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Why Data Science?

Data Science is the process of gaining actionable insights from large amounts of raw data. Data is analyzed using a variety of scientific methods, algorithms, and processes. Cleaning, aggregating, and manipulating data for advanced analyses are important aspects of Data Science. It is an interdisciplinary topic that combines programming skills, technical skills, domain knowledge, and mathematical and statistical knowledge, to extract meaningful insights from data.
A data scientist is a professional who applies Data Science to analyze data in order to provide actionable insights by using various processes, methods, systems, and algorithms. As experts in a variety of data niches such as mathematics, statistics, scientific, analytical, and technical skills, data scientists support companies in interpreting and managing data. As organizations rely more heavily on data analytics to provide insight and leverage automation and machine learning as core components of their IT strategies, the role of the data scientist becomes increasingly important. As a whole, they must fulfill several responsibilities, as follows:
  • Identify data sources and gather data.
  • Analyze structured and unstructured data effectively.
  • Formulate strategies to solve business challenges.
  • Create data strategies in collaboration with team members and leaders.
  • Use algorithms and modules to uncover trends and patterns.
  • Cleaning and verifying the data to ensure it is accurate and uniform
  • Delhi NCR has become one of India's fastest-growing and most diverse economic regions, contributing over 8% of India's GDP. Delhi will become more important as India finds its bearings in the IT arena and expands outward from there. It has the distinction of never sleeping and having space for dreams at the same time. Those who choose a Data Science course in Delhi can pursue a career of growth and achievement in the tech industry. In order to gain an in-demand set of skills required for today's job opportunities in Data Science, we offer the best Data Science training course in Delhi. Some of the great career opportunities after completing a Data Science course in Delhi include:
  • Junior/Senior/Associate Data Scientist
  • Forcasting/Risk Analyst
  • Portfolio Risk Data Scientist
  • Forecasting Analyst, Research Analyst, Data Analytics
  • Analytics Lead/Manager/Consultant
  • Machine Learning Engineer
  • Statistical Programming Specialist
  • Work on projects built in partnership with top companies

    Work on real industry projects, get real-time feedback from mentors, and engage with your peers to discuss different solutions during live classes. Here are a few of the sample projects:
    Use data to design the best strategy for offering coupons and discounts.
    Experiment with different driving routes to minimise wait times.
    Predict when would food arrive at customer's addresses.
    Sniff out fraud by analyzing millions of chat messages.
    *Projects may be updated based on student and partner feedback.

    Get 1:1 guidance with our top industry mentors

    Our 300+ mentors have helped thousands of students by defining their career paths, conducting mock interviews, and giving detailed feedback. Get paired with a mentor and schedule a personalized 1:1 mentoring session.

    Our panel of Industry Advisors has helped in creating best-in-class program.

    Ramit Sawhney
    Tower Research Capital / ShareChat
    Pawan Kumar
    Head of Data Science, Uber
    Yash Mimani
    McKinsey & Company
    Bhavik Rasyara
    Boston Consulting Group

    Curriculum is designed to make you a solid Data Scientist

    Beginner
    15 Months
    Circle boundary 0822a30d92ac3930a0c28fe0e9e569145efa2e9e93349b6405a525554aaaca79.svg
    Checked circle 8d182dfbe72948215db9ddf7a22d36b0f5d91ccada0c6e3e86415486bd00e3dd.svg
    Intermediate
    11 Months
    Circle boundary 0822a30d92ac3930a0c28fe0e9e569145efa2e9e93349b6405a525554aaaca79.svg
    Checked circle 8d182dfbe72948215db9ddf7a22d36b0f5d91ccada0c6e3e86415486bd00e3dd.svg
    Advanced
    7 Months
    Circle boundary 0822a30d92ac3930a0c28fe0e9e569145efa2e9e93349b6405a525554aaaca79.svg
    Checked circle 8d182dfbe72948215db9ddf7a22d36b0f5d91ccada0c6e3e86415486bd00e3dd.svg
    Module - 1

