Data Analyst Course In Pune

Data Analyst Course

‍in Pune

This Data Analytics course in Pune is designed to help you secure your dream job. With a curriculum reverse-engineered to meet industry demands, the course equips you with in-demand skills. Additionally, it includes hands-on industry projects to help you build a strong professional portfolio.

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Book a Live Demo For Free

Over 15 hours of immersive training delivered over two days at IIT Madras.

Engaging interactive sessions led by IIT professors.

A prestigious certificate from IITM Pravartak, recognized by the industry.

Who Should Do The Data Analyst Course?

Your search for a career without coding ends right here. Irizpro Data Analyst Course with Excel, Tableau, MySQL, Power BI, and more relevant tools and skills is the perfect career choice, and comes with intensive interview preparation from Day 1, to prepare you to secure your dream job with our network of 2000+ hiring partners.

Any Graduate

Open to graduates from diverse fields, our Data Analyst Course equips aspiring data enthusiasts with practical skills for a rewarding journey in analysis.

Professionals from BPO & KPO

Transform your career by harnessing BPO & KPO expertise in our Data Analyst Course, mastering essential analytics for success in data-driven roles.

Freshers From Any Stream

Empower your career launch through our Data Analyst Course, crafted for fresh graduates, offering hands-on skills for success in data-driven roles.

Professionals from Any Domain

Unleash your potential across industries with our Data Analyst Course, fostering essential data analysis skills for impactful contributions in a data-driven world.

Professionals from Management & Finance

Empower your management and finance background in our Data Analyst Course, equipping you with analytical tools for data interpretation, informed decisions, and strategic growth.

Upcoming Batches

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₹90000

₹ 70,000

4-Month Live/Classroom Training
No Cost EMI Options
20+ Capstone Project
1 Year LMS Access
Internship + Job Assistance

₹70000

₹ 55,000

4-Month Self Learning + Live Mentoring
No Cost EMI Options
24*7 Leaner assistance and support
20+ Capstone Project
1 Year LMS Access
Job Assistance

₹35000

₹ 14,499

160 Learning Hours
Adaptive Learning Modules
24*7 Leaner assistance and support
20+ Capstone Project
1 Year Access To Elearning

Course Description

What Does the Course Offer?

The Data Analyst Course is designed to equip you with expertise in key technologies such as Excel, Advanced Excel, Tableau, SQL, Power BI, and the basics of R and Python. In addition to comprehensive theory sessions, the program includes hands-on assignments and projects, enabling students to apply their learning effectively.

Advanced Certification Program in Data Science and AI for Digital Transformation by IITM Pravartak

In collaboration with IIT Madras, Irizpro introduces an add-on certification to enhance your Data Science course.

This prestigious certification program offers:

15+ Hours of Interactive Live-Virtual Sessions led by IIT Madras professors.
An optional 2-day Campus Immersion at the modern and scenic IIT Madras campus.
A prestigious IIT Madras Pravartak Certificate to elevate your professional credentials.

Certification Process

During the course, you will attend interactive live-virtual sessions conducted by IIT Madras professors. Additionally, an optional two-day campus immersion will be arranged, where you can visit the IIT Madras campus in Chennai for a unique learning experience.

After the training, you will complete a short quiz on the topics covered. Successfully passing this quiz will unlock your Advanced Certification in Data Science and AI for Digital Transformation from IITM Pravartak.

Program Highlights

Expert Faculty

At Irizpro, our trainers are highly experienced professionals with over 12 years of industry expertise, driven by a passion for teaching.

Comprehensive Course Curriculum

Our industry-aligned curriculum is designed to seamlessly blend theoretical knowledge with practical application.

Real-Life Projects and Bootcamps

Gain hands-on experience by working on real-world data analytics scenarios across diverse domains, enhancing your practical knowledge and skills.

Job Readiness

From Day 1, candidates will receive comprehensive interview preparation to equip them for opportunities with our network of over 2,000 hiring partners.

Skills Covered

Database Concepts

Data Analysis Concepts

Functions Charts & Slices

Data Visualization

Tools and Technologies

Excel

My SQL

Power Bi

Tableau

Python

R Language

Data Analyst Project Life Cycle

Phase 1 - Data Collection

Following a thorough assessment of the business case in a specific domain, relevant data will be gathered accordingly.

Phase 2 - Data Preparation

In this phase, a database will be established using SQL to store the data collected in the previous step.

Phase 3 - Insights Generation and Dashboard Building

Connect the database to Tableau, Python, or R tools to extract the necessary data. Create user-friendly reports tailored to business requirements and develop the dashboard using Tableau or Power BI.

