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Is your Data science course’s curriculum aligned with the industry?

Up-to-date curriculum with the fast-evolving Data Science and ML field.
Beginner
15 Months
Intermediate
11 Months
Advanced
7 Months
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
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
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
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
Programme Fee - Starting at INR 7,200/month. No cost EMI options available
INR 2,50,000 (including GST)
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Have more questions? Contact us and get all your questions answered by our expert Data Science counsellor. Call us on 08045579578
Have more questions? Reach us at 08045579578