Data Analytics with Tableau

Master's Program 4.9 out of 5 rating votes 3697

Learn the fundamentals of data analytics with Tableau and understand the use of data visualization.

INR 35000

Excluding GST

100% Placement



Ranked#2 among Top Full-Time

Data Analytics with Tableau in India- 2010-2022

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Start Date : 5th Dec 2022Duration : 6 Months
Format : Live Online /Self-Paced/Classroom
Future of Data Analytics with Tableau

₹6 LPA to ₹17 LPA

A data analyst who is proficient in Tableau can earn ₹6 LPA. And an experienced data analyst can make around ₹17 LPA.


Job Opportunities

As per IBM, the number of jobs in the data analytics industry is expected to grow to 2,720,000 in the coming years.


Future Analytics

A large number of companies will begin using public cloud warehouses for storing data for data analysis by the year 2023.


Professional in Data Analytics with Tableau

4.9 out of 5 rating vote 3697

Learn the fundamentals of data analytics with Tableau and understand the use of data visualization..

INR 35000 + GST
100% Placement Assistance

Program Overview

The professional in data analytics with Tableau course will help you get in-depth knowledge about data analytics and Tableau. In this course, you will gain an understanding of how to transform raw data into useful data with the help of various cleaning, splitting, and merging techniques. Moreover, you will become competent in working with Tableau for performing data analysis. Once this course is completed, you can work as a:

  • Data Analyst
  • Data Scientist
  • Finance Analyst
  • Tableau Developer
  • Data Engineer

The students who complete the professional in data analytics with tableau course can open doors to many opportunities in the data analytics and tableau industry. This is because of the benefits that a company gains because of data analytics and Tableau. As a result, many companies are always looking for competent data professionals like data scientists, finance analysts, data engineers, etc. that can help them in performing data analytics. Thus, learning tableau and data analytics can be very beneficial for you.

  • Web_IconAs per IBM, the number of jobs in the data analytics industry is expected to grow to 2,720,000 in the coming years.
  • branVarious opportunities are available for students who learn data analytics and Tableau. For example, you can work as a finance analyst, business Intelligence analyst, data engineer, etc.
  • polyA large number of companies will begin using public cloud warehouses for storing data for data analysis by the year 2023.
  • analyticsThe data analytics market is going to grow with a CAGR of 25% till 2030.

The popularity of Tableau and data analytics is growing with every passing day and year. This course will help you gain in-depth knowledge of Tableau and data analytics. Furthermore, you will learn how to use Tableau to analyze and extract useful data and insights from raw data.

m IconWith project-based training, you will master all the skills that are essential for becoming an expert in Tableau and data analytics.

 m IconAfter this course, you can easily get a good job in a reputed organization as an analyst with a ample salary of ₹6 to ₹17 LPA.

icon 3As per IBM, the number of jobs in the data analytics industry is expected to grow to 2,720,000 in the coming years.

The main aim of the professional in data analytics with Tableau course is to help students understand the various concepts of data analytics and make them experts in working with the tableau data visualization tool. Besides this, you will learn how to use data analytics and Tableau to improve a company's performance/profit.
Things you will learn:

  • Data fundamentals
  • How to work with Tableau?
  • How to create interactive dashboards on Tableau?
  • Importance of data analytics
  • Data analysis

The main objective of this training program is to provide top-notch data analytics and Tableau training to students and make them competent data analysts. The curriculum of the course is developed with the help of data analysts who have years of working experience. Moreover, the course is designed to satisfy the changing demands of the tableau and data analytics industry.

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Tools Covered of Data Analytics with Tableau

Power BI
Machen Learning

Data Analytics with Tableau Curriculum

Course 1
Python Statistics for Data Science

You will first learn the basic statistical concepts for Data Analytics using Python.


