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Data Science Course in Delhi

Best Data Science Course in Delhi

Our Data Science Course in Delhi trains students, freshers and professionals step by step. The course covers Python, SQL, Statistics, Data Analysis, Machine Learning, Artificial Intelligence, Deep Learning, Data Visualization, and other important Data Science topics.

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

Check Data Science Demo Class

Try real-time Data Science training in our Demo Class—see first, then decide to enroll.

Our Recently Placed Students

Neha Joshi

Placed at Wipro

Rahul Bansal

Placed at HCL

Priya Malhotra

Placed at IBM

Aditya Nair

Placed at Infosys

Sneha Kapoor

Placed at Deloitte

Kunal Sharma

Placed at Cognizant

Riya Verma

Placed at Accenture

Aarav Mehta

Placed at TCS

About the Data Science Course in Delhi

The Data Science Training Program at Delhi aims at helping students learn the fundamentals and practical aspects of Data Science. Students will be introduced to various technologies, including Python, SQL, statistics, data cleansing, data analysis, data visualization, Machine Learning, and basic Artificial Intelligence. In addition, practical training sessions will be conducted where one will learn how all the skills mentioned above can be applied in the field.

Students will get practical experience with programming in Python, SQL statements, Pandas, data visualizations, Machine Learning models, and various kinds of datasets. Trainers will give detailed information on each skill and will tell where and how it can be used. If one faces any difficulty in studying, then doubt clearing sessions will be provided to assist students in coding, concepts, assignments, and projects.

Interview preparation, resume building, project guidance, and placements will be an integral part of the training program. The objective of the training is to make the candidates practically skilled in Data Science, understand practical projects in Data Science, and gain confidence in applying for Data Science jobs. If you need extra coaching classes at any time or from anywhere, our trainer can provide online training and coaching sessions to help you continue your learning.

Course Highlights
  • Work with live datasets and task-based learning.
  • Live Instructor-led Data Science Training in Delhi.
  • Learn how to clean and preprocess the data and create models.
  • Work on Data Science projects using trainers' guidance.
  • Join online coaching whenever you require clearing your doubts from any place.
  • Practical training Python, SQL, Machine Learning Models, & projects-related queries.
  • Learn through live classes and recordings for revision purposes.

What You Get

  • Live Instructor-Led Data Science Training
  • Hands-on Practice with Real-World Data Science Projects
  • Practical Training on Python for Data Science
  • In-Depth Learning of Statistics and Data Analysis
  • Hands-on Experience with NumPy, Pandas and Matplotlib
  • Practical Training in Machine Learning Algorithms
  • Real-Time Projects for Data Science Skill Development
  • Study Material, Notes and Practice Assignments

Course Design & Approved By

Nasscom & Wipro

What Will You Learn in a Data Science Course?

A Data Science Course provides training in all aspects of handling data. Further on, you will be taught data cleaning, SQL, data analysis, visualization, Machine Learning, AI, and projects.

Core Data Science Topics

  • Python Programming for Data Science
  • Variables, Data Types, Loops, Functions
  • NumPy for numerical data
  • Pandas for data handling
  • SQL and database queries
  • Statistics and Probability
  • Data Cleaning and Data Preprocessing

Machine Learning & Advanced

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors

Download Curriculum

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

Course Design By

nasco wp

Course Offered By

Why Should I Attend Data Science Training?

  • Learn how to deal with data
  • Get some understanding of Python
  • Do practicals on actual data sets
  • Learn how to clean and organize data
  • Learn how to extract useful insights
  • Learn by coding and reporting data

Some benefits of joining the training include:

  • Learn Python from basic to advanced
  • Practice SQL queries on data
  • Understand Statistics & Probability
  • Work with real datasets
  • Create charts and dashboards
  • Work on a final project
  • Get placement assistance
Learners Reviews

“The mock interview sessions showed me exactly what companies ask about Python, SQL, statistics, and Machine Learning.”

— Rohan, Financial Analyst

“The interview practice helped me understand what companies can ask about Python, SQL, statistics, and Machine Learning.”