    Beginner Module

    4 Months
    Module - 2

    Data Analysis and Visualization

    4 Months
    Module - 3

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 4

    Specializations

    3 Months
    Module - 5

    Machine Learning Ops

    1 Month
    Module - 6

    Advanced Data Structures and Algorithms

    4 Months
    4 Months
    Tableau + Excel
    • Basic Visual Analytics
    • More Charts and Graphs, Operations on Data and Calculations in Tableau
    • Advanced Visual Analytics and Level Of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet and Workbook Formatting
    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google Spreadsheets
    SQL
    • Intro to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering and Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes and Partitioning
    Beginner Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    Module - 1

    Data Analysis and Visualization

    4 Months
    Module - 2

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 3

    Specializations

    3 Months
    Module - 4

    Machine Learning Ops

    1 Month
    Module - 5

    Advanced Data Structures and Algorithms

    4 Months
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    Module - 1

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 2

    Specializations

    3 Months
    Module - 3

    Machine Learning Ops

    1 Month
    Module - 4

    Advanced Data Structures and Algorithms

    4 Months
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    4 Months
    Tableau + Excel
    • Basic Visual Analytics
    • More Charts and Graphs, Operations on Data and Calculations in Tableau
    • Advanced Visual Analytics and Level Of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet and Workbook Formatting
    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google Spreadsheets
    SQL
    • Intro to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering and Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes and Partitioning
    Beginner Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum

    Our teaching army will make you confident about your fundamentals

    Our tutors are experts from top companies who have built scalable systems and understand the real-life importance of Data Science and Machine Learning.
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    Eligibility: Anyone who graduated in 2022 or before

    We also help you prepare for your job search and your interviews

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    Scaler alumni work at reputed tech organizations and promising startups

    Hear from our scaler students and make an informed decision!

    I am superpysched to be a part of Tekion Corp as Senior Software Engineer. The best of interview process I have ever experienced.
    Thanks to the entire team of Scaler to help upgrade my skillsets which certainly made it less daunting. Anshuman Singh Abhimanyu Saxena. Hope to take it to even greater heights!
    The way they taught was the aspect that stood out most to me. Each student was invited to give solutions, and every answer was dissected. No doubt was small enough, and every approach was thought about.
    Thanks to scaler lectures, mentor sessions, and my Mentor Drishti Agarwal, who not only helped me in tackling technical questions but also constantly gave me behavioural and communication tips.
    And the most important part, the mock interviews with my mentor, made me more confident day by day.

    Tuition Fee

    With EMI options, your payment can be as low as Rs 8,628 per month - that's like your monthly grocery bill!
    Total fee: Rs 3.69L inclusive of GST at super affordable EMI options. Try the course for the first 2 weeks - full money-back guarantee if you choose to withdraw.
    EMI Options
    You can find both no-cost EMI & standard interest EMI from our NBFC partners. See below a summary of their best plans (more details available at the time of payment)
    Total Amount
    Upfront Downpayment
    Amount split over EMI
    Duration (Months)
    Monthly Payments
    No Cost Emi
    ₹369,000
    ₹35,000
    ₹334,000
    6
    9
    12
    18
    24
    ₹55,667
    ₹37,111
    ₹27,833
    ₹18,556
    ₹13,917
    Standard Emi
    ₹369,000
    ₹35,000
    ₹334,000
    36
    60
    ₹12,339
    ₹8,628
    Delivered via our EMI partners - Liquiloans, Eduvanz, EarlySalary, Avanse & Credit Fair
    You can also choose to avail EMI options from your credit card providers.

    Data Science Training Course FAQs

    What is eligibility for a Data Science course in Delhi?

    As a subject, Data Science is a fusion of concepts from Mathematics, Computer Science, and Statistics. It is essential that students have at least one degree in one of the following fields: science, technology, engineering, or mathematics. Additionally, having studied computer programming in high school can be beneficial.

    What will I learn in the Data Science online training course?

