Curriculum
For Beginner Batches
MODULES
DURATION
(MONTHS)
ECTS CREDITS
Introduction to Programming (Beginner) 1 (1 month)
1
5
Introduction to Programming (Beginner) 2 (1 month)
1
5
Data Structure Algorithms 1 (2 months)
2
5
Data Structure Algorithms 2 (1 month)
1
5
Data Structure Algorithms 3 (1 month)
1
5
Data Structure Algorithms 4 (1 month)
1
5
Databases & SQL
1
5
Backend
Backend Project
1
5
Advance Programming concepts
1
5
Low level design
1
5
Advance Software Engineering
1
10
or
Fullstack
HTML/CSS : LLD 1
1
5
Java Scripts LLD2
1
5
React and Redux LLD 3
1
5
Full Stack Projects LLD 4
1
10
High Level Design
1
5
Note : 65 credits will be covered during the mandatory modules and an additional 25 credits can be completed by selecting electives from the list of Academy and DSML elective modules.
academy electives
Unchosen Specialisation
3
15
DSA 4.2
1
5
Data Engineering
2
10
Product Management
1
5
Neovarsity Exclusive
DSML Electives (Live)
ML Foundation 1 - Python Libraries
1
5
ML Foundation 2 - Probability & Statistics
1
5
ML Foundation 3 - Core Fundamentals
1
5
ML Foundation 4 - Product Analytics
1
5
DSML Electives (REC)
Mathematics for Machine Learning
Rec
0
Intro to Neural Networks & Machine Learning
Rec
0
Supervised Algorithms
Rec
0
Advanced Supervised Algorithm
Rec
0
Unsupervised Algorithms
Rec
0
Curriculum
Deep dive
Curriculum Deep Dive for Beginners(21 Months)
Program Timeline
Introduction to Programming (Beginner) 1 (1 month)
Introduction to Programming (Beginner) 2 (1 month)
- Introduction to Java
- Input, Output and Data Types
- Operators
- Conditions
- Loops
- Pattern Problems
- Functions
- 1D and 2D Arrays
- Strings
- Memory Management
- Introduction to Problem Solving
- Time and Space Complexity Analysis
- Introduction to Arrays
- Array Techniques
- Sorting Basics
- Bit Manipulation
- Problems on Strings
Data Structure Algorithms 1 (2 months)
Data Structure Algorithms 2 (1 month)
Data Structure Algorithms 3 (1 month)
Data Structure Algorithms 4 (1 month)
- Problems on Arrays
- Bit Manipulation
- Backtracking
- Sorting
- Searching (Binary Search)
- Two Pointers
- Stacks
- Queues
- Binary Tree
- BST
- Heaps
- Greedy
- Dynamic Programming
- Graphs
Databases & SQL
- Relational Model
- CRUD
- Joins
- Aggregation
- Subqueries
- Views
- Transactions
- Indexing
Backend Project
Advance Programming concepts
Low level design
Advance Software Engineering
- Object Oriented Programming
- Multithreading
- Adv Java Concepts and Popular Interview Questions
- SOLID Design Principles
- Design Patterns
- UML Diagrams
- Schema Design
- How Internet Works (TCP, UDP, HTTP, Layering Architecture)
- API Design
- MVC
- Backend LLD and Machine Coding Case Studies
- Unit Testing
- ORM
- Deployment
- Git
- Spring Boot
- Interview Questions (Spring/Hibernate)
- Capstone Project
HTML/CSS : LLD 1
Java Scripts LLD2
React and Redux LLD 3
Full Stack Projects LLD 4
- HTML, CSS, Javascript
- Advanced JS Concepts (OOP & Concurrency) and Popular Interview Questions
- JS for Web Dev (DOM Manipulation, Event Handling)
- Design Patterns
- Git
- React
- Redux
- How Internet Works (TCP, UDP, HTTP, Layering Architecture)
- API Design
- Deployment
- Frontend LLD and Machine Coding Case Studies
- Testing
- Capstone Project
High Level Design
- Introduction to Scale and Scaling Techniques
- Introduction to Caching Techniques
- Introduction to SQL and NoSQL Databases
- Introduction to Event Driven Architecture
- Introduction to Microservices Architecture
DSA 4.2
- Revision of DSA Topics
- Maths: Inverse Mod & Problems
- Backtracking: Famous Problems
- Tries
- Dynamic Programming( DP)
- Strings Pattern Matching
- DSU, Kruskal Algorithm & Bipartite Graph
- Bellman Ford & Floyd Warshall Algorithm
- Advanced Interview Problems
Data Engineering
- Building efficient Data Processing Systems
- Advanced SQL
- Cloud Services - AWS or GCP
- Developing ETL pipelines
