Upskill & Transition to Data Science Role

A 6-month live structured program designed by industry experts to upskill & successfully transition to Data Science roles.

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From This Program, You Will Gain

All the Right Skills required to transition to Data Science roles

Multiple Industry-Relevant Projects to showcase in your resume

Career Transition to a top-tier company as a Data Scientist/Machine Learning Engineer

Structured Curriculum for a smooth transition to Data Science Roles

Module 1 : Data Fundamentals
duration_icon Duration: 8 Weeks

The first step towards becoming a Data Analyst, Data Scientist, or ML Engineer is to have strong command over the fundamentals of visualization, dashboarding & reporting of data.

Within this module, our goal is to become confident in data fundamentals.

Topics that will be covered:

  1. Excel

    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google spreadsheets

  2. Tableau + Excel

    • Flowcharts, Data Types, Operations
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-build Data Structures – List, Tuples, Dictionary, Set
    • Matrix Algebra, Number Systems

  3. SQL

    • Visual Analytics
    • Charts, Graphs, Operations on Data & Calculations in Tableau/ PowerBI
    • Advanced Visual Analytics & Level of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet & Workbook Formatting
Module 2 - ANALYTICAL PROFICIENCY AND BUSINESS INSIGHTS
duration_icon Duration: 6 Weeks

As a Data Scientist, it is important we know how to break down business situations and design correct metrics.

Moreover, you should also be able to use the powerful language of SQL to extract and analyze data.

Within this module, our aim is for you to become skilled at interpreting data to make informed business decisions and to present your findings with clarity.

Topics that will be covered:

  1. SQL

    • Introduction to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering & Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes & Partitioning

  2. Product Analytics

    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
Module 3 - FOUNDATIONS OF MACHINE LEARNING & DEEP LEARNING
duration_icon Duration: 10 Weeks

Mathematics is the foundation upon which Machine Learning & Deep Learning algorithms are built.

That is why, in this module, you will fall in love with mathematics as you solve engaging problems & build your solid foundations of Machine Learning & Deep Learning.

Topics that will be covered:

Advanced Python & Python Libraries:

  1. Python Libraries

    • Python Refresher
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API & Web Scrapping
    • Beautifulsoup & Tweepy

  2. Advance Python

    • Basics of Time & Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling & Modules

Maths for Machine Learning:

  1. Probability & 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

  2. Calculus, Optimization & Linear Algebra

    • Classification
    • Hyperplane
    • Halfspace
    • Calculus
    • Optimization
    • Gradient Descent
    • Principal Component Analysis
Module 4 - SPECIALIZATION
duration_icon

Duration: 8/18 Weeks

Within this module, you will work on multiple projects build in partnership with top companies.

You will get your hands dirty by working with messy & unclean datasets from real companies.

You have the flexibility to select either one or both of the offered specializations, based on your interests and career goals.

Topics that will be covered:

SPECIALIZATION 1: MACHINE LEARNING

  1. Supervised Learning

    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data Decision Trees
    • Bagging
    • Naive Bayes
    • SVM

  2. Unsupervised & Recommender Systems

    • Introduction to Clustering, k-Means
    • k-Means ++, Hierarchical
    • GMM
    • Anomaly/ Outlier/ Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis

AND/OR

SPECIALIZATION 2: DEEP LEARNING ( 8 WEEKS )

  1. Neural Networks

    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward & Backward Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning

  2. 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, & Detection

  3. Natural Language Processing

    • Text Processing & Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency Parsing
    • Topic Modelling, Language Modelling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Named Entity Recognition

  4. Generative AI

    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    • Attention
    • Transformers
    • HuggingFace
    • BERT
Module 5 - DATA SCIENCE IN PRODUCTION
duration_icon Duration: 8 Weeks

A great Data Scientist or ML Engineer is also capable of developing end-to-end pipelines & building applications powered by machine Learning models.

This is the reason why, Within this module, you will learn how to develop end-to-end ML pipelines. And you will work on the latest cloud platforms to deploy & monitor your models.

Moreover, Data structures & Algorithms are part of interviews at top product companies. That is why, you will also focus on Data Structures & Algorithms to be able to crack these interviews.

Topics that will be covered:

  1. Machine Learning Ops

    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • Github Actions
    • ML System Design
    • AWS Segemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark ML lib

  2. Advanced Data Structures & Algorithms

    • Arrays
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    • Searching & Sorting Algorithms
    • Recursion
    • Hashing & 2 pointers
Module 4 - Getting Hired

Once you have upskilled yourself to become a great data scientist, it is important that we now focus on getting you interview opportunities from diverse companies.

This process is usually in 3 phases:

1. Build a strong profile

2. Applying the right way

3. Acing the interview

We focus on all the above 3 objects in this phase.

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