Projects

Analysis of Healthcare Patient Data

This project involves analyzing patient data for individuals with various diseases, focusing on summaries from healthcare facilities, chain organizations, and dialysis centers where patients are receiving treatment. Additionally, the project examines payment modes, including any discounts or payment reductions, as part of the analysis.

Customer Loan Analysis (Domain: Banking and Finance)

This project involves analyzing loans provided by a financial institution to customers across various grades and sub-grades. The analysis includes examining the reasons for loan disbursement, funded amounts, and revolving balances for each customer across different states and geolocations. Additionally, the project focuses on evaluating customers' payment modes and their most recent payment values.

Employee Retention Analysis (Domain: HR Analytics)

This HR-focused project analyzes employee attrition rates within an organization across various levels. The analysis examines factors such as monthly income, year of the last promotion, job roles, and work-life balance for employees in different departments.

Energy Consumption in Industrial Combustion (Domain: Energy)

This project involves analyzing fuel usage across various facilities and applications by evaluating MMBTu and GWHt values. The analysis also considers the types of fuels used across different geolocations and primary categories.

Flight Delay Analysis (Domain: Aviation)

The primary goal of this project is to identify the various reasons for flight delays across different airlines. The analysis includes evaluating the number of flights in operation, cancellations, and providing a statistical summary of flight distributions by week, state, and city.

Olist Store Performance Analysis (Domain: eCommerce)

This project analyzes the market for a specific product by examining a particular retail outlet that sells it. It involves a statistical analysis of payment distribution from various customers using different transaction methods across multiple product categories. Additionally, customer feedback regarding shipping times and other relevant factors will be incorporated into the analysis.

Learning Path

Why Irizpro

100% job Assistance

Guaranteed Job Assistance: Our program ensures that you receive comprehensive support to secure employment opportunities aligned with your newly acquired skills and expertise.

Small Batch size for Personalized Training

Experience personalized learning with our small batch sizes, fostering tailored interaction and effective skill development.

Flexible Payment Options

Explore our flexible payment options, including No Cost EMI, for a hassle-free investment in your education.

LMS Access

Get instant access to our comprehensive (LMS) for a seamless and enriched learning experience.

Course Curriculum

Topics to be covered

Excel: Basics to Advanced
MySQL
Tableau
Power BI

Excel

Introduction

Overview of MS Office Versions (Similarities and Differences)
Interface of the Latest Available Version
Understanding Rows and Columns
Keyboard Shortcuts for Easy Navigation
Data Entry Techniques (Fill Series)
Using Find and Select
Clear Options
Ctrl + Enter Functionality

Formatting Options

Formatting Features (Font, Alignment, Clipboard Functions: Copy, Paste Special)
Referencing and Named Ranges: Uses and Arithmetic Functions
Performing Mathematical Calculations with Cell Referencing (Absolute, Relative, Mixed)
Using Functions with Named Ranges

Arithmetic Functions

Common Arithmetic Functions: SUM, SUMIF, SUMIFS, COUNT, COUNTA, COUNTIFS, AVERAGE, AVERAGEIFS, MAX, MAXIFS, MIN, MINIFS
Logical Functions: IF, AND, OR, NESTED IF, NOT, IFERROR
Combining Mathematical and Logical Functions

Data Lookup Functions

Referring Data from Different Tables: Various Types of Lookups
LOOKUP, VLOOKUP, NESTED VLOOKUP, HLOOKUP, INDEX, INDEX with MATCH Function
Using INDIRECT and OFFSET Functions

Advanced Functions

Combinations of Arithmetic, Logical, and Lookup Functions
Data Validation (Including Dependent Drop-Downs)

Date and Text Functions

Date Functions: DATE, DAY, MONTH, YEAR, YEARFRAC, DATEDIF, EOMONTH
Text Functions: TEXT, UPPER, LOWER, PROPER, LEFT, RIGHT, SEARCH, FIND, MID, CONCATENATE, Flash Fill

Data Handling

Data Cleaning and Type Identification
Removing Duplicates, Formatting, and Filtering
Number Formatting (with Shortcuts)
Converting Data into an Excel Table (Ctrl + T)

Sorting and Filtering

Formatting Tables
Removing Duplicates
Basic Sorting and Advanced Sorting
Filtering and Advanced Filtering

Data Visualization

Conditional Formatting: Icon Sets, Highlighted Color Sets, Data Bars, Custom Formatting
Chart Types: Bar, Column, Line, Scatter, Combo, Gantt, Waterfall, Pie

