Data Science Immersive Bootcamp

Problem Formulation and Framework to choose right problem statement

Hypothesis Building and Framework

How to Build Comprehensive Hypothesis set

Excel Basics, Formulas and Functions

Excel Charts, Pivot Tables, Sort, Filter, What-if Analysis tool

Build business simulation using Excel

Basics of databases

ACID and BASE properties of a database

Working with SQL, Extract data from databases containing multiple tables

Performing Data Analysis using SQL

Loading datasets and establishing table relationships

Work with different type of charts and dashboards

Working with Map visualizations and other advanced charts with drill down functionalities

Working with power query for data manipulation

Writing DAX expressions

Python Basics Programming (Conditional, Looping, Functions)

Pandas, Matplotlib, Seaborn, regular expression, beautifulsoup

Good programming practices (testing, debugging, assertions, exception handling)

Get familiar with functional programming and its use cases

Understand the concepts of Object Oriented Programming (Inheritance, encapsulation)

Learn about version control and working with github

Connecting with databases using python

Public Vs Private Cloud

IaaS vs PaaS Vs SaaS

AWS Global Infrastructure

AWS Compute Services - EC2, AWS Lambda

AWS Storage Services - S3, DynamoDB, RedShift

AWS Security Policies

Monitoring & Analysis - AWS CloudWatch

Univariate, Bi-variate and Multivariate analysis

Work with different type of tests like t-test, z-test, chi-square test, anova

Work with Missing values, outliers, data pre-processing

Learn Important ML Basics Concepts (Train, Test, Validate, Bias , Variance, Overfitting, Underfitting)

Work with Evaluation metrics (Classification and Regression both)

Work with different validation techniques

Perform data cleaning and Preprocessing

Linear Models, Decision Tree, k-NN

Math Behind each Machine Learning Algorithm

Building Classification and Regression Models

Hyperparameter Tuning to improve model

Introduction to unsupervised learning and clustering

Working of clustering algorithms (k-means clustering)

Evaluation metrics for unsupervised learning problems

Understand feature engineering for structured and unstructured data

Perform feature extraction, generation and transformation techniques

Explore the basic and advanced ensemble techniques (rank averaging, random forest and more)

Challenges & Applications of Big Data

Introduction to Distributed Computing

Spark Architecture

RDDs & DataFrames

Understanding Spark Execution

Building Classification & Regression Models

Building ML Pipelines

SQL vs NoSQL databases

Different types of NoSQL databases

Querying, Aggregation & Indexing in MongoDB

Replicate & Share data in MongoDB

Exploring time series data and dealing with date-time variable

Learn Basic Forecasting algorithms (naive approach, simple average, moving average)

Work with Exponential smoothing algorithms (Simple, double, holts winter)

Work with Advanced forecasting algorithms (ARIMA, SARIMA, Prophet)

Introduction to NLP and its applications

Handling Text Data (Cleaning and Pre-processing)

Information Extraction and Retrieval from text

Language Modelling

Feature Engineering from textual data

Text Classification

Topic modelling

Introduction to Deep Learning

Introduction to Neural Networks

Activation Function, Optimizers, Loss Functions

Image Classification

Transfer Learning

RNN, LSTM, GRU

Text Classification

Introduction to Model Deployment

Deploying Your First Machine Learning models

Deploying ML Learning model using Streamlit + AWS

Deploying Image Classification model using Streamlit + AWS