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Data Science Online Course

Data Science Course with Placement | Online Training & Classes in India

The Data Science Online Course teaches you everything required by today’s companies. The course covers the fundamentals and then goes on to more advanced subjects like machine learning, artificial intelligence, data visualization, prediction analytics, & generative AI.

Duration: 28 to 32 weeks | Mode: Live + Recorded Sessions

Check Data Science Demo Class

You can attend a demo session of our Data Science Online Training in India before enrolling.

Our Recently Placed Students in Data Science Course

Devanshi Kapoor

Placed at Cognizant

Anika Das

Placed at IBM

Ishaan Choudhary

Placed at Accenture

Krishna Menon

Placed at Wipro

Tanvi Reddy

Placed at Infosys

Siddharth Bhatia

Placed at Capgemini

Mira Nair

Placed at Deloitte

Vivaan Roy

Placed at Tech Mahindra

About the Data Science Online Course

Data Science involves using data to gain insights and find solutions to business-related problems. Every day, companies accumulate lots of data that requires professional understanding and analysis to generate value from the collected data sets.

A Data Scientist applies statistics, coding, data analysis, and machine learning techniques to investigate and analyze the data to offer valuable findings from the investigation. Currently, data science is becoming increasingly significant in several industries such as the healthcare sector, financial institutions, retail firms, e-commerce, education, manufacturing, and technology.

Due to the increased adoption of artificial intelligence and automation systems, there is an increasing need for professionals with the necessary skills in data science. Pursuing data science studies opens up different career prospects that have attractive salaries.

Why Choose Our Data Science?
  • Live training via the internet from qualified experts
  • Classes are scheduled according to student convenience
  • Sessions are recorded for future study
  • Lifelong access to course materials
  • Practical experience working with actual data sets
  • Application of the latest technologies in use today
  • Actual examples of business cases and projects

What You Get

  • Live instructor-led sessions
  • Recorded classes for revision
  • Practical assignments
  • Interview and resume guidance
  • Hands-on projects using real-life data sets
  • Real-world relevant case studies
  • Doubts clarification sessions
  • Certification on successful course completion

Course Design & Approved By

Nasscom & Wipro

What Will You Learn in Data Science Classes?

Our Data Science Online Classes will provide easy-to-understand lessons that can be learned easily through our interactive approach. Each lesson will contain examples, practice questions, and projects to ensure comprehension.

Modules Covered in Core Course

  • Introduction to Data Science
  • Python Programming for Data Scientists
  • Statistics for Data Analysis
  • Analysis of Structured and Unstructured
  • Data Gathering Methods
  • Data Cleaning and Preparation
  • Exploratory Data Analysis (EDA)

Advanced Topics & Projects

  • Basic Concepts of Machine Learning
  • Supervised and Unsupervised Learning
  • Introduction to Deep Learning
  • Natural Language Processing (NLP)
  • Generative Artificial Intelligence Basic
  • Predictive Analytics
  • Modeling Data

Download Curriculum

Get a peek through the entire curriculum designed that ensures Placement Guidance

Course Design By

nasco wp

Course Offered By

Why Choose Our Data Science Training Material & Resources?

  • Videos with clear explanation
  • Study notes that are easy to follow
  • Examples and practical demonstration
  • Exercises and assignments
  • Learning by project
  • Interview preparation materials

Benefits of Joining Our Data Science Course with Placement

  • Expert faculty members
  • Practice-oriented study process
  • Interactive classes
  • Video lectures for revision
  • Access to study material for lifetim
  • Doubt-clearing sessions
  • Industry-oriented course content
Learners Reviews

“The interview help after the course was very useful.”

— Anjali Desai, Fresher

“Recorded classes helped me revise whenever I had time.”

— Vivek Singh, Working Professional

“This Data Science course gave me the confidence and practical skills to excel in interviews.”

— Poonam Mehta, Job Seeker

“Practice work in the course helped me understand how data science is done.”

— Rakesh Iyer, Data Science Trainee

“The Data Science Online Training was easy to follow and the trainer explained everything clearly.”

— Neha, Beginner in Data Science

“This data science course helped me understand how data is used in real company work.”

