Curriculum
OUTLINE

Topic
lectures
total Duration
Beginner
Beginner Modules
60
20 Weeks

SQL

24

8 Weeks

Tableau + Excel

12

4 Weeks

Beginner Python

24

8 Weeks

intermediate*
Data Analysis and Visualisation1
48
16 Weeks

Python libraries

12

4 Weeks

Probability and Statistics

12

4 Weeks

Fundamentals of Data Analysis

12

4 Weeks

Product Analytics

12

4 Weeks

SPECIAL ELECTIVE
Domain Analytics 2
24
8 Weeks

Complete Domain Immersion

6

2 Weeks

Domain-specific use case 1

6

2 Weeks

Domain-specific use case 2

6

2 Weeks

Domain-specific use case 3 & Final Project

6

2 Weeks

advanced
Machine Learning
72
24 Weeks

Maths for ML

12

4 Weeks

Intro to ML and NN

12

4 Weeks

ML Supervised Algorithms

12

4 Weeks

ML Time Series & Recommendation Systems

12

4 Weeks

ML Unsupervised Algorithms

12

4 Weeks

MLOps5

12

4 Weeks

SPECIAL ELECTIVE
Portfolio Project4
12
4 Weeks
Optional3
Neural Networks
12
4 Weeks
Natural Level Processing
12
4 Weeks
Computer Vision
12
4 Weeks
SPECIAL ELECTIVE
Generative AI
24
8 Weeks
Machine Learning System Design
24
8 Weeks

1 Placement assistance for Data Analyst/Product Analyst roles via mastery-based evaluation starts after completion of this module

2 Pick one case study across industries like E-commerce, Healthcare, Social Media Marketing and FinTech.

3 You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa

4 Must have Data Science certification (cleared till MLOps module) and enrolled in the Data Learning track. Must meet attendance and PSP requirements in past modules.

5 After completion of this module, placement assistance for Data Scientist (ML/DS) roles via mastery-based evaluation will start.

* Not an entry point for intermediate learners. An intermediate learner can choose/skip any of the above

A Deep Dive into
the Curriculum

Note: Listed below is the detailed insight into the Data Science and Machine Learning curriculum

Beginner Module - The Basics (20 weeks)

Topics Covered:
01SQL

SQL (Structured Query Language)

DDL (Data Definition Language)

DML (Data Manipulation Language)

DCL (Data Control Language)

TCL (Transaction Control Language)

SELECT

INSERT

UPDATE

DELETE

CREATE

ALTER

DROP

TRUNCATE

WHERE

FROM

GROUP BY

HAVING

ORDER BY

LIMIT

OFFSET

JOIN

UNION

INTERSECT

EXCEPT

INNER JOIN

LEFT JOIN (LEFT OUTER JOIN)

RIGHT JOIN (RIGHT OUTER JOIN)

FULL JOIN (FULL OUTER JOIN)

CROSS JOIN

SELF JOIN

PRIMARY KEY

FOREIGN KEY

UNIQUE

NOT NULL

CHECK

DEFAULT

Aggregate Functions (SUM, AVG, COUNT, MAX, MIN)

Scalar Functions (UPPER, LOWER, SUBSTRING, LENGTH, ROUND)

Date Functions (NOW, DATE_ADD, DATE_SUB, DATEDIFF)

Conversion Functions (CAST, CONVERT)

VIEW

SUBQUERY

CORRELATED SUBQUERY

CTE (Common Table Expression)

Stored Procedures

Triggers

Functions

02Tableau + Excel

Basic charts and operations

Data structuring options

Filters and calculations

Level of detail calculation

Table calculation and analytics

Geographic visualisations

Worksheet and Workbook Formatting

Operations on Dataset

Excel Functions & Formulas

Lookup Functions & Dynamic Array Formulas

Pivot Tables, Statistical Functions and Macros

Dashboards & Excel Capstone Intro

Google Spreadsheets

03Python

Data structures

Lists

Tuples

Dictionaries

Sets

If-else Statements

Loops (for, while)

Break

Continue

Control Flow

Functions

Modules

Packages

Exceptional Handling

Try-except blocks

OOP

Class

Object

Inheritance

Polymorphism

Encapsulation

Jupyter Notebooks

Get the hang of the predominant industry tool, Tableau, for visualising, dashboarding & reporting to ace your role as a Data Analyst, Data Scientist or ML Engineer.

