Professional in Data Science

Master's Program 4.9 out of 5 rating votes 5862

Data science is a field that uses scientific methods, processes & algorithms and systems to extract knowledge from structured &.unstructured data apply on of application domains.

INR 48000

Excluding GST

100% Placement


Ranked#2 among Top Full-Time

Professional in Data Science in India- 2010-2023

star date
Start Date : 27th Mar 2023Duration : 6 Months
Format : Live Online /Self-Paced/Classroom
Future of Professional in Data Science

₹6 LPA to ₹ 35 LPA

A fresher Data Scientist can earn almost ₹6,00,000 per year. On the other hand, an experienced can earn almost ₹35,00,000 per year.


Job Opportunities

There is huge demand of Data Science professionals in Industry. As per a survey, around 190k data science jobs are created every year.


Future Analytics

As per a survey, more than 15 million Data Scientist jobs will be created in the field of data science by the year 2026 around the globe


Professional in Data Science

4.9 out of 5 rating vote 5862

Data science is a field that uses scientific methods, processes & algorithms and systems to extract knowledge from structured &.unstructured data apply on of application domains..

INR 48000 + GST
100% Placement Assistance

Program Overview

The data science professional training program will help you master the key skills that are necessary for becoming an expert in data science. In this course, you will learn about ML, DL, statistics, python, etc. Moreover, you will learn to develop data models for analyzing data and extracting useful/meaningful insights. You will also become proficient in performing linear and logistic regression and cluster & factor analysis. After completing the data science professional training program, you may get various types of job opportunities in big organizations. For example, you may get an opportunity to work as an:

  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • Marketing Analyst
  • Functional Analyst

There is a huge demand for competent data science professionals in the market. Students who complete the data science professional training program may get various types of roles and jobs in an organization. This is because of the benefits that a data science professional provides to a company or organization. This is why many organizations are more than happy to give big paychecks to data science professionals for their services.

  • Web_IconAs per a survey, around 190k data science jobs are created every year. And the number is increasing day by day.
  • branData experts who pursue their careers in the data science field can get various types of roles and jobs in an organization such as Data Scientist, Marketing Analyst, Functional Analyst.
  • polyAs per the study of McKinsey Global Institute, there is a shortage of 2 lakh data science experts in the world.
  • analyticsAs per a survey, more than 15 million Data Scientist jobs will be created in the field of data science by the year 2026 around the globe

The demand for data science professionals is increasing in the market with every passing day. This is because of the benefits that an organization gets from the service of a data scientist. By joining this course, you will acquire all the skills that are essential/important for becoming an expert data science professional. Furthermore, you will learn to develop data models for analyzing data and extracting useful/meaningful insights.

m IconWith project-based training under an expert data scientist, you will acquire all the skills that a competent data scientist must have.

 m IconStudents who join the data science professional training program can guarantee themselves a fulfilling and successful career as a data science professional. Moreover, you will earn a hefty remuneration as a data scientist. On average, a data scientist can earn around ₹6,00,000-₹22,00,000 PA.

icon 3As per a survey, the data science industry will create around 11.5 million jobs by the year 2026.

The data science professional training program aims to provide quality data science education to aspiring data scientists and make them experts in working with data. Additionally, you will learn to work with various data collection and data visualization tools and software.
Things you will learn:

  • Fundamentals of data science
  • How to work with various data collection and data visualization tools?
  • Major duties of a data scientist
  • How to perform cluster and factor analysis?
  • Python, ML, DL, etc.

The main objective of the data science professional training program is to make aspiring data experts competent data scientists. The course covers all the concepts and skills that a skilled data professional must master. The training program fulfills the emerging demands of the data science industry and is developed in partnership with working data science professionals.

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Tools Covered of Professional in Data Science

Power BI
Deep Learning
Machen Learning
Artificial Intelligence
Data Science

Professional in Data Science Curriculum

Course 1
Python for Data Science

Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.