Data Summarization

Pivot Reports and Charts: Inserting, Interface, Crosstable Reports, Filtering, Pivot Charts
Using Slicers: Adding and Connecting to Multiple Reports and Charts
Creating Calculated Fields and Calculated Items

Dashboard Creation

Types of Dashboards: Combining Reports and Charts, Using Slicers
Design and Placement: Formatting Tables, Charts, and Sheets; Proper Use of Colors and Shapes

Connecting to Data

Power Query, Pivot, and Power Pivot within Excel
Power Query: Interface and Tabs
Connecting to Data from Other Excel Files, Text Files, and Other Sources
Data Cleaning, Transforming, and Loading Data into Excel Queries
Using Loaded Queries: Merge and Append
Introduction to Power Pivot: Similarities and Differences with Pivot Reporting

VBA and Macros

Using the View Tab and Adding the Developer Tab
Recording Macros: Naming and Storage
Formatting Tables with Recorded Macros (Absolute and Relative References)
Playing Macros by Button or Command in a New Tab
Editing Macros

VBA Basics

Introduction to VBA for Excel: Subs, Ranges, Sheets
Comparing Values and Conditions: If Statements and Select Cases
Repeating Processes with For Loops and Do While/Do Until Loops
Communicating with the End User through Message Boxes and Taking User Input with Input Boxes and User Forms

MySQL

Introduction to MySQL

Overview of Databases
Introduction to RDBMS (Relational Database Management System)
Explanation of RDBMS Through Normalization
Types of RDBMS
Software Installation (MySQL Workbench)

SQL Commands and Data Types

Overview of SQL Commands: DDL, DML, DQL, DCL, TCL, and Their Applications
Data Types in SQL: Numeric, Char, Datetime

Data Query Language (DQL) and Operators

SELECT Statement
LIMIT and DISTINCT
WHERE Clause with Conditions: AND, OR, IN, NOT IN, BETWEEN, EXIST
ISNULL and IS NOT NULL
Wildcards for Pattern Matching
ORDER BY Clause

Logical Problem Solving and NULL Handling

Using CASE WHEN THEN for Logical Problems
Handling NULL Values with IFNULL and COALESCE

Group Operations and Aggregate Functions

GROUP BY and HAVING Clause
Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
COUNT with String Functions

Date and Time Functions

Working with Date and Time Functions

Constraints in SQL

NOT NULL, UNIQUE, CHECK, and DEFAULT Constraints
Primary Key and Foreign Key (Column-Level and Table-Level)

Joins in SQL

Inner Join
Left Join
Right Join
Cross Join
Self Join
Full Outer Join

Data Definition Language (DDL) Commands

CREATE, DROP, ALTER, RENAME, TRUNCATE, MODIFY, COMMENT

Data Manipulation Language (DML) and Transaction Control Language (TCL)

DML Commands: INSERT, UPDATE, DELETE
TCL Commands: COMMIT, ROLLBACK, SAVEPOINT

Advanced Data Management

Data Partitioning
Indexes: Types of Indexes
Views in SQL

Stored Procedures and Functions

Creating Stored Procedures: IN, OUT, INOUT Parameters
User-Defined Functions

Window Functions

RANK, DENSE_RANK
LEAD, LAG
ROW_NUMBER

Set Operations and Subqueries

UNION and UNION ALL
INTERSECT
Subqueries and Nested Queries

Exception Handling

Handling Exceptions in Queries
CONTINUE Handler
EXIT Handler

Triggers in SQL

Triggers: BEFORE and AFTER DML Statements

Tableau

Introduction to Tableau

What is Tableau?
Introduction to Data Visualization
Overview of Tableau Products
Tableau Desktop Variations
Tableau File Extensions

Data Concepts in Tableau

Understanding Data Types, Dimensions, Measures, and Aggregation
Tableau Desktop Installation

Data Source Overview

Connecting to Data Sources
Live vs. Extract Data Connections

Basic Charts and Formatting

Overview of Worksheet Sections and Shelves
Creating Basic Charts: Bar Chart, Stacked Bar Chart, Line Charts (Discrete and Continuous), Symbol Map, Filled Map, Text Table, Highlight Table
Formatting Options: Removing Gridlines, Hiding Axes, Number Conversion (Thousands/Millions), Shading, Row and Column Dividers
Working with the Marks Card

Filters in Tableau

Introduction to Filters and Their Types
- Extract, Data Source, Context, Dimension, Measure, Quick Filters
Order of Operations for Filters
Cascading Filters and Applying Filters to Worksheets

Calculations in Tableau

The Need for Calculations
Types of Calculations: Basic, LOD (Level of Detail), Table
Examples of Basic Calculations: Aggregate Functions, Logical Functions, String Functions, Numerical Functions, Date Functions
Examples of LOD Calculations
Examples of Table Calculations

