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 42000

Excluding GST

100% Placement



Ranked#2 among Top Full-Time

Professional in Data Science in India- 2010-2022

star date
Start Date : 12th Dec 2022Duration : 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 42000 + 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

Professional in Data Science Curriculum

Course 1
Understanding Data Science Professional Program

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

    • In this program you will learn:
      • Python Statistics for Data Science
      • Databases – MySQL and SQL Queries
      • Data Science Professional Program
      • Machine Learning
      • Deep Learning
      • Power BI
      • Data Science Masters - Live Projects
Course 2
Python 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 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
    • Data Analysis & Visualization
      • 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
Databases – MySQL and SQL Queries

This module will help you to explore the query language SQL and its integration with My SQL to query the database. This module will help you to understand SQL access through data and the method to update and manipulate the data stored in the database. You will learn basic and advance concepts of MY SQL with complete practical exposure.


Course Content

    • Python - MySQL
      • Introduction to MySQL
      • What is the MySQLdb
      • How do I Install MySQLdb
      • Connecting to the MYSQL
      • Selecting a database
      • Adding data to a table
      • Executing multiple queries
      • Exporting and Importing data tables.
    • SQL Functions
      • Single Row Functions
      • Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
    • General Functions
      • Count, Average, Sum, Now etc.
    • Joining Tables
      • Obtaining data from Multiple Tables
      • Types of Joins (Inner Join, Left Join, Right Join & Full Join)
      • Sub-Queries Vs. Joins
    • Operators (Data using Group Function)
      • Distinct, Order by, Group by, Equal to etc.
    • Database Objects (Constraints & Views)
      • Not Null
      • Unique
      • Primary Key
      • Foreign Key
    • Structural & Functional Database Testing using TOAD Tool
      • SQL Basic
        • SQL Introduction
        • SQL Syntax
        • SQL Select
        • SQL Distinct
        • SQL Where
        • SQL And & Or
        • SQL Order By
        • SQL Insert
        • SQL Update
        • SQL Delete
      • SQL Advance
        • SQL Like
        • SQL Wildcards
        • SQL In
        • SQL Between
        • SQL Alias
        • SQL Joins
        • SQL Inner Join
        • SQL Left Join
        • SQL Right Join
        • SQL Full Join
        • SQL Union
      • SQL Functions
        • SQL Avg()
        • SQL Count()
        • SQL First()
        • SQL Last()
        • SQL Max()
        • SQL Min()
        • SQL Sum()
        • SQL Group By
Course 4
Data Science Professional Program

This course will help you to gain complete insights into the applied statistics, database systems, data preparation, and machine learning algorithms. The master in data science course will help you to gain a broad skill set to advance your career in respective fields such as data engineering, computer programming, and data architecture.


Course Content

    • Introduction to Data Science
      • What is Analytics & Data Science
      • Common Terms in Analytics
      • What is data
      • Classification 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 analytics tools & their popularity
      • Analytics Methodology & problem-solving framework
      • List of steps in Analytics projects
      • Identify the most appropriate solution design for the given problem statement
      • Project plan for Analytics project & key milestones based on effort estimates
      • Build Resource plan for analytics 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, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
      • Normalizing data
      • 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/ density 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
      • Central limit theorem
      • Spread and Dispersion
      • Inferential Statistics-Sampling
      • Hypothesis testing
      • Z-stats vs T-stats
      • Type 1 & Type 2 error
      • Confidence Interval
      • ANOVA Test
      • Chi Square Test
      • T-test 1-Tail 2-Tail Test
      • 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
    • Data Exploration for Modelling
      • 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
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
      • Artificial Intelligence
      • Machine Learning
      • Machine Learning Algorithms
      • Algorithmic models of Learning
      • Applications of Machine Learning
      • Large Scale Machine Learning
      • Computational Learning theory
      • Reinforcement Learning
    • 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
    • Classification
      • Meaning and Types of Classification
      • Nearest Neighbor Classifiers
      • K-nearest Neighbors
      • Probability and Bayes Theorem
      • Support Vector Machines
      • Naive Bayes
      • Decision Tree Classifier
      • Random Forest Classifier
    • Unsupervised Learning: Clustering
      • About Clustering
      • Clustering Algorithms
      • K-means Clustering
      • Hierarchical Clustering
      • Distribution Clustering
    • Model optimization and Boosting
      • Ensemble approach
      • K-fold cross validation
      • Grid search cross validation
      • Ada boost and XG Boost
Course 6
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
      • Types of Deep Networks
      • Feed forward neural networks (FNN)
      • Convolutional neural networks (CNN)
      • Recurrent Neural networks (RNN)
      • Generative Adversal Neural Networks (GAN)
      • Restrict Boltzman Machine (RBM)
    • Deep Learning with Keras
      • Define Keras
      • How to compose Models in Keras
      • Sequential Composition
      • Functional Composition
      • Predefined Neural Network Layers
      • What is Batch Normalization
      • Saving and Loading a model with Keras
      • Customizing the Training Process
      • Intuitively building networks with Keras
    • Convolutional Neural Networks (CNN)
      • Introduction to Convolutional Neural Networks
      • CNN Applications
      • Architecture of a Convolutional Neural Network
      • Convolution and Pooling layers in a CNN
      • Understanding and Visualizing CNN
      • Transfer Learning and Fine-tuning Convolutional Neural Networks
    • Recurrent Neural Network (RNN)
      • Intro to RNN Model
      • Application use cases of RNN
      • Modelling sequences
      • Training RNNs with Backpropagation
      • Long Short-Term Memory (LSTM)
      • Recursive Neural Tensor Network Theory
      • Recurrent Neural Network Model
      • Time Series Forecasting
    • Natural Language Processing
      • NLP with python
      • Bags of words
      • Stemming
      • Tokenization
      • Lemmatization
      • TF-IDF
      • Sentiment Analysis
    • Overview of Tensor Flow
      • What is Tensor Flow
      • Tensor Flow code-basics
      • Graph Visualization
      • Constants, Placeholders, Variables
      • Tensor flow Basic Operations
      • Linear Regression with Tensor Flow
      • Logistic Regression with Tensor Flow
      • K Nearest Neighbor algorithm with Tensor Flow
      • K-Means classifier with Tensor Flow
      • Random Forest classifier with Tensor Flow
    • Neural Networks Using Tensor Flow
      • Quick recap of Neural Networks
      • Activation Functions, hidden layers, hidden units
      • Illustrate & Training a Perceptron
      • Important Parameters of Perceptron
      • Understand limitations of A Single Layer Perceptron
      • Illustrate Multi-Layer Perceptron
      • Back-propagation – Learning Algorithm
      • Understand Back-propagation – Using Neural Network Example
      • TensorBoard
Course 7
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
      • Refreshing Power BI Service Data
      • Interacting with your Dashboards
      • Sharing Dashboards and Reports
    • Power BI Desktop
      • Power BI Desktop
      • Power BI Dashboards
      • Power BI Q & A
      • Extracting data from various sources
      • Workspaces in Power BI
      • Data Transformation
      • Measures and Calculated Columns
      • Query Editor
    • 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
    • Publishing and Sharing
      • Introduction and Sharing Options Overview
      • Publish from Power BI Desktop and Publish to Web
      • Share Dashboard with Power BI Service
      • Workspaces and Apps (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 and Publishing for Mobile Apps
      • 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 Professional Programs - 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

    • Data Science Masters - Live Projects
      • 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

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


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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.

Big Basket
Global Logic
Magic Software
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
₹ 35,700 (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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