Course Content

    • Introduction To Python
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
    • Python Keyword and Identiers
      • Python Comments, Multiline Comments.
      • Python Indentation
      • Understating the concepts of Operators
        • Arithmetic
        • Relational
        • Logical
        • Assignment
        • Membership
        • Identity
    • 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 using Numeric data types
      • Using string data type and string operations
      • Understanding Non-numeric data types
      • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets
    • Control Structure & Flow
      • Statements – if, else, elif
      • How to use nested IF and Else in Python
      • Loops
      • Loops and Control Statements.
      • Jumping Statements – Break, Continue, 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 Loop in Python
      • Use If and Else in for and While Loop
      • Examples of Looping with Break and Continue Statement
      • How to use IN or NOT IN keyword in Python Loop.
    • Python Function, Modules and Packages
      • Python Syntax
      • Function Call
      • Return Statement
      • Arguments in a function – Required, Default, Positional, Variable-length
      • Write an Empty Function in Python –pass statement.
      • Lamda/ Anonymous Function
      • *args and **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
      • Random functions in python
      • Programming using functions, modules & external packages
      • Map, Filter and Reduce function with Lambda Function
      • More example of Python Function
    • Python Date Time and Calendar
      • Day, Month, Year, Today, Weekday
      • IsoWeek day
      • Date Time
      • Time, Hour, Minute, Sec, Microsec
      • Time Delta and UTC
      • StrfTime, Now
      • Time stamp and Date Format
      • Month Calendar
      • Itermonthdates
      • Lots of Example on Python Calendar
      • Create 12-month Calendar
      • Strftime
      • Strptime
      • Format Code list of Data, Time and Cal
      • Locale’s appropriate date and time
    • 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
    • Tuple
      • What is Tuple
      • Tuple Creation
      • Accessing Elements in Tuple
      • Changing a Tuple
      • Tuple Deletion
      • Tuple Count
      • Tuple Index
      • Tuple Membership
      • TupleBuilt in Function (Length, Sort)
    • 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
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
      • Frozen Sets
    • Strings
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
    • Python Exception Handling
      • Python Errors and Built-in-Exceptions
      • Exception handing Try, Except and Finally
      • Catching Exceptions in Python
      • Catching Specic 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 Database Interaction
      • SQL Database connection using
      • Creating and searching tables
      • Reading and Storing cong information on database
      • Programming using database connections
    • Contacting user Through Emails Using Python
      • Installing SMTP Python Module
      • Sending Email
      • Reading from le and sending emails to all users
    • Reading an excel
      • Working With Excel
      • Reading an excel le using Python
      • Writing to an excel sheet using Python
      • Python| Reading an excel le
      • Python | Writing an excel le
      • Adjusting Rows and Column using Python
      • ArithmeticOperation in Excel le.
      • Play with Workbook, Sheets and Cells in Excel using Python
      • Creating and Removing Sheets
      • Formatting the Excel File Data
      • More example of Python Function
    • Complete Understanding of OS Module of Python
      • Check Dirs. (exist or not)
      • How to split path and extension
      • How to get user prole detail
      • Get the path of Desktop, Documents, Downloads etc.
      • Handle the File System Organization using OS
      • How to get any les and folder’s details using OS
Course 2
Data Analysis and 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

    • Data Analysis and Visualization using Pandas.
      • Statistics
        • Categorical Data
        • Numerical Data
        • Mean
        • Median
        • Mode
        • Outliers
        • Range
        • Interquartile range
        • Correlation
        • Standard Deviation
        • Variance
        • Box plot
      • Pandas
        • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
        • How to get record specic 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 les 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 Aggre gate Function
        • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (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 Data Frame and 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)
    • Data Analysis and Visualization using NumPy and MatPlotLib
      • NumPy
        • Introduction to NumPy Numerical Python
        • Importing NumPy and Its Properties
        • NumPy Arrays
        • Creating an Array from a CSV
        • Operations an Array from a CSV
        • 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’s Mean and Axis
        • NumPy’s Mode, Median and Sum Function
        • NumPy’s Sort 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 Data Visualization with Seaborn
      • 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 whisk
        • 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
        • Well done! What’s next
Course 3
Databases – MS SQL and SQL Queries

Microsoft SQL Server is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool, and to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.