— Vikas, Software Professional

“I was new to Data Science. The trainer started from basic topics and slowly moved to advanced topics like AI and Machine Learning.”

— Neha, Graduate

“The project sessions were useful for me. I learned how to clean data and create a Machine Learning model instead of only reading theory.”

— Aman, Working Professional

“I had basic coding knowledge before joining. The Data Science Classes helped me understand Pandas, SQL, and Machine Learning better.”

— Priya, Student

“The trainer explained Python in very simple steps. I liked the practical work because I could understand how data is used in real projects.”

— Rohit, Data Analyst
Country-wise Job Roles and Salary Guide

Top Job Profiles:

  • Data Analyst
  • Junior Data Scientist
  • Machine Learning Engineer
  • Business Analyst
  • Data Engineer
  • AI Analyst

Salary Range

  • Salary depends on the job role, company, city, technical skills, experience, and interview performance. Freshers usually start with entry-level roles, while people with project experience and strong technical skills can apply for higher-level positions.

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • AI Engineer
  • Business Intelligence Analyst

Salary Range

  • Salary depends on location, company, experience, education, and technical skills. Jobs in major technology and business areas may have different salary levels.

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • Data Engineer
  • AI Specialist

Salary Range

  • Salary can change based on experience, location, company, and job role. Candidates with Python, SQL, Machine Learning, and AI skills can apply for different data-related jobs.

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • Data Engineer
  • AI Developer

Salary Range

  • The salary depends on the company, city, experience, and skills required for the role.

Enroll Now

Join our Data Science Online Training in Delhi if you want to learn Data Science from basic to advanced topics. The training covers Python, SQL, Statistics, Machine Learning, AI.

About the Trainer

Our Data Science trainer has hands-on expertise in Python, SQL, Statistics, Data Analytics, Machine Learning and AI. Simple examples and practical tasks help learners understand how ideas are used in real work.

  • Practical Data Science experience
  • Python and SQL training
  • Statistics and data analysis knowledge
  • Machine Learning project guidance
  • AI and Deep Learning basics
  • Real dataset practice
  • Doubt-clearing sessions
Frequently Asked Questions

Students, graduates, freshers, working professionals, and people changing careers can join the course.

Basic coding knowledge can help, but beginners can also start. Python is taught from the basic level.

Python is the main programming language used for Data Science training.

Yes. SQL is covered for working with databases and getting useful data from tables.

Yes. Statistics and Probability are important parts of the training.

Yes. You will learn important supervised and unsupervised Machine Learning methods.

Yes. The course includes Artificial Intelligence basics and its connection with Data Science.

Yes. Deep Learning basics, neural networks, and their use in AI are covered.

Yes. Practical work includes datasets and project-based tasks so you can practice the concepts.

Yes. Advanced Excel can be used for data analysis and basic reporting tasks.

Yes. You will learn how to create charts and understand data using tools such as Matplotlib and Seaborn.

Yes. Freshers can start with basic Python, SQL, statistics, and data analysis before moving to advanced topics.

Yes. Practical assignments and a final project are included to help you understand real project work.

Placement assistance is provided through resume support, interview preparation, job guidance, and related career support.

Yes. Course certification is provided after successful completion of the required training.

Yes. Working professionals can join the training to learn Data Science skills or move toward data-related roles.

EDA means Exploratory Data Analysis. It is used to understand data, find patterns, check missing values, and identify unusual values before model building.

Machine Learning is a part of AI where models learn patterns from data and use those patterns to make predictions or decisions.

Data Analytics mainly focuses on understanding and reporting data. Data Science also includes programming, Machine Learning, prediction, and AI-related work.

Yes. Data Science Online Training allows learners to attend classes from home and practice using datasets, assignments, recordings, and projects.

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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Spotify - Music Recommendation Project

Scenario: Students worked with music data to understand listener choices and create simple song recommendations.

Live Work
  • Cleaned song and listening data
  • Studied listener preferences
  • Compared song ratings
  • Found similar songs
  • Used Python for analysis

Outcome: Students learned to build song recommenders.

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Ericsson - Loan Default Prediction Project

Scenario: Students worked with customer payment data to find risky accounts that may be linked to loan default.

