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Data Science Course in Noida

Best Data Science Course in Noida | Placement Assistance

Start your career with our Data Science Course in Noida with Placement support. This course will help you gain knowledge in Python, Machine Learning, AI, Statistics, SQL, Deep Learning, and Data Visualization with live projects.

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

Check Data Science Demo Class

Experience real-time Data Science training with our Demo Class—learn before you enroll.

Our Recently Placed Students

Amit Sharma

Placed at TCS

Priya Verma

Placed at Infosys

Rahul Singh

Placed at Accenture

Neha Gupta

Placed at Capgemini

Keshav Kumar

Placed at Traviyo

Shweta Mishra

Placed at Wipro

Arjun Mehta

Placed at HCL

Sonia Raj

Placed at Tech Mahindra

Online Data Science Course in Noida Videos

About the Data Science Course in Noida

Our Data Science Training Institute in Noida offers full training from basic to advanced level. You will learn Python, machine learning, AI, statistics, SQL, and data visualization with real project work. This course is suitable for students, freshers, working professionals, and both IT and non-IT learners.

Course Highlights
  • Live Instructor-led Data Science Training in Noida
  • Practical training on Python, SQL, ML, AI, and Analytics
  • Statistics, Probability, EDA & Data Visualization
  • Industry real case studies
  • Final Capstone Project with trainer support

What You Get

  • Live Classes + Recording
  • Real-time Project Scenario
  • Interview & resume support
  • Placement Assistance

Course Design & Approved By

Nasscom & Wipro

What You Will Learn in the Data Science Course

This section explains all the topics covered in the course:

Core Modules Covered

  • Python Programming for Data Science
  • Statistics & Probability
  • Data Cleaning & Preprocessing
  • Exploratory Data Analysis (EDA)
  • SQL for Data Science
  • Data Visualization (Matplotlib, Seabor
  • Pandas & NumPy

Advanced Topics & Projects

  • Machine Learning Algorithms
  • Deep Learning Basics
  • Model Improvement Methods

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?

  • Updated learning content made
  • Real datasets and practical assign
  • Full study material for Python
  • Interview questions & Practice Test
  • Job-focused tasks for analyst
  • Easy lessons for beginners

Benefits of Enrolling in Our Data Science Course in Noida

  • Job-Focused Course Content
  • Practical Learning with Real Project
  • Expert Trainers
  • Modern Lab Facilities
  • 100% Placement Assistance
Learners Reviews

“The projects and assignments were great to learn Python, SQL, and Tableau in-depth. This course was a great boost for my skills.”

— Sneha Gupta, Financial Analyst

“Instructors taught theoretical and practical aspects. Now, I am confident in dealing with data pipelines and machine learning.”

— Aditya Rao, Junior Data Scientist

“The course was well-organized, and the practical examples made learning easy. I would definitely suggest this course to all who are new.”

— Priya Sharma, Marketing Analyst

“I learned a lot about data visualization and predictive modeling. The course helped me grow as an analytics professional.”

— Vikram Singh, Software Engineer

“They explained everything to us clearly. Even the complex concepts seemed simple to learn, and the practice sessions helped immensely.”

— Anjali, Business Intelligence

“It was a very practical course that was easy to follow along with. I can apply the skills learned about Python as well as machine learning.”

— Rahul Mehra, Data Analyst
Data Science - Country-wise Job Profiles & Salary

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer

Average Salary Range:

  • INR 5 LPA - INR 9 LPA (Entry Level)
  • INR 8 LPA - INR 18 LPA (Mid Level)
  • INR 18 LPA - INR 35+ LPA (Senior)

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer:

Average Salary Range:

  • $70,000 - $95,000 (Entry Level)
  • $110,000 - $150,000 (Mid Level)
  • $120,000 - $170,000+ (Senior)

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer

Average Salary Range:

  • CAD 60,000 - CAD 85,000 (Entry Level)
  • CAD 90,000 - CAD 130,000 (Mid Level)
  • CAD 100,000 - CAD 160,000+ (Senior)

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer

Average Salary Range:

  • £35,000 - £50,000 (Entry Level)
  • £55,000 - £80,000 (Mid Level)
  • £80,000 - £120,000+ (Senior)

Enroll Today

Join our Data Science Course in Noida with Placement support and start your career with live projects, expert trainers, and full interview help.

About the Trainer

The best thing about our courses is that you will be trained under experts having years of experience. They are industry trainers with years of practical experience in Data Science.

  • They focus on practical learning and guide students.
  • 10+ years of Data Scientist experience.
  • Provide Core Programming & Tools
  • Free Aptitude and Technical Skills Training
  • They have worked on real company projects.
  • Interview Preparation
Frequently Asked Questions

Yes, absolutely. The learning starts from basics and slowly moves to advanced topics.

That is one of the plus points of our courses. We help with resumes, interviews, and job support.

Yes. You will work on live industry data and projects.

Many tools are there but majorly you will be introduced to Python, SQL, Power BI, Tableau, ML, DL, Pandas, NumPy, TensorFlow, AWS & Azure.