— Amit, Data Science Student
Data Science - Country-wise Job Profiles & Salary

Top Job Profiles:

  • Junior Data Science job
  • Business Data Science job
  • Data Science executive role

Average Salary Range:

  • INR 4 LPA - INR 7 LPA (Entry Level)
  • INR 8 LPA - INR 15 LPA (Mid Level)
  • INR 15 LPA - INR 18+ LPA (Senior Level)

Top Job Profiles:

  • Business Data Science job
  • Data Science executive role

Average Salary Range:

  • $70,000 - $90,000 (Entry Level)
  • $95,000 - $125,000 (Mid Level)
  • $135,000 - $145,000+ (Senior Level)

Top Job Profiles:

  • Junior Data Science job
  • Business Data Science job
  • Data Science executive role

Average Salary Range:

  • CAD 70,000 - CAD 110,000 (Entry Level)
  • CAD 115,000 - CAD 125,000 (Mid Level)
  • CAD 125,000 - CAD 135,000+ (Senior Level)

Enroll Today

Start your learning journey with our Data Science Online Training in India. Enroll now and build strong data science skills for your career.

About the Trainer

Our trainer has over 10 years of industry experience and has coached many students, fresher’s, and professionals. The Data Science Classes curriculum focuses hands-on training and industry-relevant projects.

  • More Than 10 Years of Industry Experience
  • Expert in Data Science and Artificial Intelligence Courses
  • Conducted Several Online Training Batches
  • Practical Learning Style through Projects
  • Flexible Class Schedule
  • Resume Guidance
  • Mentorship for Career Development
Frequently Asked Questions

The main reason to choose the Online Data Science Course in India from the Croma Campus is so that you can grow your skills under the guidance of the corporate trainers that help you too gain the essential skills and knowledge to meet the demands of the organization with perfect solutions.

The Data Science Online Training in India from Croma Campus will help you to learn from the practical and theoretical formats and will also help you to learn from the real time-based projects that will upgrade your profile needed by the fortune organizations.

The Online Data Science Course in India with Certification can be done with Croma campus offering various ways to learn. You can choose any service from:

  • Instructor Training
  • Online Training
  • Corporate Training
  • Self-paced Training
  • 1 on 1 Training

It takes around 5 to 6 months to learn the course from the Online Data Science Course in India. Also, it depends upon the learner. On an average this time is perfect to learn the course.

To start learning Data Science Online Certification Course in India you can contact to: Email: Info@cromacampus.com Contact no.: +91-9711526942 / +91-120-4155255

The duration of a Data Science course typically ranges from a few weeks for introductory programs to 2-3 years for in-depth degree courses.

Data Science course fees in India vary, starting from around INR 10,000 for basic courses to over INR 2,00,000 for comprehensive and specialized programs.

Topics in a Data Science online course usually include statistics, machine learning, Python programming, data visualization, and big data analysis.

A Data Scientist is a professional skilled in extracting insights and knowledge from data, using techniques in statistics, machine learning, and data analysis.

A Data Science Management course focuses on combining data science skills with management principles, targeting professionals who oversee data-driven projects and teams.

Yes, a Data Scientist job is often categorized under IT, as it involves working with technology and data systems, though it also encompasses statistical and analytical skills.

Eligibility for a Data Science online course varies, but generally, anyone with a keen interest in data and basic analytical skills can enroll. Some advanced courses might require prior knowledge in statistics or programming.

You will learn how to work with data, clean it, analyze patterns, and generate meaningful insights useful for organizations.

Yes, the Data Science Online Course in India includes hands-on work with real datasets.

The Data Science Online Training includes assignments and projects based on real business data.

The Data Science Course helps you explain your project work confidently and answer interview questions clearly.

The Data Science Training follows the same steps used in real workplace environments.

CURRICULUM & PROJECTS

Data Science Training Program

    Introduction To Python

    • Installation and Working with Python
    • Understanding Python variables
    • Python basic Operators
    • Understanding the Python blocks.

    Python Keyword and Identifiers

    • 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.

    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 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

    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 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.
    • 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

    Decorator, Generator and Iterator

    • Creation and working of decorator
    • Idea and practical example of generator, use of generator
    • Concept and working of Iterator

    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

    Memory management using python

    • Threading, Multi-threading
    • Memory management concept of python
    • working of Multi tasking system
    • Different os function with thread

    Python Database Interaction

    • SQL Database connection using
    • Creating and searching tables
    • Reading and Storing config information on database
    • Programming using database connections

    Reading an excel

    • Working With Excel
    • Reading an excel file using Python
    • Writing to an excel sheet using Python
    • Python| Reading an excel file
    • Python | Writing an excel file
    • Adjusting Rows and Column using Python
    • ArithmeticOperation in Excel file.
    • 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 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

    AI and LLM Integration in Python:

    • PandasAI: Natural language queries on DataFrames
    • OpenAI API (GPT) to generate code, EDA, and reports
    • LangChain for building chat-based data apps
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    Data Analysis and Visualization using Pandas.

