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Data Science Online Course

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

The Data Science Online Course is practical, structured and easy to follow. Each topic is explained clearly so that even beginners can learn comfortably. If you are new to data or looking to upgrade your skills this Data Science Course help you build strong foundations.

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

The Data Science Online Classes explain how data is collected, processed, analyzed, and used for decision-making. This Data Science Training focuses on practical learning and real business scenarios followed by companies.

Training Highlights
  • Live online sessions by experienced trainers
  • Practical exercises with real datasets
  • Real business case studies
  • Job-oriented learning approach
  • Resume and interview preparation
  • Placement support

What You Get

  • Live instructor-led sessions
  • Recorded classes for revision
  • Practical assignments
  • Interview and resume guidance

Course Design & Approved By

Nasscom & Wipro

What Will You Learn in Data Science Course

This Data Science Online Course is taught in a simple and structured way. Every concept is supported with examples to ensure clarity and real-world understanding.

Core Modules Covered

  • Fundamentals of data science
  • Working with structured and unstructured
  • Data cleaning and preparation
  • Data exploration techniques
  • Extracting insights from data

Advanced Topics & Projects

  • Data modeling concepts
  • Trend and pattern analysis
  • Improving data accuracy
  • Real-time data science projects
  • Business-focused case studies

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?

  • Easy-to-follow training videos
  • Simple study material
  • Real-world examples
  • Practice assignments
  • Interview questions

Benefits of Joining Our Data Science Course with Placement

  • Learn from experienced trainers
  • Work on real data science projects
  • Lifetime access to learning material
  • Recorded sessions for revision
  • Live doubt-clearing sessions
  • Certification guidance
  • Placement assistance
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

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

Get trained by an instructor with over 10 years of real industry experience in Data Science Classes and Data Science Online Training. The trainer shares practical insights to guide your learning journey.

  • 10+ years of industry experience
  • Strong expertise in Data Science Course
  • Conducted multiple online batches
  • Project-based teaching approach
  • Interview and career guidance
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

    NA

    • Introduction To Python
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
      • Version Control with Git & GitHub
    • Python Keyword and Identiers
      • Python Comments, Multiline Comments.
      • Python Indentation
      • Understating the concepts of Operators
    • Introduction To Variables
      • Variables, expression condition and function
      • Global and Local Variables in Python
      • Packing and Unpacking Arguments
      • Type Casting in Python
      • Byte objects vs. string in Python
      • Variable Scope
    • Python Data Type
      • Declaring and using Numeric data types
      • Using string data type and string operations
      • Understanding Non-numeric data types
      • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets
    • Control Structure & Flow
      • Statements if, else, elif
      • How to use nested IF and Else in Python
      • Loops
      • Loops and Control Statements.
      • Jumping Statements Break, Continue, pass
      • Looping techniques in Python
      • How to use Range function in Loop
      • Programs for printing Patterns in Python
      • How to use if and else with Loop
      • Use of Switch Function in Loop
      • Elegant way of Python Iteration
      • Generator in Python
      • How to use nested Loop in Python
      • Use If and Else in for and While Loop
      • Examples of Looping with Break and Continue Statement
      • How to use IN or NOT IN keyword in Python Loop.
    • Python Function, Modules and Packages
      • Python Syntax
      • Function Call
      • Return Statement
      • Arguments in a function Required, Default, Positional, Variable-length
      • Write an Empty Function in Python pass statement.
      • Lamda/ Anonymous Function
      • *args and **kwargs
      • Help function in Python
      • Scope and Life Time of Variable in Python Function
      • Nested Loop in Python Function
      • Recursive Function and Its Advantage and Disadvantage
      • Organizing python codes using functions
      • Organizing python projects into modules
      • Importing own module as well as external modules
      • Understanding Packages
      • Random functions in python
      • Programming using functions, modules & external packages
      • Map, Filter and Reduce function with Lambda Function
      • More example of Python Function
    • List
      • What is List.
      • List Creation
      • List Length
      • List Append
      • List Insert
      • List Remove
      • List Append & Extend using + and Keyword
      • List Delete
      • List related Keyword in Python
      • List Revers
      • List Sorting
      • List having Multiple Reference
      • String Split to create a List
      • List Indexing
      • List Slicing
      • List count and Looping
      • List Comprehension and Nested Comprehension
    • Tuple
      • What is Tuple
      • Tuple Creation
      • Accessing Elements in Tuple
      • Changing a Tuple
      • Tuple Deletion
      • Tuple Count
      • Tuple Index
      • Tuple Membership
      • TupleBuilt in Function (Length, Sort)
    • Dictionary
      • Dict Creation
      • Dict Access (Accessing Dict Values)
      • Dict Get Method
      • Dict Add or Modify Elements
      • Dict Copy
      • Dict From Keys.
      • Dict Items
      • Dict Keys (Updating, Removing and Iterating)
      • Dict Values
      • Dict Comprehension
      • Default Dictionaries
      • Ordered Dictionaries
      • Looping Dictionaries
      • Dict useful methods (Pop, Pop Item, Str , Update etc.)
    • Sets
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
      • Frozen Sets
    • Strings
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
    • Python Exception Handling
      • Python Errors and Built-in-Exceptions
      • Exception handing Try, Except and Finally
      • Catching Exceptions in Python
      • Catching Specic Exception in Python
      • Raising Exception
      • Try and Finally
    • Python File Handling
      • Opening a File
      • Python File Modes
      • Closing File
      • Writing to a File
      • Reading from a File
      • Renaming and Deleting Files in Python
      • Python Directory and File Management
      • List Directories and Files
      • Making New Directory
      • Changing Directory
    • Python Database Interaction
      • Basic SQL, DDL and DML commands
      • SQL Database connection using
      • Creating and searching tables
      • Reading and Storing cong information on database
      • Programming using database connections
    • Reading an excel
      • Working With Excel
      • Reading an excel le using Python
      • Writing to an excel sheet using Python
      • Python| Reading an excel le
      • Python | Writing an excel le
      • Adjusting Rows and Column using Python
      • ArithmeticOperation in Excel le.
      • Play with Workbook, Sheets and Cells in Excel using Python
      • Creating and Removing Sheets
      • Formatting the Excel File Data
      • More example of Python Function
    • Complete Understanding of OS Module of Python
      • Check Dirs. (exist or not)
      • How to split path and extension
      • How to get user prole detail
      • Get the path of Desktop, Documents, Downloads etc.
      • Handle the File System Organization using OS
      • How to get any les and folders details using OS
Get full course syllabus in your inbox