Yes. Live online classes and recorded videos are provided.

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
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    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:
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    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
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Cisco – AI-Driven Healthcare Analytics

Scenario: Cisco needed a unified analytics platform to process patient, device, and clinical data across 30+ countries.

Live Work:
  • Building predictive models
  • Creating ETL pipelines using Python
  • Developing dashboards in Power BI
  • Real-time IoT device data processing

Outcome: Improved diagnosis accuracy by 40%.

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Uber – Real-Time Demand Prediction

Scenario: Uber wanted to improve surge-pricing accuracy using machine-learning models.

Live Work:
  • Building demand forecasting models using Python
  • Analyzing 3+ billion ride-history data points
  • Geo-spatial clustering for hotspot identification
  • Deploying ML pipelines on AWS (SageMaker)

Outcome: 28% improved price accuracy & faster ride.

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Samsung – Customer Analytics & Network

Scenario: Samsung needed AI-based demand forecasting to reduce inventory gaps across APAC & EU regions.

Live Work:
  • Building predictive demand models using Python.
  • Integrating ML outputs with SAP IBP
  • Real-time sales & shipment dashboarding (PowerBI)
  • Automating data pipelines using Airflow

Outcome: Forecast accuracy improved to 93%.

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Zomato – Customer Sentiment Analysis Using NLP

Scenario: Zomato wanted to analyze user reviews to improve food partner performance.

Live Work:
  • Scraping 10M+ reviews using Python & BeautifulSoup
  • Text cleaning, tokenization, stemming.
  • Building sentiment classifier using BERT
  • Insights dashboards for restaurant performance

Outcome: Restaurant complaint resolution time reduced.

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Flipkart – Real-Time Demand Forecasting System

Scenario: Flipkart needed accurate inventory forecasting for festive sale events.

Live Work:
  • Building ARIMA & Prophet forecasting models
  • Data scraping & preprocessing
  • Real-time API deployment using FastAPI
  • Stock-out & overstock risk predictions

Outcome: Saved 52 crore by optimizing inventory allocation

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Netflix – Predictive Analytics for User Engagement

Scenario: Netflix wanted to reduce churn by predicting dropping engagement patterns.

Live Work:
  • Time-series analysis on weekly watch duration
  • Clustering user personas using K-Means
  • Building churn prediction models
  • Visualization dashboards in Tableau & Power BI

Outcome: Churn reduced by 12% in Q3.

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IBM – Enterprise AI & Predictive Analytics

Scenario: IBM enhanced enterprise analytics for global clients using data science frameworks.

Live Work:
  • Implementing predictive analytics models
  • Real-time dashboards using Power BI & IBM Cognos
  • Data cleaning & feature engineering
  • Automating model retraining pipelines

Outcome: Improved business forecasting accuracy by 40%.

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Google – Advanced Data Science & Machine Learning

Scenario: Google modernized its ML pipelines to improve data processing & prediction accuracy.

Live Work:
  • Building scalable ETL workflows using Python
  • Training ML models for search optimization
  • Deploying models on Google Cloud AI Platform
  • Automated A/B testing & performance monitoring

Outcome: 28% faster predictions & improved search

Recent Data Science Job Requirements
Machine Learning Engineer

Company: TCS

Location: Hyderabad

Experience: 0–2 Years

Required Skills: ML Algorithms, Python, NumPy/Pandas.

Junior Data Analyst

Company: Deloitte

Location: Gurgaon

Experience: 0–1 Year

Required Skills: Excel, Python/R, Data Cleaning, Dashboard Creation.

Data Scientist (Entry Level)

Company: Infosys

Location: Pune

Experience: 0–2 Years

Required Skills: Python, SQL, Machine Learning, Data Visualization.

Data Science-Job Profiles & Salary Guide
  • Backgrounds : B.Tech, BCA, B.Sc (Math/Stats), MCA, B.Com, BA (Economics).
  • Why: Data Science is open to anyone with logical thinking & basic math skills
  • Career Advantage: Entry-level analysts earn well and grow quickly with skills.
  • Why : Strong coding mindset gives an edge in ML & AI development.
  • Best Fit Tracks: Machine Learning, Deep Learning, MLOps, Data Engineering
  • Career Advantage: Developers, QA engineers, and DevOps can switch to ML engineering roles.
  • Why : Data Science heavily uses statistics & probability.
  • Best Fit Tracks: Statistical Modeling, Predictive Analytics, Machine Learning Algorithms
  • Career Advantage: Ideal for ML research, quant analysis, and algorithm development roles.
  • Why : Data Science applications in finance are huge (fraud, forecasting, risk).
  • Best Fit Tracks: Financial Analytics, Data Visualization, Forecasting Models
  • Career Advantage: Domain knowledge + data skills = high-value data analyst roles.
  • Includes : BPO, HR, Teaching, Operations, Support, Logistics professionals.
  • Why: Data Science welcomes anyone willing to learn tools & analytics.
  • Career Advantage: Smooth transition into analyst or reporting profiles.
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