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.
Excluding GST
Professional in Data Science in India- 2010-2025
Format : Live Online /Self-Paced/Classroom
- Course 1Analytics with Excel
- Course 2Databases - MS SQL and SQL Queries
- Course 3Analytics Microsoft Power BI
- Course 4Python for Data Science
- Course 5Understanding Deep Learning for AI
- Master's Certificate
₹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
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..
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.
As per a survey, around 190k data science jobs are created every year. And the number is increasing day by day.
Data 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.-
As per the study of McKinsey Global Institute, there is a shortage of 2 lakh data science experts in the world. 
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
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.
With project-based training under an expert data scientist, you will acquire all the skills that a competent data scientist must have.
Students 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.
As 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.
Phone (For Voice Call):
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+918287060032Tools Covered of Professional in Data Science
Professional in Data Science Curriculum
Understanding Concepts of Excel
Ms Excel Advance
MIS Reporting & Dash Board
What is Macro
Recording a Macro
Different Components of a Macro
What is VBA and how to write macros in VBA.
Course Content
- 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
- 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
- Dashboard Background
- Dashboard Elements
- Interactive Dashboards
- Type of Reporting In India
- Industry Related Dashboard
- Indian Print Media Reporting
- Understanding Macros
- Recording a Macro
- User Form
- Title
- Module
- Writing a simple macro
- Apply arithmetic operations on two cells using macros.
- How to align the text using macros.
- How to change the background color of the cells using macros.
- How to change the border color and style of the cells using macros.
- Use cell referencing using macros.
- How to copy the data from one cell and paste it into another.
- How to change the font color of the text in a cell using macros
SQL Server Fundamentals
SQL Server 2019 Database Design
SQL Tables in MS SQL Server
Data Validation and Constraints
Views and Row Data Security
Indexes and Query tuning
Stored Procedures and Benets
System functions and Usage
Triggers, cursors, memory limitations
Cursors and Memory Limitations
Transactions Management
Course Content
- 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 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 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
- 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
- Benets of Views in SQL Database
- Views on Tables and Views
- SCHEMA BINDING and ENCRYPTION
- Issues with Views and ALTER TABLE
- Common System Views and Metadata
- Common Dynamic Management views
- Working with JOINS inside views
- 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
- Why to use Stored Procedures
- Types of Stored Procedures
- Use of Variables and parameters
- SCHEMABINDING and ENCRYPTION
- INPUT and OUTPUT parameters
- System level Stored Procedures
- Dynamic SQL and parameterization
- Scalar Valued Functions
- Types of Table Valued Functions
- SCHEMABINDING and ENCRYPTION
- System Functions and usage
- Date Functions
- Time Functions
- String and Operational Functions
- ROW_COUNT
- GROUPING Functions
- 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
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
Introduction to Power BI
Power BI Desktop
Power BI Data Transformation
Modelling with Power BI
Data Analysis Expressions (DAX)
Power BI Desktop Visualisations
Introduction to Power BI Dashboard and Data Insights
Direct Connectivity
Publishing and Sharing
Refreshing Datasets
Course Content
- 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
- Extracting data from various sources
- Workspaces in Power BI
- 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
- 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
- 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
- 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
- Why Dashboard and Dashboard vs Reports
- Creating Dashboards
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Power BI Q&A
- Quick Insights in Power BI
- 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
- 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
- Understanding Data Refresh
- Personal Gateway (Power BI Pro and 64-bit Windows)
- Replacing a Dataset and Troubleshooting Refreshing
Introduction To Python
Python Keyword and Identiers
Introduction To Variables
Python Data Type
Control Structure & Flow
List
Tuple
Dictionary
Sets
Strings
Python Function, Modules and Packages
Decorator, Generator and Iterator
Python Exception Handling
Python File Handling
Memory management using python
Python Database Interaction
Reading an excel
Complete Understanding of OS Module of Python
Course Content
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- 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
- 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
- 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.