Course Content

    • SQL Server Fundamentals
      • SQL Server 2019 Installation
      • Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
      • SQL Server Features & Purpose
      • Using Management Studio (SSMS)
      • Conguration Tools & SQLCMD
      • Conventions & Collation
    • SQL Server 2019 Database Design
      • SQL Database Architecture
      • Database Creation using GUI
      • Database Creation using T-SQL scripts
      • DB Design using Files and File Groups
      • File locations and Size parameters
      • Database Structure modications
    • SQL Tables in MS SQL Server
      • SQL Server Database Tables
      • Table creation using T-SQL Scripts
      • Naming Conventions for Columns
      • Single Row and Multi-Row Inserts
      • Table Aliases
      • Column Aliases & Usage
      • Table creation using Schemas
      • Basic INSERT
      • UPDATE
      • DELETE
      • SELECT queries and Schemas
      • Use of WHERE, IN and BETWEEN
      • Variants of SELECT statement
      • ORDER BY
      • GROUPING
      • HAVING
      • ROWCOUNT and CUBE Functions
    • Data Validation and Constraints
      • Table creation using Constraints
      • NULL and IDENTITY properties
      • UNIQUE KEY Constraint and NOT NULL
      • PRIMARY KEY Constraint & Usage
      • CHECK and DEFAULT Constraints
      • Naming Composite Primary Keys
      • Disabling Constraints & Other Options
    • Views and Row Data Security
      • Benets of Views in SQL Database
      • Views on Tables and Views
      • Issues with Views and ALTER TABLE
      • Common System Views and Metadata
      • Common Dynamic Management views
      • Working with JOINS inside views
    • Indexes and Query tuning
      • Need for Indexes & Usage
      • Indexing Table & View Columns
      • Index SCAN and SEEK
      • INCLUDED Indexes & Usage
      • Materializing Views (storage level)
      • Composite Indexed Columns & Keys
      • Indexes and Table Constraints
      • Primary Keys & Non-Clustered Indexes
    • Stored Procedures and Benets
      • Why to use Stored Procedures
      • Types of Stored Procedures
      • Use of Variables and parameters
      • INPUT and OUTPUT parameters
      • System level Stored Procedures
      • Dynamic SQL and parameterization
    • System functions and Usage
      • Scalar Valued Functions
      • Types of Table Valued Functions
      • System Functions and usage
      • Date Functions
      • Time Functions
      • String and Operational Functions
      • ROW_COUNT
      • GROUPING Functions
    • Triggers, cursors, memory limitations
      • Why to use Triggers
      • DML Triggers and Performance impact
      • INSERTED and DELETED memory tables
      • Data Audit operations & Sampling
      • Database Triggers and Server Triggers
      • Bulk Operations with Triggers
    • Cursors and Memory Limitations
      • Cursor declaration and Life cycle
      • STATIC
      • DYNAMIC
      • SCROLL Cursors
      • FORWARD_ONLY and LOCAL Cursors
      • KEYSET Cursors with Complex SPs
    • Transactions Management
      • ACID Properties and Scope
      • EXPLICIT Transaction types
      • IMPLICIT Transactions and options
      • AUTOCOMMIT Transaction and usage
      • SAVEPOINT and Query Blocking
Course 4
Statistics for Data Science

This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.