    NA

    • Data Analysis and Visualization using Pandas.
      • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
      • How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
      • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalues
      • Exploring the Data Plotting, Correlations, and Histograms
      • Getting statistical information about the data Analysis Concepts, Handle the None Values
      • Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
      • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
      • Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
      • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
      • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
      • Exporting the results to Excel Joins
      • Python | Pandas Data Frame Inner Join
      • Under sting the properties of Data Frame Left Join (Left Outer Join)
      • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
      • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
      • Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
      • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
      • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
    • Data Analysis and Visualization using NumPy
      • Introduction to NumPy Numerical Python
      • Importing NumPy and Its Properties
      • NumPy Arrays
      • Creating an Array from a CSV
      • Operations an Array from a CSV
      • Operations with NumPy Arrays
      • Two-Dimensional Array
      • Selecting Elements from 1-D Array
      • Selecting Elements from 2-D Array
      • Logical Operation with Arrays
      • Indexing NumPy elements using conditionals
      • NumPys Mean and Axis
      • NumPys Mode, Median and Sum Function
      • NumPys Sort Function and More
    • Data Analysis and Visualization using MatPlotLib
      • Bar Chart using Python MatPlotLib
      • Column Chart using Python MatPlotLib
      • Pie Chart using Python MatPlotLib
      • Area Chart using Python MatPlotLib
      • Scatter Plot Chart using Python MatPlotLib
      • Play with Charts Properties Using MatPlotLib
      • Export the Chart as Image
      • Understanding plt. subplots () notation
      • Legend Alignment of Chart using MatPlotLib
      • Create Charts as Image
      • Other Useful Properties of Charts.
      • Complete Understanding of Histograms
      • Plotting Different Charts, Labels, and Labels Alignment etc.
    • Introduction to Data Visualization with Seaborn
      • Introduction to Seaborn
      • Making a scatter plot with lists
      • Making a count plot with a list
      • Using Pandas with seaborn
      • Tidy vs Untidy data
      • Making a count plot with a Dataframe
      • Adding a third variable with hue
      • Hue and scattera plots
      • Hue and count plots
      • Visualizing Two Quantitative Variables
      • Visualizing a Categorical and a Quantitative Variable
      • Customizing Seaborn Plots
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    NA

    • Foundation for AI: Learn traditional ML models, evaluation, and workflows.
      • Introduction to ML, AI, and Deep Learning
      • Types of ML (Supervised, Unsupervised, Reinforcement)
      • ML Pipeline: Data Cleaning, Feature Engineering
      • Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
      • Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
      • Overfitting, Underfitting, Cross-Validation
      • Hands-on Project: Titanic Dataset (or similar)
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    NA

    • 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
      • Backpropagation & Gradient Descent
      • Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
      • Softmax, Cross-Entropy Loss
      • Keras Basics: Sequential API & Functional API
      • Fully Connected Layer Forward/Backward Pass
      • Regularization Dropout, Batch Normalization
      • Data Preprocessing & Data Augmentation
      • Weight Initialization Strategies
      • Babysitting Learning: Overfit detection, TensorBoard Monitoring
      • Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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    NA

    • 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
      • Hands-on:
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    NA

    • 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
      • Hands-on:
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    NA

    • Take models from notebooks to real-world applications.
      • Saving & Loading Models (Pickle, Joblib, Keras)
      • Flask vs FastAPI Serving ML models
      • Streamlit/Gradio for Web Apps
      • Hosting Models on Hugging Face Spaces, Streamlit Cloud
      • MLflow Intro Model Tracking & Versioning
      • Hands-on:
Get full course syllabus in your inbox

    NA

    • Build, evaluate, and deploy a mini AI project end-to-end.
      • Project Selection: Tabular, CV, or NLP
      • Data Collection/Exploration
      • Preprocessing + Feature Engineering
      • Model Training & Tuning
      • Evaluation & Interpretation
      • App Creation (Streamlit/Gradio)
      • Deployment + Final Presentation/Submission
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+ More Lessons

Course Design By

naswipro

Nasscom & Wipro

Course Offered By

croma-orange

Croma Campus

Our Students' Projects
1769686580.webp
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.

1769686447.webp
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.

1769686348.webp
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.

1769686275.webp
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.

1769686197.webp
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.

1769686131.webp
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.

1769686058.webp
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

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
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.

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