Course Content

    • Things you will learn:
    • Introduction to Python
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
    • Introduction to Variables
      • Variables, expression condition and function
      • Global and Local Variables in Python
      • Packing and Unpacking Arguments
      • Type Casting in Python
      • Byte objects vs. string in Python
      • Variable Scope
    • Python Data Type
      • Declaring and usingNumeric data types
      • Using stringdata type and string operations
      • Understanding Non-numeric data types
      • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets
    • Introduction Keywords and Identifiers and Operators
      • Python Keyword and Identifiers
      • Python Comments, Multiline Comments.
      • Python Indentation
      • Understating the concepts of Operators
    • Data Structure
      • List
        • What is List.
        • List Creation
        • List Length
        • List Append
        • List Insert
        • List Remove
        • List Append & Extend using “+” and Keyword
        • List Delete
        • List related Keyword in Python
        • List Revers
        • List Sorting
        • List having Multiple Reference
        • String Split to create a List
        • List Indexing
        • List Slicing
        • List count and Looping
        • List Comprehension and Nested Comprehension
      • Dictionary
        • Dict Creation
        • Dict Access (Accessing Dict Values)
        • Dict Get Method
        • Dict Add or Modify Elements
        • Dict Copy
        • Dict From Keys.
        • Dict Items
        • Dict Keys (Updating, Removing and Iterating)
        • Dict Values
        • Dict Comprehension
        • Default Dictionaries
        • Ordered Dictionaries
        • Looping Dictionaries
        • Dict useful methods (Pop, Pop Item, Str , Update etc.)
    • Sets, Tuples and Looping Programming
      • Sets
        • What is Set
        • Set Creation
        • Add element to a Set
        • Remove elements from a Set
        • PythonSet Operations
        • Frozen Sets
      • Tuple
        • What is Tuple
        • Tuple Creation
        • Accessing Elements in Tuple
        • Changinga Tuple
        • TupleDeletion
        • Tuple Count
        • Tuple Index
        • TupleMembership
        • TupleBuilt in Function (Length, Sort)
      • Control Flow
        • Loops
        • Loops and Control Statements (Continue, Break and Pass).
        • Looping techniques in Python
        • How to use Range function in Loop
        • Programs for printing Patterns in Python
        • How to use if and else with Loop
        • Use of Switch Function in Loop
        • Elegant way of Python Iteration
        • Generator in Python
        • How to use nested IF and Else in Python
        • How to use nested Loop in Python
        • Use If and Else in for and While Loop
        • Examples of Looping with Break and Continue Statements
        • How to use IN or NOTkeywordin Python Loop.
    • Exception and File Handling, Module, Function and Packages
      • Python Exception Handling
        • Python Errors and Built-in-Exceptions
        • Exception handing Try, Except and Finally
        • Catching Exceptions in Python
        • Catching Specific Exception in Python
        • Raising Exception
        • Try and Finally
      • Python File Handling
        • Opening a File
        • Python File Modes
        • Closing File
        • Writing to a File
        • Reading from a File
        • Renaming and Deleting Files in Python
        • Python Directory and File Management
        • List Directories and Files
        • Making New Directory
        • Changing Directory
      • Python Function, Modules and Packages
        • Python Syntax
        • Function Call
        • Return Statement
        • Write an Empty Function in Python –pass statement.
        • Lamda/ Anonymous Function
        • *argsand **kwargs
        • Help function in Python
        • Scope and Life Time of Variable in Python Function
        • Nested Loop in Python Function
        • Recursive Function and Its Advantage and Disadvantage
        • Organizing python codes using functions
        • Organizing python projects into modules
        • Importing own module as well as external modules
        • Understanding Packages
        • Programming using functions, modules & external packages
        • Map, Filter and Reduce function with Lambda Function
        • More example of Python Function
    • Data Automation (Excel, SQL, PDF etc)
      • Python Object Oriented Programming—Oops
        • Concept of Class, Object and Instances
        • Constructor, Class attributes and Destructors
        • Real time use of class in live projects
        • Inheritance, Overlapping and Overloading operators
        • Adding and retrieving dynamic attributes of classes
        • Programming using Oops support
      • Python Database Interaction
        • SQL Database connection using
        • Creating and searching tables
        • Reading and Storing configinformation on database
        • Programming using database connections
      • Reading an excel
        • Reading an excel file usingPython
        • Writing toan excel sheet using Python
        • Python| Reading an excel file
        • Python | Writing an excel file
        • Adjusting Rows and Column using Python
        • ArithmeticOperation in Excel file.
        • Plotting Pie Charts
        • Plotting Area Charts
        • Plotting Bar or Column Charts using Python.
        • Plotting Doughnut Chartslusing Python.
        • Consolidationof Excel File using Python
        • Split of Excel File Using Python.
        • Play with Workbook, Sheets and Cells in Excel using Python
        • Creating and Removing Sheets
        • Formatting the Excel File Data
        • More example of Python Function
      • Working with PDF and MS Word using Python
        • Extracting Text from PDFs
        • Creating PDFs
        • Copy Pages
        • Split PDF
        • Combining pages from many PDFs
        • Rotating PDF’s Pages
      • Complete Understanding of OS Module of Python
        • Check Dirs. (exist or not)
        • How to split path and extension
        • How to get user profile detail
        • Get the path of Desktop, Documents, Downloads etc.
        • Handle the File System Organization using OS
        • How to get any files and folder’s details using OS
Course 2
Data Analysis & Visualization