Course Content

    • Introduction to Data Science
      • What is Analytics & Data Science
      • Common Terms in Data Science
      • What is data
      • Classication of data
      • Relevance in industry and need of the hour
      • Types of problems and business objectives in various industries
      • How leading companies are harnessing the power of analytics
      • Critical success drivers.
      • Overview of Data Science tools & their popularity.
      • Data Science Methodology & problem-solving framework.
      • List of steps in Data Science projects
      • Identify the most appropriate solution design for the given problem statement
      • Project plan for Data Science project & key milestones based on effort estimates
      • Build Resource plan for Data Science project
      • Why Python for data science
    • Accessing/Importing and Exporting Data
      • Importing Data from various sources (Csv, txt, excel, access etc)
      • Database Input (Connecting to database)
      • Viewing Data objects - sub setting, methods
      • Exporting Data to various formats
      • Important python modules Pandas
    • Data Manipulation Cleansing - Munging Using Python Modules
      • Cleansing Data with Python
      • Filling missing values using lambda function and concept of Skewness.
      • Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
      • Normalizing data
    • Feature Engineering in Data Science
      • Feature Engineering
      • Feature Selection
      • Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
      • Label encoding/one hot encoding
    • Data Analysis Visualization Using Python
      • Introduction exploratory data analysis
      • Descriptive statistics, Frequency Tables and summarization
      • Univariate Analysis (Distribution of data & Graphical Analysis)
      • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
      • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)
      • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
    • Introduction to Statistics
      • Descriptive Statistics
      • Sample vs Population Statistics
      • Random variables
      • Probability distribution functions
      • Expected value
      • Normal distribution
      • Gaussian distribution
      • Z-score
      • Spread and Dispersion
      • Correlation and Co-variance
    • Introduction to Predictive Modelling
      • Concept of model in analytics and how it is used
      • Common terminology used in Analytics & Modelling process
      • Popular Modelling algorithms
      • Types of Business problems - Mapping of Techniques
      • Different Phases of Predictive Modelling
    • EDA (Exploratory Data Analysis)
      • Need for structured exploratory data
      • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
      • Identify missing data
      • Identify outliers’ data
      • Imbalanced Data Techniques
    • Data Pre-Processing & Data Mining
      • Data Preparation
      • Feature Engineering
      • Feature Scaling
      • Datasets
      • Dimensionality Reduction
      • Anomaly Detection
      • Parameter Estimation
      • Data and Knowledge
      • Selected Applications in Data Mining
Course 5
Mastering Machine Learning

Machine learning courses help to understand the complete concepts behind the processing of Artificial intelligence and Computer science. With the Machine learning course, you will cover topics based on supervised and unsupervised learning along with the development of software and algorithms to extract predictions based on data.


Course Content

    • Introduction to Machine Learning
      • Machine Learning
      • Machine Learning Algorithms
      • Algorithmic models of Learning
      • Applications of Machine Learning
      • Large Scale Machine Learning
      • Computational Learning theory
    • Techniques of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised and Reinforcement Learning
      • Bias and variance Trade-off
      • Representation Learning
    • Regression
      • Regression and its Types
      • Logistic Regression
      • Linear Regression
      • Polynomial Regression
    • Classication
      • Meaning and Types of Classication
      • Probability and Bayes Theorem
      • Support Vector Machines
      • Naive Bayes
      • Decision Tree Classier
      • Random Forest Classier
Course 6
Understanding Deep Learning

Deep learning is the most effective skill in AI. The course is intended to provide a complete foundation over the deep learning algorithms that help you to understand the process to build neural networks. The course of deep learning will help you to successfully handle the Machine learning projects needed by the organization today.


Course Content

    • Introduction to Deep Learning
      • What are the Limitations of Machine Learning
      • What is Deep Learning
      • Advantage of Deep Learning over Machine learning
      • Reasons to go for Deep Learning
      • Real-Life use cases of Deep Learning
    • Deep Learning Networks
      • What is Deep Learning Networks
      • Why Deep Learning Networks
      • How Deep Learning Works
      • Feature Extraction
      • Working of Deep Network
      • Training using Backpropagation
      • Variants of Gradient Descent
Course 7
Microsoft Power BI

The Power BI course assists the user to understand the way to install Power BI desktop also by understanding and developing the workshop and insights using the data. It offers tools and techniques that are used to visualize and analyze data. The course will help you to learn and grab insights on everything an organization need; to manage the data with Power BI.