    • Statistics
    • Pandas
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    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

    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, Feature Transformation and Dimensionality Reduction
    • Datasets
    • Dimensionality Reduction (PCA, ICA,LDA)
    • Anomaly Detection
    • Parameter Estimation
    • Data and Knowledge
    • Selected Applications in Data Mining

    Introduction to Predictive Modelling

    • Difference between Analysis and Analytics
    • Concept of model in analytics and how it is used
    • Common terminology used in Analytics & Modelling process
    • Popular Modelling algorithms, Data Analytics Life cycle
    • Types of Business problems - Mapping of Techniques
    • Introduction to Machine Learning
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    SQL Server Fundamentals

    • SQL Server 2019 Installation
    • Service Accounts & Use, Authentication Modes & Usage, Instance Configurations
    • SQL Server Features & Purpose
    • Using Management Studio (SSMS)
    • Configuration 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 modifications

    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
    • SCHEMA BINDING and ENCRYPTION
    • 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 Benefits

    • 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

    System functions and Usage

    • 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

    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

    AI Integration in SQL:

    • AI Tools:
    • Copilot in Azure Data Studio or GitHub for SQL suggestions
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    Understanding Concepts of Excel

    • Creation of Excel Sheet Data
    • Range Name, Format Painter
    • Conditional Formatting, Wrap Text, Merge & Centre
    • Sort, Filter, Advance Filter
    • Different type of Chart Creations
    • Auditing, (Trace Precedents, Trace Dependents)Print Area
    • Data Validations, Consolidate, Subtotal
    • What if Analysis (Data Table, Goal Seek, Scenario)
    • Solver, Freeze Panes
    • Various Simple Functions in Excel(Sum, Average, Max, Min)
    • Real Life Assignment work

    Ms Excel Advance

    • Advance Data Sorting
    • Multi-level sorting
    • Restoring data to original order after performing sorting
    • Sort by icons
    • Sort by colours
    • Lookup Functions
    • Subtotal, Multi-Level Subtotal
    • Grouping Features
    • Consolidation With Several Worksheets
    • Filter
    • Printing of Raw & Column Heading on Each Page
    • Workbook Protection and Worksheet Protection
    • Specified Range Protection in Worksheet
    • Excel Data Analysis
    • Data Table
    • Pivot Table
    • Generating MIS Report In Excel
    • Text Functions
    • Lookup & Reference Function
    • Logical Functions & Date and Time Functions
    • Database Functions
    • Statistical Functions
    • Financial Functions
    • Functions for Calculation Depreciation
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    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
    • 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

    AI Integration in Power BI:

    • Smart Narrative visual (AI-generated insights)
    • Decomposition Tree (Root Cause Analysis)
    • Q&A Visual (Natural Language Querying)
    • Azure Cognitive Services integration
    • Power BI Copilot (Preview): Report creation via prompts
    • Integration with Power Automate for alerts and workflows
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    Introduction to Data Preparation using Tableau Prep

    • Data Visualization
    • Business Intelligence tools
    • Introduction to Tableau
    • Tableau Architecture
    • Tableau Server Architecture
    • VizQL Fundamentals
    • Introduction to Tableau Prep
    • Tableau Prep Builder User Interface
    • Data Preparation techniques using Tableau Prep Builder tool

    Data Connection with Tableau Desktop

    • Features of Tableau Desktop
    • Connect to data from File and Database
    • Types of Connections
    • Joins and Unions
    • Data Blending
    • Tableau Desktop User Interface

    Basic Visual Analytics

    • Visual Analytics
    • Basic Charts Bar Chart, Line Chart, and Pie Chart
    • Hierarchies
    • Data Granularity
    • Highlighting
    • Sorting
    • Filtering
    • Grouping
    • Sets

    Calculations in Tableau

    • Types of Calculations
    • Built-in Functions (Number, String, Date, Logical and Aggregate)
    • Operators and Syntax Conventions
    • Table Calculations
    • Level of Detail (LOD) Calculations
    • Using R within Tableau for Calculations

    Advanced Visual Analytics

    • Parameters
    • Tool tips
    • Trend lines
    • Reference lines
    • Forecasting
    • Clustering