Data visualization is the graphical way to representation of information and data. By using visual elements like graphs, maps and charts. Data visualization tools provide an accessible easy way to see and understand the data.


Course Content

    • Things you will learn:
    • Pandas
      • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
      • How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
      • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
      • Exploring the Data Plotting, Correlations, and Histograms
      • Getting statistical information about the data Analysis Concepts, Handle the None Values
      • Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
      • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
      • Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
      • Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
      • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
      • Exporting the results to Excel Joins:
      • Python | Pandas Data Frame Inner Join
      • Under sting the properties of Data Frame Left Join (Left Outer Join)
      • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
      • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
      • Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
      • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
      • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
    • NumPy
      • Introduction to NumPy: Numerical Python
      • Importing NumPy and Its Properties
      • NumPy Arrays
      • Creating an Array from a CSV
      • Operations an Array from aCSV
      • Operations with NumPy Arrays
      • Two-Dimensional Array
      • Selecting Elements from 1-D Array
      • Selecting Elements from 2-D Array
      • Logical Operation with Arrays
      • Indexing NumPy elements using conditionals
      • NumPy’sMean and Axis
      • NumPy’sMode, Median and Sum Function
      • NumPy’sSort Function and More
    • MatPlotLib
      • Bar Chart using Python MatPlotLib
      • Column Chart using Python MatPlotLib
      • Pie Chart using Python MatPlotLib
      • Area Chart using Python MatPlotLib
      • Scatter Plot Chart using Python MatPlotLib
      • Play with Charts Properties Using MatPlotLib
      • Export the Chart as Image
      • Understanding plt. subplots () notation
      • Legend Alignment of Chart using MatPlotLib
      • Create Charts as Image
      • Other Useful Properties of Charts.
      • Complete Understanding of Histograms
      • Plotting Different Charts, Labels, and Labels Alignment etc.
    • Introduction to Seaborn
      • Introduction to Seaborn
      • Making a scatter plot with lists
      • Making a count plot with a list
      • Using Pandas with seaborn
      • Tidy vs Untidy data
      • Making a count plot with a Dataframe
      • Adding a third variable with hue
      • Hue and scattera plots
      • Hue and count plots
    • Visualizing Two Quantitative Variables
      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots
    • Visualizing a Categorical and a Quantitative Variable
      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whiskers
      • Point plots
      • Customizing points plots
      • Point plot with subgroups
    • Customizing Seaborn Plots
      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels: Part 1
      • Face Grids vs. Axes Subplots
      • Adding a title to a face Grid object
      • Adding title and labels: Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
Course 3
Data Analytics Overview

Data analytics helps individuals and organizations to make meaningful data by analyzing raw data. Data analysts typically analyzing raw data in order to make meaningful & actionable Data by using various tools.


Course Content

    • Things you will learn:
      • Dealing with Different Types of Data
      • Data Visualization for Decision making
      • Data Science, Data Analytics, and Machine Learning
      • Data Science Methodology
      • Data Analytics in Different Sectors
      • Analytics Framework and Latest trends
Course 4
Statistics Essentials For Analytics

Statistics Essentials for Data Analytics provides complete knowledge about the Statistical Techniques. In Statistics Essentials For Analytics you will learn how every technique is employed on a real world data set to analyse and conclude insights.