Intermediate Module: Data Analysis and Visualisation 1 (16 Weeks)

Topics Covered:
01Python Libraries

Pandas

Numpy

SciPy

Matplotlib

Seaborn

Plotly

Data Analysis

Data Visualisation

Data Cleaning

Data Wrangling

Exploratory Data Analysis (EDA)

Missing Data Analysis

Data Discovery

Data Profiling

Data Inspection

Beautiful Soup

Web-API

Data Quality Checks

02Probability and Stats

Probability

Random Variable

Probability Distribution

Joint Probability

Conditional Probability

Bayes' Theorem

Probability Mass Function (PMF)

Probability Density Function (PDF)

Descriptive Statistics

Inferential Statistics

Statistical Measures

Population

Sample

Mean

Median

Mode

Variance

Standard Deviation

Correlation Coefficient

Confidence Interval

Skewness

Correlation

Covariance

Outlier Detection

Feature Engineering

Dimensionality Reduction

Data Distribution

Normal Distribution

Uniform Distribution

Binomial Distribution

Poisson Distribution

Kernel Density Estimation (KDE)

Correlation Analysis

Pearson Correlation Coefficient

Spearman Correlation Coefficient

03Fundamentals

Hypothesis Testing

Null Hypothesis (H0)

Alternative Hypothesis (H1 or Ha)

One-Tailed Test

Two-Tailed Test

Significance Level (α)

Type I Error (False Positive)

Type II Error (False Negative)

Power of the Test

Critical Value

Test Statistic

P-Value

Confidence Interval

Parametric Test

Non-Parametric Test

Z-test

T-test (Student's T-test)

ANOVA (Analysis of Variance)

Chi-Square Test

Kruskal-Wallis Test

04Product Analytics

Product Strategy & Business Acumen

Product Metrics - 1

Product Metrics - 2

Root Cause Analysis - 1

Root Cause Analysis - 2

CRM Analytics - RFM model

Customer Segmentation

A/B Testing & Launch Recommendation

Guess Estimate - 1

Guess Estimate - 2

Outshine your Problem-Solving skills as you learn to break down business situations, design correct metrics & deal with uncertainty.

From Emergency Call Centre to Casino of Las Vegas - Experience Probability & Statistics with a fresh perspective.

Special Elective: Domain Analytics2 (8 Weeks)

Topics Covered:
01Complete Domain Immersion

Develop a deep understanding of the selected domain

02Domain-specific use case 1

Dive deep into a domain-specific use case leveraging tools like Python, SQL or Tableau

03Domain-specific use case 2

Prepare a second domain-specific use case leveraging tools like Python, SQL or Tableau

04Domain-specific use case 3 & Final Project

Solve a capstone project, highlighting insights and the approach taken to solve the project, followed by a one-on-one viva

Build the necessary domain understanding and business knowledge, and delve into real-world case studies across industries such as E-commerce, Health Care, Social Media Marketing, and FinTech.

Participants will gain valuable insights into the intricate details of their chosen domain and develop practical skills through a structured journey of learning, mentorship, practical applications and hands-on projects.

Advanced Module: Machine Learning (24 Weeks)

Topics Covered:
01Maths for ML

Linear Algebra

Dot products

Hyperplanes

Halfspaces

Distance

Loss Function and Loss minimization

Calculus

Geometry

Optimisation

Constrained Optimisation

Principal Component Analysis

02Introduction to ML & NN

Supervised Learning

Unsupervised Learning

Semi-supervised Learning

Regression

Classification

Ridge and Lasso Regression

Regularisation

Performance Metrics

Confusion Matrix

F1-score

Receiver operating characteristic (ROC AUC)

Accuracy

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Mean Absolute Percentage Error (MAPE)

Mean Absolute Error (MAE)

Gradient Descent

Stochastic Gradient Descent (SGD)

Mini-Batch Gradient Descent

Learning Rate

03ML Supervised Algorithms

Linear Regression

Polynomial Regression

Logistic Regression

Support Vector Machines (SVM)

Decision Trees

Bagging/Boosting

Random Forest

Gradient Boosting

XGBoost

CatBoost

LightGBM

Naive Bayes

04ML Time Series & Recommendation Systems

Time Series Analysis

Time Series Decomposition

Stationarity

Seasonality

Trend

Noise

Cyclic Patterns

Autocorrelation

Partial Autocorrelation

Autoregressive (AR) Model

Moving Average (MA) Model

Augmented Dickey-Fuller (ADF) Test

Autoregressive Integrated Moving Average (ARIMA) Model

Seasonal ARIMA (SARIMA) Model

Exponential Smoothing

Holt-Winters Method

Differencing

Prophet

Long Short-Term Memory (LSTM) Networks

Recurrent Neural Networks (RNN)

Recommendation Engine

Recommender System

Collaborative Filtering

Content-Based Filtering

Hybrid Recommendation System

Cold Start Problem

User-Based Collaborative Filtering

Item-Based Collaborative Filtering

Matrix Factorization

Non-Negative Matrix Factorization (NMF)

Singular Value Decomposition (SVD)

Alternating Least Squares (ALS)

Implicit Explicit Feedback

Cosine Similarity

Feature Engineering

Deep Learning for Recommendations

Surprise Library

05ML Unsupervised Algorithms

Clustering Analysis

K-Means Clustering

Elbow Method

K-Means++

Hierarchical Clustering

Agglomerative clustering

Silhouette score

Within-Cluster Sum of Squares (WCSS)

DBSCAN

Anomaly Detection

Outlier Detection

Novelty Detection

Isolation Forest

Gaussian Mixture Model (GMMs)

Elliptic Envelope

Local Outlier Factor (LOF)

One-class-SVM

Dimensionality Reduction

Feature Extraction

Eigenvalues/Eigenvectors

Eigenvalues decomposition

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Uniform Manifold Approximation and Projection (UMAP)

06MLOPs

Git & GitHub

Streamlit

Flask

Containerization - Docker & DockerHub

AWS, ECS

GitHub Actions

CI/CD pipelines

MLFlow

AWS Sagemaker

Apache Spark

Be it forecasting the exact number of orders to be placed at a restaurant on New Year’s Eve or forecasting the number of oxygen cylinders a hospital will require. Scaler will ensure you get a hold of both situations like a Pro!

