analyst course at DataMites covers essential topics such as data cleaning, data visualization, and statistical analysis. It also includes training in tools like Excel, SQL, and Python for effective data manipulation and analysis. The curriculum is designed to provide practical skills for data-driven decision-making.
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 during the first week. Refunds will not be provided after 6 months or if more than 30% of the material has been accessed. To request a refund, please email care@datamites.com from your registered email and refer to our refund policy for more details.
Enrolling in the DataMites Data Analyst course in Pune provides you with comprehensive training materials, including detailed course textbooks and online resources. You will also receive access to real-world case studies and practical assignments. Additionally, there are options for hands-on practice through live projects and interactive sessions.
Yes, DataMites offers live projects as part of our Data Analyst course in Pune. This practical experience helps students apply their learning to real-world scenarios. The inclusion of live projects enhances the overall learning experience and skill development.
Yes, DataMites offers EMI options for our Data Analyst training in Pune, allowing you to pay in affordable monthly installments. For more details, you can reach out to our admissions team or visit the official website.
Yes, if you miss a session at DataMites, you can typically catch up by accessing recorded sessions or rescheduling. Please check with our support team for specific options available to you. We aim to ensure you stay on track with your learning goals.
The fees for the DataMites Certified Data Analyst course in Pune range from ?25,000 to ?1,00,000. The exact amount may vary based on promotions or additional features. For the most accurate and up-to-date information, contact a DataMites counselor.
DataMites offers an internship opportunity as part of their Data Analyst course in Pune. This hands-on experience allows students to apply their learning in a real-world setting. For detailed information, please contact DataMites directly or visit our official website.
Yes, you can typically switch from an offline Data Analyst course to an online format in Pune. It's advisable to contact your course provider to discuss the options available for transitioning. We will provide guidance on the necessary steps and any potential adjustments to your curriculum.
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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