- 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 Reverse
- 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
- 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)
- 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.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- 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
- Creation and working of decorator
- Idea and practical example of generator, use of generator
- Concept and working of Iterator
- 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
- 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
- Threading, Multi-threading
- Memory management concept of python
- working of Multi tasking system
- Different os function with thread
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- 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
- 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
Introduction to Text Mining and NLP
Extracting, Cleaning and Preprocessing Text
Analyzing Sentence Structure
Text Classification - I
Getting Started with TensorFlow 2.0
Introduction to Deep Learning
Neural Networks
Convolution Neural Network
Image Processing and Computer Vision
Regional CNN
Introduction to RNN and GRU
RNN, LSTM
Introduction to Generative AI & Transfer Learning
Boltzmann Machine & Autoencoder
BERT Algorithm
Course Content
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Activation Function
- Layer Types
- Model Compilation
- Model Optimizer
- Model Loss Function
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
- What is Deep Learning
- Curse of Dimensionality
- Machine Learning vs. Deep Learning
- Use cases of Deep Learning
- Human Brain vs. Neural Network
- What is Perceptron
- Learning Rate
- Epoch
- Batch Size
- Activation Function
- Single Layer Perceptron
- What is NN
- Types of NN
- Creation of simple neural network using tensorflow
- Image Classification Example
- What is Convolution
- Convolutional Layer Network
- Convolutional Layer
- Filtering
- ReLU Layer
- Pooling
- Data Flattening
- Fully Connected Layer
- Predicting a cat or a dog
- Saving and Loading a Model
- Face Detection using OpenCV
- Introduction to Vision
- Importance of Image Processing
- Image Processing Challenges Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories
- Introduction to Image Image Transformation, Image Processing Operations & Simple Point Operations
- Noise Reduction Moving Average & 2D Moving Average
- Image Filtering Linear & Gaussian Filtering
- Disadvantage of Correlation Filter
- Introduction to Convolution
- Boundary Effects Zero, Wrap, Clamp & Mirror
- Image Sharpening
- Template Matching
- Edge Detection Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
- Effect of Noise
- Laplacian Filter
- Smoothing with Gaussian
- LOG Filter Blob Detection
- Noise Reduction using Salt & Pepper Noise using Gaussian Filter
- Nonlinear Filters
- Bilateral Filters
- Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding
- Image Sampling & Interpolation Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
- Image Interpolation Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation
- Introduction to the dnn module
- Deep Learning Deployment Toolkit
- Use of DLDT with OpenCV4.0
- OpenVINO Toolkit
- Introduction
- Model Optimization of pre-trained models
- Inference Engine and Deployment process
- Regional-CNN
- Selective Search Algorithm
- Bounding Box Regression
- SVM in RCNN
- Pre-trained Model
- Model Accuracy
- Model Inference Time
- Model Size Comparison
- Transfer Learning
- Object Detection Evaluation
- mAP
- IoU
- RCNN Speed Bottleneck
- Fast R-CNN
- RoI Pooling
- Fast R-CNN Speed Bottleneck
- Faster R-CNN
- Feature Pyramid Network (FPN)
- Regional Proposal Network (RPN)
- Mask R-CNN
- Issues with Feed Forward Network
- Recurrent Neural Network (RNN)
- Architecture of RNN
- Calculation in RNN
- Backpropagation and Loss calculation
- Applications of RNN
- Vanishing Gradient
- Exploding Gradient
- What is GRU
- Components of GRU
- Update gate
- Reset gate
- Current memory content
- Final memory at current time step
- What is LSTM
- Structure of LSTM
- Forget Gate
- Input Gate
- Output Gate
- LSTM architecture
- Types of Sequence-Based Model
- Sequence Prediction
- Sequence Classification
- Sequence Generation
- Types of LSTM
- Vanilla LSTM
- Stacked LSTM
- CNN LSTM
- Bidirectional LSTM
- How to increase the efficiency of the model
- Backpropagation through time
- Workflow of BPTT
- What is Boltzmann Machine (BM)
- Understanding Autoencoders
- Architecture of Autoencoders
- Brief on types of Autoencoders
- Applications of Autoencoders
- What is BERT
- Brief on types of BERT
- Applications of BERT
Master's Program Certificate
You will get certificate after completion of program
- - 6 Months Online Program
- - 90+ Hours of Intensive Learning
- - 10+ Assigments & 5+ Projects
- - 5 Live Projects
- - 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
Our Master program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
The knowledge and skill you've gained working on projects, simulation, case studies will set you ahead of competition.
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
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.
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
Important Date & TimeYou 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.
Enrollment 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.
Final Enrollment ProcessEligible 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.
₹ 33,250 (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
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