Live Work
  • Cleaned payment data
  • Checked missing and duplicate records
  • Studied repayment patterns
  • Selected useful features
  • Used Python for analysis

Outcome: Students learned ML for default prediction.

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HCL - AI Support Ticket Analysis Project

Scenario: Students worked with support ticket data and learned the basic idea of Natural Language Processing and Artificial Intelligence.

Live Work
  • Prepared ticket data
  • Cleaned ticket text
  • Studied common words
  • Used basic NLP methods
  • Created issue analysis

Outcome: Students learned AI for ticket data analysis.

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Accenture - Client Segmentation Project

Scenario: The project grouped clients based on their buying behaviour.

Live Work
  • Cleaned client data
  • Selected useful features
  • Applied clustering
  • Studied client groups
  • Created visual charts

Outcome: Students learned clustering for client analysis.

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Infosys - Employee Attrition Prediction

Scenario: Students used employee information to build a model for employee attrition prediction.

Live Work
  • Cleaned the dataset
  • Explored key features
  • Applied classification
  • Built a Machine Learning model
  • Evaluated predictions

Outcome: Students learned classification for predictions.

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TCS - Employee Data Analysis Project

Scenario: Students studied employee data to understand department, salary, experience, and other patterns.

Live Work
  • Cleaned employee records
  • Used SQL queries
  • Created visual reports
  • Compared departments
  • Studied salary patterns

Outcome: The project showed how data guides HR decisions.

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Tech Mahindra - Customer Churn Prediction

Scenario: The project focused on finding customers who may leave a service.

Live Work
  • Cleaned customer data
  • Selected useful features
  • Used Python for analysis
  • Created a classification model
  • Checked model performance

Outcome: Students learned how ML solves prediction tasks.

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Amazon - Sales Data Analysis Project

Scenario: Students worked with sales data containing products, customers, orders, prices, and sales values.

Live Work
  • Cleaned the dataset
  • Removed duplicate records
  • Checked missing values
  • Used Pandas for analysis
  • Created sales charts

Outcome: Students made a simple report on sales patterns.

Our Recent Job Requirements for Data Science
Machine Learning Engineer

Company: Software Company

Location: Gurgaon

Experience: 1–4 years

Required Skills: Python, Machine Learning, model evaluation.

Junior Data Scientist

Company: Technology Company

Location: Chennai

Experience: 0–2 Years

Required Skills: Python, Pandas, NumPy, SQL, & project knowledge.

Data Analyst

Company: Business Services Company

Location: Mumbai

Experience: 0–2 Years

Required Skills: Excel, SQL, Python, Data Visualization, & reporting.

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Who Can Enroll in a Data Science Course?
  • Why : Students can learn job-focused technical skills while completing their studies.
  • Best Topics: Python, SQL, Statistics, Data Analysis, Machine Learning, and AI.
  • Job Benefit:: These skills can help students prepare for entry-level data jobs.
  • Why : Freshers can build technical knowledge and project experience from the beginning.
  • Best Topics: Python, SQL, Excel, Machine Learning, Data Visualization, and projects.
  • Job Benefit: Project work can help freshers explain their skills during interviews.
  • Why : Working professionals can learn new data skills or prepare for a career change.
  • Best Topics:: Advanced Python, SQL, Machine Learning, AI, and project work.
  • Job Benefit:: These skills can support a move toward Data Analyst, Data Scientist, or AI-related roles.
  • Why : IT professionals can add Data Science and AI skills to their current technical knowledge.
  • Best Topics: Python, Machine Learning, Deep Learning, AI, SQL, and deployment basics.
  • Job Benefit: The added skills can help them apply for data and AI-related work.
  • Why : People from non-IT fields can learn the basics and move toward technical data roles.
  • Best Topics: Python basics, SQL, Statistics, Excel, Data Analytics, and Machine Learning basics.
  • Job Benefit: The training gives them a step-by-step path to start learning Data Science. Choose our Data Science Course if you want to learn how raw data is cleaned, studied, visualized, and used for Machine Learning and AI. The course covers the main skills needed to understand Data Science work and gives you practical tasks to improve your knowledge.
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