    Level of Detail (LOD) Expressions in Tableau

    • Count Customer by Order
    • Profit per Business Day
    • Comparative Sales
    • Profit Vs Target
    • Finding the second order date
    • Cohort Analysis

    Geographic Visualizations in Tableau

    • Introduction to Geographic Visualizations
    • Manually assigning Geographical Locations
    • Types of Maps
    • Spatial Files
    • Custom Geocoding
    • Polygon Maps
    • Web Map Services
    • Background Images

    Advanced charts in Tableau

    • Box and Whiskers Plot
    • Bullet Chart
    • Bar in Bar Chart
    • Gantt Chart
    • Waterfall Chart
    • Pareto Chart
    • Control Chart
    • Funnel Chart
    • Bump Chart
    • Step and Jump Lines
    • Word Cloud
    • Donut Chart

    Dashboards and Stories

    • Introduction to Dashboards
    • The Dashboard Interface
    • Dashboard Objects
    • Building a Dashboard
    • Dashboard Layouts and Formatting
    • Interactive Dashboards with actions
    • Designing Dashboards for devices
    • Story Points

    Get Industry Ready

    • Tableau Tips and Tricks
    • Choosing the right type of Chart
    • Format Style
    • Data Visualization best practices

    Exploring Tableau Online

    • Publishing Workbooks to Tableau Online
    • Interacting with Content on Tableau Online
    • Data Management through Tableau Catalog
    • AI-Powered features in Tableau Online (Ask Data and Explain Data)
    • Understand Scheduling
    • Managing Permissions on Tableau Online
    • Data Security with Filters in Tableau Online

    AI Integration in Tableau:

    • Ask Data: Natural Language Data Exploration
    • Explain Data: Automatic statistical insights
    • Tableau GPT (Einstein Copilot - Salesforce)
    • AI forecasting in visualizations
    • Integration with Python (TabPy) and R
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    Introduction to Machine Learning

    • What is Machine Learning
    • Machine Learning Use-Cases
    • Machine Learning Process Flow
    • Machine Learning Categories

    Time Series Analysis

    • What is Time Series Analysis
    • Importance of TSA
    • Components of TSA
    • White Noise
    • AR model
    • MA model
    • ARMA model
    • ARIMA model
    • Stationarity
    • ACF & PACF

    Statistical Foundations (Self-Paced)

    • What is Exploratory Data Analysis
    • EDA Techniques
    • EDA Classification
    • Univariate Non-graphical EDA
    • Univariate Graphical EDA
    • Multivariate Non-graphical EDA
    • Multivariate Graphical EDA
    • Heat Maps

    Introduction to Text Mining and NLP

    • Overview of Text Mining
    • Need of Text Mining
    • Natural Language Processing (NLP) in Text Mining
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    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

    Neural Networks & Deep Learning

    • Understand The Inner Workings Of Neural Networks And Train Them With Keras.
    • Introduction to Neural Networks & Deep Learning
    • Activation Functions (ReLU, Sigmoid, Tanh)
    • Feedforward Neural Network
    • Fully Connected Layer Forward,Backward Pass
    • Regularization Dropout, Batch Normalization
    • Data Preprocessing & Data Augmentation
    • Babysitting Learning: Overfit detection, TensorBoard Monitoring

    Computer Vision

    • Master CNNs, Object Detection, Segmentation, And Deployment.
    • Basics of Images, Image Preprocessing
    • Convolution: 2D Conv, Forward & Backward
    • Pooling, Padding, Stride, Transposed Conv
    • CNN Architectures: LeNet, AlexNet, VGG, ResNet
    • GPU vs CPU for DL
    • Transfer Learning: Inception, MobileNet, fine-tuning
    • Semantic Segmentation using UNet
    • Object Detection YOLO, SSD, Region Proposal
    • Bounding Box Regressor
    • Siamese Networks for Similarity Search
    • Image Classification with CNN
    • Object Detection with YOLOv8
    • Visual Search with Embeddings

    Natural Language Processing (NLP)

    • Train Text Models From Scratch And With BERT.
    • Introduction to NLP and Use Cases
    • Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
    • Feature Extraction: BOW, TF-IDF, N-Grams
    • Word Embeddings: Word2Vec, GloVe, Dense Vectors
    • POS Tagging, Named Entity Recognition
    • RNN, LSTM Forward Pass and BPTT
    • Advanced LSTM Applications + Architectures
    • Attention Mechanism + Encoder-Decoder
    • Transformers, BERT, Hugging Face Pipelines
    • NLP Evaluation Metrics: BLEU, ROUGE
    • Sentiment Classifier (LSTM or BERT)
    • Deploy NLP Model with Streamlit
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    Capstone Project