    Our curriculum for the Data Science Course will introduce you to concepts of Data Science tools, Data Science algorithms, and machine learning principles that will assist you in gaining some meaningful insights from unstructured data. During a Data Science training course, you will learn about various languages and tools, from Python to SQL, Deep learning, Machine Learning, Artificial Intelligence, Statistical Methods, data analysis, data wrangling, and Data Visualization.

    What are the Career Opportunities or type of jobs that I will be suited for after completing this Data Scientist course in Delhi?

    In Data Science, there are a variety of job roles that can be assigned depending on industry needs. Once you graduate from our Data Science course in Delhi, you will possess an in-demand set of skills critical to today's Data Science career opportunities, which include: Junior/Senior/Associate Data Scientist, Product/e-Commerce Data Scientist, Portfolio Risk Data Scientist, Forecasting Analyst, Research Analyst, Data Analytics, Analytics Lead/Manager/Consultant, Machine Learning Engineer, Statistical Programming Specialist, and many more.

    What kind of salary can I expect as a Data Science Professional?

    As a fresher in Data Science, you can expect to earn between Rs. 4-8 lakhs per annum. An experienced professional data scientist (3-5 years experience) earns more than Rs. 10 lakhs per annum by virtue of their skills and expertise. Their salary increases are largely determined by their skill development and learning capabilities. If you are more adept at learning, you will be more likely to grow.

    Who should take this Data Science course in Delhi?

    Professionals interested in advancing their careers with Data Science should take this Data Scientist course in Delhi.

  • Professional/fresher with logical, mathematical, and analytical skills
  • Professionals working on Business intelligence, Data Warehousing and reporting tools
  • Banking and Finance Professionals
  • Analytics Managers, Marketing Managers, Supply Chain Network Managers
  • Statisticians, Economists, Mathematicians
  • Business analysts
  • What is the Data Science career path in Delhi?

    Delhi is a great place to work, with rewarding work, timely paychecks, and unending growth opportunities. Several multinational companies have established offices here, including Google, American Express, and Microsoft. Delhi is likely to provide you with your dream job. Data Science professionals/freshers in Delhi have a range of options at their disposal if they choose to pursue a Data Science course in Delhi. Our Data Science training course in Delhi is designed to train students with the in-demand skills they will need to succeed in today's job market. This course facilitates a deeper understanding of the Data Science domain enabling you to comprehend both trivial and substantial concepts with ease.

    What are the fees for the Data Science course in Delhi?

    In Delhi, the fee for the Data Science course is Rs. 3.69L inclusive of GST. EMI options are available. Take the course for two weeks - if you decide to withdraw, you're fully refunded. If you opt for an EMI option, your monthly payment will be as low as Rs. 8,628.

    Why should I learn Data Science in Delhi from Scaler?

    The Scaler Data science course in Delhi will prepare you for interviews and assist you with your career advancement. During this course, you will gain extensive practical experience by working on real-world projects. This program offers students a well-structured curriculum which includes program solving and CS fundamentals, programming skills such as R, Python, Java, etc., system design (high-level and low-level), programming constructs, specialization options (backend or full stack), etc. We provide 1:1 mentoring with top industry experts, conduct mock interviews, provide personal assistance, and provide ongoing guidance after course completion.

    Is this course suitable for freshers?

    Getting into the field of data science does not require previous experience. If you are a student or a fresher with a keen interest in data science and are planning to gain individual experience in this area, you can apply here. Maybe you are already working in one industry but want to pursue a course in data science due to your love of data or the growing interest and demand for this career path. A career in data science is highly rewarding and can boost your career prospects.

    What if I miss a lecture during the course?

    The student who pursues a Data science course may face more difficulties if he or she misses any lectures during a course. Missing classes directly affects numerous aspects of your data science skills, as well as your personal development. If you are also among those students who have a habit of missing lectures for a prolonged period, you do not have to worry. At the Scaler data science course in Delhi, every lecture is recorded. Students will have the opportunity to view them afterwards.

    Program Registration
    Thanks for your interest. We will let you know when the course is about to begin.