Mastering Python
for Data Science

Learn Important Machine Learning concepts

Perform data cleaning and Preprocessing

In-depth understanding of Basic ML models

Linear Models, Decision Tree, k-NN

Math Behind each Machine Learning Algorithm

Building Classification and Regression Models

Hyperparameter Tuning to improve model

Solving real-world business problems using Machine Learning

Learn the art of Feature engineering

Feature Generation from time-series data

Automated Feature Engineering Tool

Concept of dimensionality reduction

Feature Selection and Elimination Techniques

Detailed Understanding of Principal Component Analysis (PCA)

Concept of Factor Analysis

Explore the Advanced ML concepts and Algorithms

Use Ensemble Learning Techniques (Stacking and Blending)

Understand and Implement Bagging and Boosting Algorithms

Learn to handle Text data and Image Data

Working with structured and unstructured data

Dealing with unsupervised learning problems

Clustering Algorithms including k-means and Hierarchical clustering

Important concepts of Deep Learning

Working of Neural Network from Scratch

Activation Functions and Optimizers for Deep Learning

Understand Deep Learning architectures (MLP, CNN, RNN and more)

Explore Deep Learning Frameworks like Keras and PyTorch

Learn to tune the hyperparameters of Neural Networks

Build Deep Learning models to tackle real-life problems

Get familiar with the world of Computer Vision

Transfer Learning for Computer Vision

Work with popular Deep Learning Framework - Pytorch

Learn State-of-the-art Algorithms like YOLO, SSD, RCNN and more

Work on different types of problems

Build Face Detection and Pose Detection Models

Advanced CV Problems like Image Segmentation and Image Generation

Understand how GANs work

Handling Text Data (Cleaning and Pre-processing)

Use Spacy, Rasa and Regex for exploring and processing text data

Information Extraction and Retrieval from text-based data

Understand Language Modelling

Learn Advanced Feature Engineering techniques

Build NLP models for Text Classification

Understand Topic modelling

Work on Industry Relevant Projects

Understand the concept of Sequence-to-Sequence Modeling

Build a Deep Learning Model for Language translation in PyTorch

Learn to use Transformers library by Huggingface

Use Transformers to perform transfer learning in NLP

Build and Deploy your own chatbot

Learn to work with audio-based data

Build a voice assistant system using Deep Learning

Recommender Systems in industry

Detailed Taxonomy of types of Recommender Systems

Collaborative Filtering Methods

Content-Based Recommender Systems

Knowledge-Based & Hybrid Recommender Systems

Market Basket Analysis & Association Rules

Evaluation of Recommender Systems

Build Book recommender System and other real-life projects

Important concepts of Time Series Forecasting

Machine Learning techniques for Time Series forecasting

Validation techniques for Time series data

Framework to evaluate Time Series Models

Exponential Smoothing Methods for forecasting

Reading ACF and PACF plots

Tuning Parameters for ARIMA

ARIMA and SARIMA Model

Deep Learning for time series

Solve Real-world business problems

Understanding the different roles in Data Science

Dos and Don'ts for Resume Building

Tips and strategies to build the perfect resume

Preparing for Data Science Interviews

Understanding the important skills required

How to build your digital Presence

Tips and Tricks to Ace Data Science Resume

List of Interview Questions for Data Science

Overview of Python Basics

Documentation and Formatting

Testing and Debugging

Exception Handling and Assertions

Python Standard Libraries

Functional Programming

Object Oriented Programming

Working with Shell/Terminal Commands

Introduction to Version Control and Github

Connecting with DataBases

Understanding NoSQL Databases

Querying in MongoDB

Aggregation Pipeline

Indexing in MongoDB

Replication and Sharding

Introduction to Data Engineering

Demand for Data Engineers

Roles & Responsibilities of Data Engineer

Big Data and its applications

Distributed Systems

Components of Apache Hadoop

Hadoop Ecosystem

Introduction to Hadoop 1.x

Working and Archutecture of MapReduce

Introduction to Hadoop 2.x

Understanding HDFS and Components

Working and Architecture of YARN

Hadoop 2.x vs Hadoop 3.x

Overview of Hive

Creating databases and tables in Hive

Types of Hive tables

Hive Query Language

Joins in Hive

Partitioining and Bucketing

File Formats and SerDes in Hive

Views in Hive

Introduction to Hadoop and Spark Ecosystem

Deep Dive into Spark Ecosystem

RDDs in Spark

DataFrames in Spark

Spark SQL

Data Wrangling with Spark

Jobs, Stages and Tasks in Spark

Advanced Programming in Spark

Machine Learning with Spark ML

Challenges with traditional stream processing systems

DStreams

Spark Streaming Architecture

Spark Streaming Data Sources

Transformations on DStreams

Stateful and Stateless Transformations

Overview and aspects of Model Deployment

Deploying Machine Learning models using Streamlit

Introduction to Amazon Web services

Deploying and Machine Learning Deep Learning models using AWS

Understanding Amazon Sagemaker

Model Deployment using Sagemaker

APIs for Model deployment