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+ More Lessons

Course Design By

naswipro

Nasscom & Wipro

Course Offered By

croma-orange

Croma Campus

Our Students' Projects
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Samsung – Dashboard Update & Reporting Project

Scenario: Keeping dashboards updated

Live Work:
  • Updated data regularly
  • Checked values for accuracy
  • Fixed small issues
  • Ensured dashboards stayed current

Outcome: Dashboards consistently displayed accurate.

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Ericsson – Sales Reporting & Analysis Project

Scenario: Making simple sales reports

Live Work:
  • Collected sales data
  • Made simple reports
  • Checked totals
  • Explained the results

Outcome: Reports were easy for managers to understand.

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Wipro – Risk Data Analytics Project

Scenario: Finding risky cases from data

Live Work:
  • Checked old records
  • Found risky signs
  • Updated the results
  • Verified the numbers

Outcome: Risk cases were easy to spot.

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Deloitte – Data Cleaning & Validation Project

Scenario: Cleaning messy data

Live Work:
  • Removed duplicate records
  • Fixed missing values
  • Arranged the data properly
  • Made it ready to use

Outcome: The data was clean and usable.

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Capgemini – Product Data Management Project

Scenario: Understanding product details.

Live Work:
  • Looked at product records
  • Compared product numbers
  • Wrote simple points
  • Shared results

Outcome: Product information was easy to read.

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Accenture – Data Validation & Checking Project

Scenario: Finding mistakes in data

Live Work:
  • Checked data carefully
  • Removed wrong entries
  • Filled missing details
  • Checked the data again

Outcome: The data became correct and usable.

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TCS – Customer Data Analytics Project

Scenario: Understanding customers using data

Live Work:
  • Looked at customer information
  • Grouped similar customers
  • Compared customer data
  • Fixed small mistakes

Outcome: Customer details became clearer.

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Infosys – Sales Data Management Project

Scenario: Looking at old sales data to understand sales

Live Work:
  • Collected past sales details
  • Fixed and cleaned the data
  • Checked the numbers
  • Made easy notes

Outcome: Sales data became easy to understand.

Our Recent Job Requirements
Statistical Analyst

Company: Capgemini

Location: Hyderabad

Experience: 1–3 Years

Required Skills: Proficiency in hypothesis testing, regression

Predictive Analytics Expert

Company: Infosys

Location: Pune

Experience: 1–3 Years

Required Skills: Basic Power BI or Tableau, Making simple reports

Data Scientist

Company: TCS

Location: Bangalore

Experience: 0–2 Years

Required Skills: Working with data, Cleaning and fixing data, Using Excel

Our Data Science Courses

Explore our Data Science courses: Python, Power BI & Machine Learning programs.

Machine Learning Online Course

Learn Machine Learning algorithms, Python libraries & real-time projects to build intelligent, data-driven applications.

Power BI Course

Build interactive dashboards and reports using Power BI with real datasets, DAX concepts, and practical.

Python Online Course

Master Python programming with hands-on coding, real-world projects, and industry-focused online training.

Business Analytics Online Course

Learn data-driven decision making using real projects, analytics tools, and expert-led Business Analytics.

Who Can Join Data Science Online Course?
  • Why : This is good for anyone who is just starting and has no experience in data science.
  • Best Topics: Basics of data science, Working with data, Simple results and reports.
  • Job Benefit: Can apply for beginner data science jobs.
  • Why : Helpful for people who want to change their job and move into data science.
  • Best Topics: Handling data, Understanding daily data science work, Practice with real projects.
  • Job Benefit: Can move into a data science role.
  • Why : Good for people from non-technical backgrounds who are willing to learn data work.
  • Best Topics: Basic data work, Simple steps used in data science, Easy practice tasks.
  • Job Benefit: Entry-level jobs in data science teams.
  • Why : Helps you understand how data science is used in real software and systems.
  • Best Topics: Full data process, Working with real data, Project-based learning.
  • Job Benefit: Can work better on data science projects.
  • Why : Helps you understand data so you can make better business decisions.
  • Best Topics: Simple reports, Understanding results, Reading trends.
  • Job Benefit: Can make better decisions and guide teams clearly
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