- Map-Reduce Framework
- Big Data
- Data Warehousing & Modelling
- OLAP, Dashboarding
- Workflow Orchestration
- Logging and Monitoring
- MapReduce, HiveQL, Presto
- Projects
Product Management
- Introduction to Product Management
- Product Thinking & Product Discovery
- Product Roadmap & Prioritization
- Mental Models for Product Managers
- Product Analytics
- Mixpanel
- Hands on Case Studies
- Delivery & Project Management
- Practical ways to apply PM lessons as an Engineer
ML Foundation 1 - Python Libraries
- Numpy, Pandas
- Matplotlib
- Seaborn
- Data Acquisition
- Web API
- Web Scraping
- Beautifulsoup
- Tweepy
ML Foundation 2 - Probability & Statistics
- Probability
- Bayes Theorem
- Distributions
- Descriptive Statistics, outlier treatment
- Confidence Interval
- Central Limit Theorem
ML Foundation 3 - Core Fundamentals
- Hypothesis Test, AB Testing
- ANOVA
- Correlation
- EDA, Feature Engineering, Missing value treatment
- Experiment Design
- Regex, NLTK, OpenCV
ML Foundation 4 - Product Analytics
- Framework to address product sense questions
- Diagnostics
- Metrics, KPI
- Product Design & Development
- Guesstimates
- Product Cases from Netflix, Stripe, Instagram
Mathematics for Machine Learning
- Classification
- Hyperplane
- Halfspaces
- Calculus
- Optimization
- Gradient descent
- Principal Component Analysis
Intro to Neural Networks & 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
Supervised Algorithms
- MLE, MAP, Confidence Interval
- Classification Metrics
- Imbalanced Data
- Decision Trees
- Bagging
- Naive Bayes
- SVM
Advanced Supervised Algorithm
- SVM
- Time Series Analysis
- Supervised Learning Wrap-up
Unsupervised Algorithms
- Introduction to Clustering, k-Means
- K-means ++, Hierarchical
- GMM
- Anomaly/Outlier/Novelty Detection
- PCA, t-SNE
- Recommender Systems
- Time Series Analysis
For Intermediate
MODULES
DURATION
(MONTHS)
ECTS CREDITS
Introduction to Problem Solving (Intermediate) 1
0.5
5
Introduction to Problem Solving (Intermediate) 2
0.5
5
Advanced DSA 1
1
5
Advanced DSA 2
1
5
Advanced DSA 3
1
5
Advanced DSA 4
1
5
Databases & SQL
1
5
Backend
Backend Project
1
5
Advance Programming concepts
1
5
Low level design
1
5
Advance Software Engineering
1
10
or
Fullstack
HTML/CSS : LLD 1
1
5
Java Scripts LLD2
1
5
React and Redux LLD 3
1
5
Full Stack Projects LLD 4
1
10
High Level Design
1
5
Note : 65 credits will be covered during the mandatory modules and an additional 25 credits can be completed by selecting electives from the list of Academy and DSML elective modules.
academy electives
Unchosen Specialisation
3
15
DSA 4.2
1
5
Data Engineering
2
10
Product Management
1
5
Neovarsity Exclusive
DSML Electives (Live)
ML Foundation 1 - Python Libraries
1
5
ML Foundation 2 - Probability & Statistics
1
5
ML Foundation 3 - Core Fundamentals
1
5
ML Foundation 4 - Product Analytics
1
5
DSML Electives (REC)
Mathematics for Machine Learning
Rec
0
Intro to Neural Networks & Machine Learning
Rec
0
ML: Supervised Algorithms
Rec
0
ML: Advanced Supervised Algorithm
Rec
0
ML: Unsupervised Algorithms
Rec
0
Curriculum
Deep dive
Curriculum Deep Dive for Intermediate(19 Months)
Program Timeline
Introduction to Problem Solving (Intermediate) 1
Introduction to Problem Solving (Intermediate) 2
- Introduction to Problem-Solving
- Introduction to Time and Space Complexity
- Introduction to Basic Data Structures (1D and 2D Arrays, Strings)
- Introduction to Bit Manipulation
- Introduction to Problem-Solving Techniques (Prefix, Sliding Windows, Subarrays, Subsets, Subsequences, Sorting)
Advanced DSA 1
Advanced DSA 1
Advanced DSA 1
Advanced DSA 1
- Time and Space Complexity
- Array Problem-Solving Techniques
- Bit Manipulation
- Maths for Problem-Solving
- Recursion
- Backtracking
- Sorting
- Searching (Binary Search)
- Two Pointers
- Linked Lists
- Stacks
- Queues and Deques
- Trees and BST