Course Content

    • Introduction to Power BI
      • Overview of BI concepts
      • Why we need BI
      • Introduction to SSBI
      • SSBI Tools
      • Why Power BI
      • What is Power BI
      • Building Blocks of Power BI
      • Getting started with Power BI Desktop
      • Get Power BI Tools
      • Introduction to Tools and Terminology
      • Dashboard in Minutes
      • Interacting with your Dashboards
      • Sharing Dashboards and Reports
    • Power BI Desktop
      • Power BI Desktop
      • Extracting data from various sources
      • Workspaces in Power BI
    • Power BI Data Transformation
      • Data Transformation
      • Query Editor
      • Connecting Power BI Desktop to our Data Sources
      • Editing Rows
      • Understanding Append Queries
      • Editing Columns
      • Replacing Values
      • Formatting Data
      • Pivoting and Unpivoting Columns
      • Splitting Columns
      • Creating a New Group for our Queries
      • Introducing the Star Schema
      • Duplicating and Referencing Queries
      • Creating the Dimension Tables
      • Entering Data Manually
      • Merging Queries
      • Finishing the Dimension Table
      • Introducing the another DimensionTable
      • Creating an Index Column
      • Duplicating Columns and Extracting Information
      • Creating Conditional Columns
      • Creating the FACT Table
      • Performing Basic Mathematical Operations
      • Improving Performance and Loading Data into the Data Model
    • Modelling with Power BI
      • Introduction to Modelling
      • Modelling Data
      • Manage Data Relationship
      • Optimize Data Models
      • Cardinality and Cross Filtering
      • Default Summarization & Sort by
      • Creating Calculated Columns
      • Creating Measures & Quick Measures
    • Data Analysis Expressions (DAX)
      • What is DAX
      • Data Types in DAX
      • Calculation Types
      • Syntax, Functions, Context Options
      • DAX Functions
        • Date and Time
        • Time Intelligence
        • Information
        • Logical
        • Mathematical
        • Statistical
        • Text and Aggregate
      • Measures in DAX
      • Measures and Calculated Columns
      • ROW Context and Filter Context in DAX
      • Operators in DAX - Real-time Usage
      • Quick Measures in DAX - Auto validations
      • In-Memory Processing DAX Performance
    • Power BI Desktop Visualisations
      • How to use Visual in Power BI
      • What Are Custom Visuals
      • Creating Visualisations and Colour Formatting
      • Setting Sort Order
      • Scatter & Bubble Charts & Play Axis
      • Tooltips and Slicers, Timeline Slicers & Sync Slicers
      • Cross Filtering and Highlighting
      • Visual, Page and Report Level Filters
      • Drill Down/Up
      • Hierarchies and Reference/Constant Lines
      • Tables, Matrices & Conditional Formatting
      • KPI's, Cards & Gauges
      • Map Visualizations
      • Custom Visuals
      • Managing and Arranging
      • Drill through and Custom Report Themes
      • Grouping and Binning and Selection Pane, Bookmarks & Buttons
      • Data Binding and Power BI Report Server
    • Introduction to Power BI Dashboard and Data Insights
      • Why Dashboard and Dashboard vs Reports
      • Creating Dashboards
      • Conguring a Dashboard Dashboard Tiles, Pinning Tiles
      • Power BI Q&A
      • Quick Insights in Power BI
    • Direct Connectivity
      • Custom Data Gateways
      • Exploring live connections to data with Power BI
      • Connecting directly to SQL Server
      • Connectivity with CSV & Text Files
      • Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
      • Content packs
      • Update content packs
    • Publishing and Sharing
      • Introduction and Sharing Options Overview
      • Publish from Power BI Desktop and Publish to Web
      • Share Dashboard with Power BI Service
      • Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
      • Print or Save as PDF and Row Level Security (Power BI Pro)
      • Export Data from a Visualization
      • Export to PowerPoint and Sharing Options Summary
    • Refreshing Datasets
      • Understanding Data Refresh
      • Personal Gateway (Power BI Pro and 64-bit Windows)
      • Replacing a Dataset and Troubleshooting Refreshing
Course 8
Data Science - Live Projects

The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data science projects based on a high-level perspective helping you to understand and articulate the innovative solutions for topical real-time data science projects.


