Python Basics Programming (Conditional, Looping, Functions)

Pandas, Matplotlib, Seaborn, regular expression, beautifulsoup

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

SQL vs NoSQL databases

Different types of NoSQL databases

Querying, Aggregation & Indexing in MongoDB

Replicate & Share data in MongoDB

Best practice to perform hypothesis generation

Hypothesis Building and Framework

How to Build Comprehensive Hypothesis set

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

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

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)

Introduction to unsupervised learning and clustering

Working of clustering algorithms (k-means clustering)

Evaluation metrics for unsupervised learning problems

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

Building Scalable Models

Introduction to Distributed Computing

Spark Architecture

RDDs & DataFrames

Understanding Spark Execution

Building Classification & Regression Models

Building ML Pipelines