Course Content

    • Things you will learn:
    • Introduction to Statistics for Analytics
      • Sample or Population Data
      • The Fundamentals of Descriptive Statistics
      • Measures of Central Tendency, Asymmetry, and Variability
      • Practical Example: Descriptive Statistics
    • Distributions
      • Estimators and Estimates
      • Confidence Intervals: Advanced Topics
      • Practical Example: Inferential Statistics
    • Hypothesis Testing
      • Introduction
      • Hypothesis Testing: Let’s Start Testing!
      • Practical Example: Hypothesis Testing
    • The Fundamentals of Regression Analysis
      • Subtleties of Regression Analysis
      • Assumptions for Linear Regression Analysis
      • Dealing with Categorical Data
      • Practical Example: Regression Analysis
Course 5
SQL For Data Analytics

SQL for Data Analysis is a very useful programming language that helps data analysts to interact with data stored in databases. SQL is the most used data analysis tool for data analysts and data scientists because the majority of the world's data is stored in databases.


Course Content

    • Things you will learn:
    • Introduction
      • Overview of Oracle Database 11g and related products
      • Overview of relational database management concepts and terminologies
      • Introduction to SQL and its development environments
      • The HR schema and the tables used in this course
      • Oracle Database documentation and additional resources
    • Retrieve Data using the SQL SELECT Statement
      • List the capabilities of SQL SELECT statements
      • Generate a report of data from the output of a basic SELECT statement
      • Use arithmetic expressions and NULL values in the SELECT statement
      • Invoke Column aliases
      • Concatenation operator, literal character strings, alternative quote operator, and the DISTINCT keyword
      • Display the table structure using the DESCRIBE command
    • Usage of Single-Row Functions to Customize Output
      • List the differences between single row and multiple row functions
      • Manipulate strings using character functions
      • Manipulate numbers with the ROUND, TRUNC, and MOD functions
      • Perform arithmetic with date data
      • Manipulate dates with the DATE functions
    • Conversion Functions and Conditional Expressions
      • Describe implicit and explicit data type conversion
      • Describe the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions
      • Nesting multiple functions
      • Apply the NVL, NULLIF, and COALESCE functions to data
      • Usage of conditional IF THEN ELSE logic in a SELECT statement
    • Aggregated Data Using the Group Functions
      • Usage of the aggregation functions in SELECT statements to produce meaningful reports
      • Describe the AVG, SUM, MIN, and MAX function
      • How to handle Null Values in a group function
      • Divide the data in groups by using the GROUP BY clause
      • Exclude groups of date by using the HAVING clause
    • Display Data from Multiple Tables
      • Write SELECT statements to access data from more than one table
      • Join Tables Using SQL:1999 Syntax
      • View data that does not meet a join condition by using outer joins
      • Join a table to itself by using a self join
      • Create Cross Joins
    • Usage of Sub-queries to Solve Queries
      • Use a Sub-query to Solve a Problem
      • Single-Row Sub-queries
      • Group Functions in a Sub-query
      • Multiple-Row Sub-queries
      • Use the ANY and ALL Operator in Multiple-Row Sub-queries
      • Use the EXISTS Operator
    • SET Operators
      • Describe the SET operators
      • Use a SET operator to combine multiple queries into a single query
      • Describe the UNION, UNION ALL, INTERSECT, and MINUS Operators
      • Use the ORDER BY Clause in Set Operations
    • Data Manipulation
      • Add New Rows to a Table
      • Change the Data in a Table
      • Use the DELETE and TRUNCATE Statements
      • How to save and discard changes with the COMMIT and ROLLBACK statements
      • Implement Read Consistency
      • Describe the FOR UPDATE Clause
Course 6
Analytics with Excel

Excel is one of the most popular data analysis tool, to help visualize and gain insights from your data. Analytics with Excel helps you to boost your Microsoft Excel skills.