Course Content

    • Here is the project list you will going to work on
      • Managing credit card Risks
      • Bank Loan default classification
      • YouTube Viewers prediction
      • Super store Analytics (E-commerce)
      • Buying and selling cars prediction (like OLX process)
      • Advanced House price prediction
      • Analytics on HR decisions
      • Survival of the fittest
      • Twitter Analysis
      • Flight price prediction
CertificatesMaster's Program Certificate

You will get certificate after completion of program

Course Structure
  • - 6 Months Online Program
  • - 90+ Hours of Intensive Learning
  • - 10+ Assigments & 4+ Projects
  • - 2 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

The knowledge and skill you've gained working on projects, simulation, case studies will set you ahead of competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.

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 Professional in Data Science

There is a big demand for competent data science professionals in the market, and as per surveys, this demand is going to increase even further in the coming years. According to a survey, around 190k data science jobs are created every year. In short, there is no shortage of jobs and growth opportunities in the data science field. Besides this, a data scientist can earn a respectable amount of money by working for an organization. On average, a data science professional can earn approximately ₹6,00,000- ₹22,00,000 per year.


A data analyst collects and cleans data and interprets it to answer a question or to solve a business problem. He is responsible for mining data, designing data systems, and using various statistical tools for interpreting data. A data analyst must have knowledge of SQL, Python, Tableau, etc. A data analyst, on average, can earn approximately ₹2,00,000 – ₹12,00,000 per year.

Data scientists are data experts that work with large volumes of data. They work in partnership with stakeholders to identify issues and use business data to give solutions for identified issues. Additionally, they design algorithms for merging, managing, and extracting data for creating reports. A data scientist must have knowledge of data science, mathematics, statistics, and operational research. A data scientist, on average, can earn approximately ₹6,00,000 – ₹22,00,000 per year.

A machine learning engineer develops self-running software for a firm to automate predictive models. They develop AI systems that use big data sets for developing algorithms that are capable of learning themselves and making accurate predictions on the basis of given data. A machine learning engineer must have knowledge of applied mathematics, computer science, ML, neural networks, etc. A machine learning engineer can earn approximately ₹7,00,000 – ₹20,00,000 per year.

A marketing analyst tracks the advertising cost of a firm, research consumer behavior, and explore market trends for finding opportunities. A competent marketing analyst must be proficient in running PPC campaigns and processing/analyzing the marketing data of a firm. On average, a marketing analyst can earn approximately ₹4,00,000- ₹16,00,000 per year.

A functional analyst analyzes a company's processes for fulfilling the needs and requirements of customers. A functional analyst acts as a link between the end-users and the technical team who is responsible for developing the product. A functional analyst must have knowledge about IT principles, advanced excel, SQL, etc. On average, a functional analyst can earn approximately ₹7,00,000- ₹17,00,000 per year

Croma Campus has tie-ups with more than 150 companies such as Wipro, HP, Tech Mahindra, HCL, etc. Thus, by completing your training from here, you may get a chance to work as a data scientist in some of the leading companies in the world. Besides this, once you complete your training, you may get placed with a salary package of ₹6,00,000 or more.


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.


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
₹ 45,600 (Excluding of GST)

Frequently Asked Questions

  • Passion for learning
  • Go-getter attitude
  • Basic computer knowledge
  • Basic data science knowledge

  • High demand
  • Lots of job opportunities
  • Great pay
  • Recognition

6 Months

Yes, you can learn data science even if you are not good at programming.

A data science professional can earn approximately ₹6,00,000- ₹ 35,00,000 per year.

You will get training from an expert data science professional.

  • ISO certified training institute
  • Project-based training
  • Industry recognized certification
  • Learn under a skilled data science professional

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
I’m interested in the program

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