SPECIAL ELECTIVE

Topics Covered:
01Portfolio Project (4 weeks)

Work on projects built in partnership with top companies. Get your hands dirty by working with messy and unclean real-world data. Prepare for Data Science & Machine Learning interviews by getting your hands-on essential Problem-Solving skills.

Work on projects built in partnership with top companies. Get your hands dirty by working with messy and unclean real-world data. Prepare for Data Science & Machine Learning interviews by getting your hands-on essential Problem-Solving skills.

OPTIONAL

01Neural Networks (4 weeks)

Neural Network

Artificial Neural Network (ANN)

Deep Learning

Perceptron

Activation Function

Weight

Bias

Layer (Input, Hidden, Output)

Feedforward Neural Network

Backpropagation

Loss Function

Optimizer

Single-Layer Perceptron (SLP)

Multi-Layer Perceptron (MLP)

Autoencoder

Sigmoid

Hyperbolic Tangent (tanh)

Rectified Linear Unit (ReLU)

Leaky ReLU

Softmax

Gradient Descent

Stochastic Gradient Descent (SGD)

Mini-Batch Gradient Descent

Learning Rate

Batch Normalization

Regularization (L1, L2)

Dropout

Fine-tuning

Cross-Entropy Loss

TensorFlow

Keras

Explainable AI (XAI)

Interpretability in Neural Networks

Gradient Check

Vanishing Gradient

Exploding Gradient

GPU

TPU (Tensor Processing Unit)

Vanishing Gradient Problem

Exploding Gradient Problem

02Computer Vision (4 weeks)

Convolutional Neural Network (CNN)

Feature Extraction

Feature Map

Convolution Operation

Pooling (Max Pooling, Average Pooling)

Padding

Receptive Field

Weight Sharing

Translation Invariance

Convolutional Layer

Activation Map

Kernel (Filter)

Pooling Layer

Fully Connected Layer (Dense Layer)

Dropout Layer

Batch Normalization Layer

Transposed Convolution (Deconvolution or Upsampling)

AlexNet

VGG (VGG16, VGG19)

GoogLeNet (Inception)

ResNet (Residual Network)

DenseNet

MobileNet

U-Net

Intersection over Union (IoU)

Transfer Learning

Fine-tuning

Data Augmentation

Object Detection

Image Segmentation

Semantic Segmentation

Instance Segmentation

TensorFlow

Keras

PyTorch

Image Classification

Facial Recognition

Medical Image Analysis

Autonomous Vehicles

R-CNN

Fast R-CNN

Faster R-CNN:

Mask R-CNN

YOLO

FCN (Fully Convolutional Network)

Generative Adversarial Networks (GANs)

03Natural Language Processing (NLP) (4 weeks)

Word Embeddings

Word2Vec

GloVe (Global Vectors for Word Representation)

Sentiment Analysis

Topic Modeling

Sequence Modeling

Language Modeling

Temporal Dependency

Machine Translation

Text Generation

Transfer Learning in NLP

Sentiment Analysis

Text Classification

Question Answering

Text Summarization

Speech Recognition

NLTK (Natural Language Toolkit)

SpaCy

Gensim

Transformers (Hugging Face)

TensorFlow

PyTorch

Named Entity Recognition (NER)

Part-of-Speech Tagging (POS Tagging)

Sequence-to-Sequence Model (Seq2Seq)

Encoder-Decoder Architecture

Backpropagation Through Time (BPTT)

Gradient Clipping

Attention Mechanism

Self-Attention

Transformer Architecture

GPT (Generative Pre-trained Transformer)

Recurrent Neural Network (RNN)

Long Short-Term Memory (LSTM)

Gated Recurrent Unit (GRU)

Bidirectional RNN

BERT (Bidirectional Encoder Representations from Transformers)

BERT

Don’t stop at just building the models, learn to develop end-to-end ML pipelines. Build applications powered by your Machine Learning models. Work with the latest Cloud Platforms to deploy these apps and monitor your models

Special Elective: Generative AI (8 Weeks)

Topics Covered:

Introduction to GenAI

Types of GenAI Models
• Transformers
• Diffusion Models

Text Generation Models

Applications of LLMs

Langchain Framework

RAG (Retrieval Augment Generation)

Fine-tuning of LLMs

Image Generation Models

Advanced Techniques

Special Elective: Machine Learning System Design (8 Weeks)

Topics Covered:

System Design Principles

Machine Learning Pipelines

Distributed Training

Model Serving & Inference

High Availability & Fault Tolerance

Security & Privacy

Productionization

ML System Design

This is where you get 100x better than mediocre Data Scientists. Go from being a good Data Scientist to being great by learning to solve problems in the simplest and fastest way possible.