Course Content

    • Things you will learn:
    • Ms Excel Basic
      • Creation of Excel Sheet Data
      • Range Name, Format Painter
      • Conditional Formatting, Wrap Text, Merge & Centre
      • Sort, Filter, Advance Filter
      • Different type of Chart Creations
      • Auditing, (Trace Precedents, Trace Dependents)Print Area
      • Data Validations, Consolidate, Subtotal
      • What if Analysis (Data Table, Goal Seek, Scenario)
      • Solver, Freeze Panes
      • Various Simple Functions in Excel(Sum, Average, Max, Min)
      • Real Life Assignment work
    • Ms Excel Advance
      • Advance Data Sorting
      • Multi-level sorting
      • Restoring data to original order after performing sorting
      • Sort by icons
      • Sort by colours
      • Lookup Functions
        • Lookup
        • VLookup
        • HLookup
      • Subtotal, Multi-Level Subtotal
      • Grouping Features
        • Column Wise
        • Row Wise
      • Consolidation With Several Worksheets
      • Filter
        • Auto Filter
        • Advance Filter
      • Printing of Raw & Column Heading on Each Page
      • Workbook Protection and Worksheet Protection
      • Specified Range Protection in Worksheet
      • Excel Data Analysis
        • Goal Seek
        • Scenario Manager
      • Data Table
        • Advance use of Data Tables in Excel
        • Reporting and Information Representation
      • Pivot Table
        • Pivot Chat
        • Slicer with Pivot Table & Chart
      • Generating MIS Report In Excel
        • Advance Functions of Excel
        • Math & Trig Functions
      • Text Functions
      • Lookup & Reference Function
      • Logical Functions & Date and Time Functions
      • Database Functions
      • Statistical Functions
      • Financial Functions
      • Functions for Calculation Depreciation
    • MIS Reporting & Dash Board
      • Dashboard Background
      • Dashboard Elements
      • Interactive Dashboards
      • Type of Reporting In India
        • Reporting Analyst
        • Indian Print Media Reporting
      • Audit Report
      • Accounting MIS Reports
      • HR Mis Reports
      • MIS Report Preparation Supplier, Exporter
      • Data Analysis
        • Costing Budgeting Mis Reporting
        • MIS Report For Manufacturing Company
        • MIS Reporting For Store And Billing
      • Product Performance Report
      • Member Performance Report
      • Customer-Wise Sales Report
      • Collections Report
      • Channel Stock Report
      • Prospect Analysis Report
      • Calling Reports
      • Expenses Report
      • Stock Controller MIS Reporting
      • Inventory Statement
      • Payroll Report
      • Salary Slip
      • Loan Assumption Sheet
      • Invoice Creation
Course 7
Analytics with Tableau

Tableau is an end-to-end data analytics platform that allows you to prepare and analyze your data insights. Tableau helps people see and understand data.