- Heaps
- Greedy
- Dynamic Programming
- Graphs
Databases & SQL
- Relational Model
- CRUD
- Joins
- Aggregation
- Subqueries
- Views
- Transactions
- Indexing
Backend Project
Advance Programming concepts
Low level design
Advance Software Engineering
- OOP
- Multithreading
- Adv Lang Concepts and Popular Interview Questions
- SOLID
- Design Patterns
- UML Diagrams
- Schema Design
- How Internet Works (TCP, UDP, HTTP, Layering Architecture)
- API Design
- MVC
- Backend LLD and Machine Coding Case Studies
- Unit Testing
- ORM
- Deployment
- Git
- Spring Boot
- Project Interview Questions (Spring/Hibernate)
- Capstone Projects
HTML/CSS : LLD 1
Java Scripts LLD2
React and Redux LLD 3
Full Stack Projects LLD 4
- Git
- React
- Redux
- Deployment
- Testing
- MongoDB
- NodeJS
- ExpressJS
- Capstone Projects
High Level Design
- Introduction to Scale and Scaling Techniques
- Introduction to Caching Techniques
- Introduction to SQL and NoSQL Databases
- Introduction to Event Driven Architecture
- Introduction to Microservices Architecture
DSA 4.2
- Revision of DSA Topics
- Maths: Inverse Mod & Problems
- Backtracking: Famous Problems
- Tries
- Dynamic Programming( DP)
- Strings Pattern Matching
- DSU, Kruskal Algorithm & Bipartite Graph
- Bellman Ford & Floyd Warshall Algorithm
- Advanced Interview Problems
Data Engineering
- Building efficient Data Processing Systems
- Advanced SQL
- Cloud Services - AWS or GCP
- Developing ETL pipelines
- Map-Reduce Framework
- Big Data
- Data Warehousing & Modelling
- OLAP, Dashboarding
- Workflow Orchestration
- Logging and Monitoring
- MapReduce, HiveQL, Presto
- Projects
Product Management
- Introduction to Product Management
- Product Thinking & Product Discovery
- Product Roadmap & Prioritization
- Mental Models for Product Managers
- Product Analytics
- Mixpanel
- Hands on Case Studies
- Delivery & Project Management
- Practical ways to apply PM lessons as an Engineer
ML Foundation 1 - Python Libraries
- Numpy, Pandas
- Matplotlib
- Seaborn
- Data Acquisition
- Web API
- Web Scraping
- Beautifulsoup
- Tweepy
ML Foundation 2 - Probability & Statistics
- Probability
- Bayes Theorem
- Distributions
- Descriptive Statistics, outlier treatment
- Confidence Interval
- Central Limit Theorem
ML Foundation 3 - Core Fundamentals
- Hypothesis Test, AB Testing
- ANOVA
- Correlation
- EDA, Feature Engineering, Missing value treatment
- Experiment Design
- Regex, NLTK, OpenCV
ML Foundation 4 - Product Analytics
- Framework to address product sense questions
- Diagnostics
- Metrics, KPI
- Product Design & Development
- Guesstimates
- Product Cases from Netflix, Stripe, Instagram
Mathematics for Machine Learning
- Classification
- Hyperplane
- Halfspaces
- Calculus
- Optimization
- Gradient descent
- Principal Component Analysis
Intro to Neural Networks & 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
Supervised Algorithms
- MLE, MAP, Confidence Interval
- Classification Metrics
- Imbalanced Data
- Decision Trees
- Bagging
- Naive Bayes
- SVM
Advanced Supervised Algorithm
- SVM
- Time Series Analysis
- Supervised Learning Wrap-up
Unsupervised Algorithms
- Introduction to Clustering, k-Means
- K-means ++, Hierarchical
- GMM
- Anomaly/Outlier/Novelty Detection
- PCA, t-SNE
- Recommender Systems
- Time Series Analysis
For Advanced
MODULES
DURATION
(MONTHS)
ECTS CREDITS
Advanced DSA 1
1
5
Advanced DSA 2
1
5
Advanced DSA 3
1
5
Advanced DSA 4
1
5
Databases & SQL
1
5
Backend
Backend Project
1
5
Advance Programming concepts
1
5
Low level design
1
5
Advance Software Engineering
1
10
or
Fullstack
HTML/CSS : LLD 1
1
5
Java Scripts LLD2
1
5
React and Redux LLD 3
1
5
Full Stack Projects LLD 4
1
10
High Level Design
1
5
Note : 55 credits will be covered during the mandatory modules and an additional 35 credits can be completed by selecting electives from the list of Academy and DSML elective modules.