Data Combining Techniques

Introduction to Data Combining Techniques
Types of Data Combining: Joins, Relationships, Blending, Union

Custom Charts

Dual Axis Charts
Combined Axis Charts
Donut Chart
Lollipop Chart
KPI Cards (Simple and With Shapes)

Advanced Features

Groups: Purpose and Usage
Bins: Purpose and Usage
Hierarchies: Purpose and Usage
Sets: Purpose and Usage
Parameters: Purpose and Examples

Analytics and Dashboard Creation

Adding Reference Lines and Trend Lines
Dashboard Overview: Tiled vs. Floating Layouts
Understanding Dashboard Objects and Layouts
Creating and Formatting Dashboards

Dashboard Actions and Tableau Public

Types of Dashboard Actions: Filter, Highlight, URL, Sheet, Parameter, Set
Saving Workbooks to Tableau Public Website

Power BI

Introduction to Power BI and Installation

Overview of Power BI
Installation Process and Checklist for Successful Installation
Formatting and Setting Up Prerequisites

Understanding Power BI Basics

Differences Between Power BI Desktop and Power Query
Power BI User Interface: Data Sources and Visualizations
Navigating the Power BI Interface: Query Editor and Desktop

Data Loading and Visualization Basics

Loading Data from Multiple Sources
Understanding Data Types and Default Charts with Drag-and-Drop
Geo-Location Map Integration
Creating a Sample Dashboard with Animation Visuals
Financial Sample Data in Power BI: Building a Starter Dashboard

Advanced Visualizations

Map Visual Types and Use Cases
Scatter Plot Chart with Play Axis and Parameters
AI-Driven Visuals in Power BI
Utilizing AI Features
AI-Based Chart Analysis
Q&A Chatbot Applications in Real-Life Scenarios
Hierarchy Tree Visualizations

Power BI Visualizations

Column and Line Charts
Conditional Formatting and Advanced Formatting Techniques

Power Query Editor

Loading Data from Folders
Power Query Features: Promoting Headers, Splitting to Delimiters, Adding Columns, Appending, and Merging Queries

Data Modeling in Power BI

Loading Data in Different Formats
Creating and Understanding Relationships in Data Models
Connection Types, Cardinality, and Filter Directions
Building Dashboards with New Data

Filtering and Data Customization

Types of Filters and Filter Applications
Trend Analysis and Future Forecasting
Customizing Data in Power BI
Tooltips
Drill Down and Drill Through
Visual Interactions
Button Triggers and Navigation Buttons
Bookmarks and Their Implementation

DAX (Data Analysis Expressions)

Introduction to DAX: Calculated Columns, Tables, and Measures
Examples of DAX Functions
Calendar, Calendar Auto
Summarize, Group By
Related, Lookup Value, Switch, DateDiff, RankX
Time Intelligence Functions: Rolling Average, YoY, Running Totals

Custom Visualizations

Understanding and Implementing Custom Visuals
Loading, Pinning, and Saving Custom Visual Templates

Publishing and Sharing Power BI Work

Power BI Service Overview (app.powerbi.com)
Scheduling Data Refreshes
Using Data Flows and Power BI Online Features
Exporting Power BI Data for Live Presentations

Value-Added Courses

Business Statistics
Fundamentals of R
Fundamentals of Python
SAS (Self-Paced)
ChatGPT Integration

Business Statistics

Descriptive Statistics

Data Types: Overview and Classification
Measures of Central Tendency: Mean, Median, Mode
Measures of Dispersion: Range, Variance, Standard Deviation
Graphical Techniques: Visualization Methods
Skewness and Kurtosis: Understanding Distribution Shapes
Box Plot: Identifying Outliers and Distribution Summary

Probability and Normal Distribution

Random Variables: Definition and Examples
Probability: Basics and Rules
Probability Distributions: Key Concepts and Applications
Normal Distribution: Characteristics and Applications
Standard Normal Distribution (SND): Z-Scores and Usage
Expected Value: Calculation and Interpretation

Inferential Statistics

Sampling Funnel: Understanding Sampling Process
Sampling Variation: Impacts and Examples
Central Limit Theorem: Importance in Statistics
Confidence Intervals: Estimation and Significance

Introduction to Hypothesis Testing

Hypothesis Testing Basics: Null and Alternative Hypotheses
Types of Tests:
- Two-Proportion Test
- Two-Sample T-Test

ANOVA and Chi-Square Tests

ANOVA (Analysis of Variance): Comparing Means Across Groups
Chi-Square Test: Testing for Independence