Course Content

    • Things you will learn:
    • Introduction to Tableau2018
      • What is Tableau
      • Features of Tableau
      • Top Chart Types in Tableau
      • Introduction to the various File Types
      • Quick Introduction to the User Interface in Tableau
      • How to Create Data Visualization Using Tableau feature “Show Me”
      • Reorder & Remove Visualization Fields
      • How to Sort & Filter Data
      • How to Create a Calculated Field
      • How to Perform Operations using Cross-Tab
      • Working with Workbook Data & Worksheets
      • How to Create a Packaged Workbook
    • Tableau Architecture & User Interface
      • Architecture of Tableau
      • Installation of Tableau Desktop
      • The interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc.)
      • How to Start with Tableau
    • Data Preparation
      • Connecting to Different Data Sources
      • Excel
      • CSV
      • Microsoft Access
      • SQL server
      • Google Sheets
      • Live vs. Extract Connection
      • Creating Extract
      • Refreshing Extract
      • Incremental Extract
      • Refreshing Live
      • Data Source Editor
      • Managing Metadata and Extracts
      • Pivoting & Splitting
      • Data Interpreter : Clean dirty data
      • TWB vs. TWBX
    • Data Visualization Principles
      • What is Data Visualization
      • Why Visualization came into the picture
      • Importance of Visualizing Data
      • Poor Visualizations versus Perfect Visualizations
      • Principles of Visualizations
      • Tufte’s Graphical Integrity Rule
      • Tufte’s Principles for Analytical Design
      • Visual Rhetoric
      • Goal of Data Visualization
      • Data Interpretation
      • Pivot Tables
      • Split Tables
      • Responsive Tool Tips
      • Radial & Lasso Selection
      • Right Click Filtering
      • Creating Calculated Fields
      • Logical functions
      • Case-if functions
      • ZN function
      • Else-if function
      • Ad-Hoc Calculations
      • Manipulating Text-Left and Right Functions
    • Basic Data Visualization
      • Pivot Table & Heat Map
      • Highlight Table
      • Bar Charts
      • Line Charts
      • Pie Chart
      • Scatter Plot
      • Word Cloud
      • Tree Map
      • Blended Axis
      • Dual Axis
    • Managing Your Data
      • Filters
      • Types of Filters
      • Dimension Filters
      • Measure Filters
      • Condition based Filters
      • Advanced filters using wildcards
      • Top & Bottom N Filtering
      • Filtering order of operations
      • Extract Filter
      • Data Source Filter
      • Context Filter
      • Other Filters etc
      • Sorting
      • Calculations - String, Basic, Date & Logic
      • Parameters
      • Working with Dates
      • Table Calculation
      • Discrete vs Continuous measures
      • Grouping Data
      • Groups
      • Sets
      • Hierarchies
      • Bins
      • Combined Fields
    • Formatting
      • Size
      • Updating Axis
      • Colors
      • Borders
      • Transparency
      • Chart Lines
      • Trend Line
      • Forecasting
      • Reference Line
      • Mark Labels
      • Annotations
    • Dashboard Design
      • Canvas Selection & Adjusting Sizes
      • Tiled Objects
      • Floating Objects
      • Pixel Perfect Alignment
      • Summary Box
      • Chart Titles & Captions
      • Adding Images & Text
      • Adding Background Color
      • Adding Shading
      • Adding Separator Lines
      • Dynamic Chart Titles
      • Information Icons
      • Creating a Story
    • Advanced Data Preparation
      • Join
      • Inner
      • Left
      • Right
      • Full
      • Complex Joins
      • Union
      • Data Blending & when it is required
    • Advance Data Visualization
      • Bar Chart
      • Stack Bar Chart
      • Bar in Bar Chart
      • Combo Chart
      • Line Chart
      • Single Axis
      • Blended Axis
      • Dual Axis
      • Dual Axis Chart
      • Line
      • Bar
      • Lollipop Chart
      • Donut
      • Pareto Chart
      • Motion Charts
      • Other Advanced Charts
    • Advanced Filtering & Actions
      • Action Filters
      • Action Jumps
    • Sharing Your Dashboards
      • Publishing to PDF
      • Exporting to Pivot Tables and Images
      • Exporting Packaged Workbooks
      • Publishing to Tableau Server
Course 8
Data Analytics Live Projects

Here, at Croma Campus, you will get the opportunity to enroll in Data Analytics Live Projects.


Course Content

    • This training will comprise of up to 3 live projects respectively.
CertificatesMaster's Program Certificate

You will get certificate after completion of program

Course Structure
  • - 6 Months Online Program
  • - 80+ Hours of Intensive Learning
  • - 8+ Assigments & 3+ Projects
  • - 3 Live Projects
Career Assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired

Get Ahead with Croma Campus master Certificate

Earn your certificate

Our Master program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Masters Certificate

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Industry Project


Real-life Case Studies

Work on case studies based on top industry frameworks and connect your learning with real-time industry solutions right away.


Best Industry-Practitioners

All of our trainers and highly experienced, passionate about teaching and worked in the similar space for more than 3 years.


Acquire essential Industrial Skills

Wisely structured course content to help you in acquiring all the required industrial skills and grow like a superstar in the IT marketplace.


Hands-on Practical Knowledge

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.


Collaborative Learning

Take your career at the top with collaborative learning at the Croma Campus where you could learn and grow in groups.


Assignment & Quizzes

Practice different assignments and quizzes on different topics or at the end of each module to evaluate your skills and learning speed.