academy electives
Unchosen Specialisation
3
15
DSA 4.2
1
5
Data Engineering
2
10
Product Management
1
5
Neovarsity Exclusive
DSML Electives (Live)
ML Foundation 1 - Python Libraries
1
5
ML Foundation 2 - Probability & Statistics
1
5
ML Foundation 3 - Core Fundamentals
1
5
ML Foundation 4 - Product Analytics
1
5
DSML Electives (REC)
Mathematics for Machine Learning
Rec
0
Intro to Neural Networks & Machine Learning
Rec
0
ML: Supervised Algorithms
Rec
0
ML: Advanced Supervised Algorithm
Rec
0
ML: Unsupervised Algorithms
Rec
0
Curriculum
Deep dive
Curriculum Deep Dive for Advanced(18 Months)
Advanced DSA 1
Advanced DSA 1
Advanced DSA 1
Advanced DSA 1
- Time and Space Complexity
- Array Problem-Solving Techniques
- Bit Manipulation
- Maths for Problem-Solving
- Recursion
- Backtracking
- Sorting
- Searching (Binary Search)
- Two Pointers
- Linked Lists
- Stacks
- Queues and Deques
- Trees and BST
- Heaps
- Greedy
- Dynamic Programming
- Graphs
Databases & SQL
- Relational Model
- CRUD
- Joins
- Aggregation
- Subqueries
- Views
- Transactions
- Indexing
Backend Project
Advance Programming concepts
Low level design
Advance Software Engineering
- OOP
- Multithreading
- Adv Lang Concepts and Popular Interview Questions
- SOLID
- Design Patterns
- UML Diagrams
- Schema Design
- How Internet Works (TCP, UDP, HTTP, Layering Architecture)
- API Design
- MVC
- Backend LLD and Machine Coding Case Studies
- Unit Testing
- ORM
- Deployment
- Git
- Spring Boot
- Project Interview Questions (Spring/Hibernate)
- Capstone Projects
HTML/CSS : LLD 1
Java Scripts LLD2
React and Redux LLD 3
Full Stack Projects LLD 4
- Git
- React
- Redux
- Deployment
- Testing
- MongoDB
- NodeJS
- ExpressJS
- Capstone Projects
High Level Design
- Introduction to Scale and Scaling Techniques
- Introduction to Caching Techniques
- Introduction to SQL and NoSQL Databases
- Introduction to Event Driven Architecture
- Introduction to Microservices Architecture
DSA 4.2
- Revision of DSA Topics
- Maths: Inverse Mod & Problems
- Backtracking: Famous Problems
- Tries
- Dynamic Programming( DP)
- Strings Pattern Matching
- DSU, Kruskal Algorithm & Bipartite Graph
- Bellman Ford & Floyd Warshall Algorithm
- Advanced Interview Problems
Data Engineering
- Building efficient Data Processing Systems
- Advanced SQL
- Cloud Services - AWS or GCP
- Developing ETL pipelines
- Map-Reduce Framework
- Big Data
- Data Warehousing & Modelling
- OLAP, Dashboarding
- Workflow Orchestration
- Logging and Monitoring
- MapReduce, HiveQL, Presto
- Projects
Product Management
- Introduction to Product Management
- Product Thinking & Product Discovery
- Product Roadmap & Prioritization
- Mental Models for Product Managers
- Product Analytics
- Mixpanel
- Hands on Case Studies
- Delivery & Project Management
- Practical ways to apply PM lessons as an Engineer
ML Foundation 1 - Python Libraries
- Numpy, Pandas
- Matplotlib
- Seaborn
- Data Acquisition
- Web API
- Web Scraping
- Beautifulsoup
- Tweepy
ML Foundation 2 - Probability & Statistics
- Probability
- Bayes Theorem
- Distributions
- Descriptive Statistics, outlier treatment
- Confidence Interval
- Central Limit Theorem
ML Foundation 3 - Core Fundamentals
- Hypothesis Test, AB Testing
- ANOVA
- Correlation
- EDA, Feature Engineering, Missing value treatment
- Experiment Design
- Regex, NLTK, OpenCV
ML Foundation 4 - Product Analytics
- Framework to address product sense questions
- Diagnostics
- Metrics, KPI
- Product Design & Development
- Guesstimates
- Product Cases from Netflix, Stripe, Instagram
Mathematics for Machine Learning
- Classification
- Hyperplane
- Halfspaces
- Calculus
- Optimization
- Gradient descent
- Principal Component Analysis
Intro to Neural Networks & 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
Supervised Algorithms
- MLE, MAP, Confidence Interval
- Classification Metrics
- Imbalanced Data
- Decision Trees
- Bagging
- Naive Bayes
- SVM
Advanced Supervised Algorithm
- SVM
- Time Series Analysis
- Supervised Learning Wrap-up
Unsupervised Algorithms
- Introduction to Clustering, k-Means
- K-means ++, Hierarchical
- GMM
- Anomaly/Outlier/Novelty Detection
- PCA, t-SNE
- Recommender Systems
- Time Series Analysis