Data Cleaning and Insights

Data Cleaning: Identifying and Addressing Invalid Cells, Blanks, Outliers, and Null Values
Imputation Techniques: Using Mean and Median for Missing Data

Correlation and Visualization

Scatter Diagram: Relationship Between Variables
Correlation Analysis: Strength and Direction of Relationships

Fundamentals of R

Introduction to R

Overview of R and Its Applications
Installing RStudio

Data Types in R

Understanding Data Types: Numeric, Character, Logical, Complex
Data Structures: Vector, List, Matrix, Factor, Array, Dataframe

Operators in R

Relational Operators
Logical Operators

Decision-Making Statements and Loops

Conditional Statements: if, ifelse
Loop Structures: for, while, repeat

Functions in R

Creating and Using Functions
Overview of Built-in Functions

Data Manipulation in R

Merging Dataframes
Analyzing the Iris Dataset with Apply Functions

dplyr Package

Key Functions: filter, select, arrange

Data Visualization with ggplot2

Creating Visualizations
Scatterplot
Histogram
Boxplot

Fundamentals of Python

Setting Up and Getting Started

Anaconda Installation
Introduction to Python: Overview and Applications

Python Basics

Variables and Data Types: Integer, Boolean, Float, List, Tuple, String
Operators in Python: Arithmetic, Relational, Logical, and Identity Operators

Data Handling and Operations

Data Types and Slicing
Inbuilt Functions: Overview of Common Functions

Working with Collections

Dictionaries:
- Sequence Methods
- Concatenation and Repetition
- len, min, max Functions
- Index Positioning
- Adding and Deleting Elements
- Reversing and Sorting
Sets and Set Theory:
Basic Operations and Applications

Regular Expressions and Decision Making

Regular Expressions with re Module:
findall, search, split, match
Decision Making Statements:
if, elif, and User Input Handling

Control Flow and Functions

Loops: for, while
Functions: Basics and Lambda Functions

Modules in Python

Using Standard Modules :math,calendar,datetime

Data Analysis and Visualization

Pandas: Creating DataFrames with Different Methods, Analyzing University and Salary Datasets
NumPy: Introduction to Array Handling
Matplotlib and Seaborn: Visualization Techniques and Applications

ChatGPT

Understanding ChatGPT

What is ChatGPT?
History of ChatGPT
Applications of ChatGPT
ChatGPT vs Other Chatbot Platforms
Industries Leveraging ChatGPT
Benefits and Limitations of ChatGPT
Future Developments in ChatGPT Technology
Ethical Considerations in ChatGPT and AI

ChatGPT Architecture and AI Overview

What is AI?
Types of AI
Understanding Machine Learning
Neural Networks and Their Applications
Deep Learning Fundamentals
Natural Language Processing (NLP)
Computer Vision Overview
AI in Robotics

ChatGPT Functionalities and Applications

Overview of ChatGPT Functionality
Drafting Emails and Professional Communications
Automating Content Creation
Resume and Cover Letter Assistance
Research and Information Gathering
Brainstorming Ideas and Solving Creative Problems

ChatGPT Prompt Engineering

What is Prompt Engineering?
Types of Prompts
Creating Effective Prompts
Leveraging ChatGPT for Prompt Generation

FAQs

What is data analytics?

Data analytics is the process of examining and analyzing large datasets to discover patterns, extract meaningful insights, and make data-driven decisions. It involves various techniques, tools, and statistical methods to uncover valuable information from structured and unstructured data.

Who can benefit from a data analytics course?

A data analytics course is beneficial for a wide range of individuals, including professionals looking to enhance their analytical skills, business managers seeking to make data-driven decisions, aspiring data analysts, data scientists, and anyone interested in harnessing the power of data for decision-making.

Do I need any prior experience in data analytics or programming?

No prior experience is required for our data analytics course. We provide a comprehensive curriculum that caters to beginners and those with some prior knowledge. Basic computer skills are recommended, but we will guide you through the fundamentals of data analytics and provide hands-on training with relevant tools and programming languages.

What software or tools will I learn during the course?

Our data analytics course covers a variety of tools and software commonly used in the industry. This may include popular data analysis and visualization tools such as Python libraries (e.g., pandas, NumPy, Matplotlib), R programming language, SQL for data querying, and data visualization tools like Tableau or Power BI.

Will I receive a certificate upon completion?

As a data analyst, upon successful completion of the data analytics course, you will receive a certificate that validates your participation and achievement. This certificate can be a valuable addition to your professional portfolio and demonstrate your commitment to enhancing your data analytics skills.