Placement & Recruitment Partners

Placement Assistance

We provide 100 percent placement assistance and most of our students are placed after completion of the training in top IT giants. We work on your resume, personality development, communication skills, soft-skills, along with the technical skills.

CAREERS AND SALARIES IN Data Analytics with Tableau

The demand for data and data visualization experts is growing at a rapid pace in the market. As per the data of IBM data analytics industry alone will create around 2,720,000 jobs in the US job market in coming years. Besides this, a data and data visualization expert can also earn a good amount of money for his services. On average, a data analyst can earn approximately around ₹6 LPA to ₹17 LPA. On the other hand, a tableau developer can earn around ₹4 LPA to ₹10 LPA. In short, learning data analytics and Tableau can be very beneficial for you.


A data analyst helps an organization interpret and represent its business data in various forms for the purpose of extracting actionable insights from it. Besides this, he is responsible for segregating/simplifying data from different data systems. A data analyst must have expertise in working with MS Excel, MS SQL, etc. Additionally, he should also be proficient in data mining and data modeling. On average, an analyst can earn around ₹6 LPA to ₹17 LPA.

Data scientist empowers a firm to collect and analyze raw data so that useful, actionable insights can be extracted from it. The work of a data scientist revolves around working with complex data sets; thus, he must be proficient in data analytics, R programming, and different visualization tools. A data scientist, on average, can earn ₹4 LPA to ₹24 LPA.

Financial analysts are experts in finance and use the financial data of a firm to extract actionable insights from it. This role or profile is perfect for data experts that are experts in finance. A financial analyst must be proficient in working with bonds, stocks, etc. A financial analyst can earn around ₹4 LPA to ₹ 8 LPA.

Tableau developers are professionals who perform data visualization and data analysis for a firm using Tableau. They are responsible for developing and managing BI systems for an organization and implementing data-driven policies. A competent Tableau developer must be proficient in working with RDBMS, MS-Excel, PL/SQL, etc. A Tableau developer can earn around ₹4 LPA to ₹ 10 LPA.

A data engineer's work revolves around working with big data sets or big data. They optimize the infrastructure of an organization. Moreover, they also help a firm in optimizing its data analytics process. A good data engineer must be an expert in programming and data visualization. Furthermore, they must also have experience in creating and testing solutions. A data engineer can earn around ₹8 LPA to 17 LPA.


On the completion of the course, you may work in various domains like manufacturing, It, healthcare, telecom, and more. Also, most of the students get 200 percent hike after completing this course. The average you will get 6 lac p.a. and for a little more efforts you may acquire salary packages up to 12 lacs p.a.

Tech Mahindra

Admission Process

date timeImportant Date & Time

You can apply for the master program online at our site. Mark the important date and time related to the program and stay in touch with our team to get the information about the program in detail.

enrollEnrollment Criteria

Once you submit your profile online, it will be reviewed by our expert team closely for the eligibility like graduation degree, basic programming skills, etc. Eligible candidates can move to the next step quickly.

finalFinal Enrollment Process

Eligible candidates have to appear for the online assessment based on your graduation and basic programming knowledge. Candidates who clear the exam will appear for the interview and finally they can join the program.

Get a chance to win a scholarship up to
₹ 29,750 (Excluding of GST)

Frequently Asked Questions

  • Passion for learning
  • Go-getter attitude
  • Basic computer knowledge
  • Fundamental knowledge about Tableau and data analytics

No, you don't have to know programming or coding for doing this course.

  • Lots of job opportunities
  • Enhances firm's productivity
  • Helps in preventing frauds
  • Helps in retrieving actionable insights

  • User-friendly UI
  • Great visualizations
  • Easy to use
  • It can work with massive data sets without any difficulty

  • ISO certified training institute
  • Project-based training
  • Industry recognized certification
  • Learn under a Tableau and data analytics expert

6 Months.

You can earn ₹6 LPA to 17 LPA as a data analyst.

If you like our Curriculum

What You will get Benefit
from this Program

  • Simulation Test Papers
  • Industry Case Studies
  • 61,640+ Satisfied Learners
  • 140+ Training Courses
  • 100% Certification Passing Rate
  • Live Instructor Online Training
  • 100% Placement Assistance
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