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Why Data Science?

In a nutshell, it involves extracting actionable insights from raw data. Various scientific methods, algorithms, and processes are used to extract insights from vast amounts of data. Data Science provides a vast array of tools for working with data coming from a variety of different sources, such as financial logs, multimedia files, marketing forms, sensors, and text files. An important aspect of Data Science is the preparation of data for analysis, including cleaning, aggregating, and manipulating it to perform advanced analyses.
A data scientist is someone who practices Data Science to analyze data so that they can provide actionable insights by using various processes, methods, systems, and algorithms. A Data Scientist is a highly qualified professional with highly developed mathematical, statistical, scientific, analytical and technical skills who can prepare, clean, and validate structured and unstructured data for businesses to use to make better decisions. 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
  • Globally, data scientists are the most sought-after, in-demand profession and almost all industries are actively hiring Data Scientists, so obtaining a Data Science certification is highly recommended. As Bangalore is home to several tech companies, the Data Science field in Bangalore offers vast and promising career opportunities. In this fast-growing field, we offer a Data Science training course in Bangalore designed for beginners and professionals. After you complete this Data science course in Bangalore, you will have an in-demand set of skills that are critical to today's career opportunities in:
  • 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
    Intermediate
    11 Months
    Advanced
    7 Months
    Module - 1

    Beginner Module

    5 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
    5 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
    Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    4 Months
    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
    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
    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
    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
    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
    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
    5 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
    Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    4 Months
    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
    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
    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
    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
    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
    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.
    Connect with an Academic Counsellor
    Get all the information about scholarships and low cost EMI options
    Eligibility: Anyone who graduated in 2022 or before

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

    Access job opportunities from our 600+ employer partners
    Exchange job opportunities with our 20K+ Scaler student community'
    Practice mock interviews with people working in the industry
    Optimize your resume & LinkedIn profile with our experienced experts

    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

    Who can do a Data Science course?

    Those who wish to learn Data Science, whether they are beginners or professionals, are welcome to enroll. Software, IT, and marketing professionals can enrol part-time or through external programs in Data Science.

    What is eligibility for a Data Science course in Bangalore?

    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 Bangalore?

    In Data Science, there are a variety of job roles that can be assigned depending on industry needs. Once you graduate from our Data Scientist course in Bangalore, you will possess an in-demand set of skills critical to today's career opportunities, which include: Data Scientist, Forecasting Analyst, SAS Analyst, Research Analyst, Data Analytics, Analytics Lead/Manager/Consultant, Machine Learning Engineer, Statistical Programming Specialist, Hadoop Developer, 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.

    What important skills will you learn through this Data Scientist course in Bangalore?

    A Data Scientist must possess a wide range of skills and be knowledgeable about the tools and techniques involved. Some of the key skills you learn with our Data Science program in Bangalore include statistical skills, mathematical skills, programming skills (Python, Java, etc.), Data mining skills, cloud computing skills (like AWS and others), ML skills (K-Nearest Neighbour, random forests, ensemble methods, support vector machines, etc.), database skills (SQL, NoSQL, etc.), etc

    What projects are included in this Data Scientist course?

    As part of the course, you will have the opportunity to work on real industry projects, get feedback directly from mentors, and discuss different solutions with your peers. A few sample projects are listed below:

  • Use data to design the best strategy for offering coupons and discounts.
  • Experiment with different driving routes to minimise wait times.
  • Predict when food will arrive at customers' addresses.
  • Sniff out fraud by analyzing millions of chat messages.
  • Who should take this Data Science course in Bangalore?

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

  • 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 Bangalore?

    Known for its parks and nightlife, Bangalore is also the center of high-tech industry in India. Bengaluru has grown at a breakneck pace in various industries (especially IT). Data science is one of the hottest fields in Bangalore, as there are several tech companies. Therefore, job and growth prospects in the Data Science field are vast and commendable in Bangalore.

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

    In Bangalore, 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.

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