RoadMap to be a Data Scientist

Masters Program in Data Science & Machine Learning

(Duration – 4 to 4.5 Months)

LEVEL-1

Python Programming(Basic to Advanced Level)

  • Introduction to Python Basic
  • Introduction to Python Object
  • Python Data Types
  • Conditional Statements
  • Iterators
  • Loops and its implementations
  • Functional Programming
  • Modular Programming
  • Data Scientists tool pack
  • Data Visualization using Matplotlib &Seaborn
  • Python Code Debugging and Troubleshooting Techniques
  • Python Code Optimization Techniques
  • Hacking Techniques using Python
  • Data Analytics Project using Python
  • OOPS concepts
  • SOLID Principals
  • Modules & Packages

LEVEL-2

Business Decision Making using Statistics

  • Introduction
  • Random Variables
  • Descriptive Statistics
  • Inferential Statistics
  • Probability Concepts
  • Probability Distributions
  • Binomial, Poisson and Normal Distributions 
  • Probability for Business Decision Making
  • Exploratory Data Analysis
  • Presentation of Data
  • Hypothesis Formation
  • Hypothesis Testing
  • Z-test
  • T-test
  • Chi Square test
  • Implementation of hypothesis in business use cases
  • Analysis of Variances (ANOVA)

LEVEL-3

Supervised Machine Learning Algorithms

  • Linear Regression
  • Group Project and Presentation
  • Logistic Regression
  • Naïve Bayes
  • Support Vector Machines
  • K- nearest neighbors (KNN)
  • Model Selection Rationale
  • Model Hyper Parameter Tuning
  • Classification and Regression Tree (CART)
  • Random Forest
  • Boosting Techniques
  • Bagging Techniques
  • K- fold Cross Validation

LEVEL-4

Unsupervised Machine Learning

  • Different Distance Measures
  • Hierarchical Clustering
  • K- means Clustering
  • K- medoid Clustering
  • Partition Around Medoids Clustering
  • Feature Selection Techniques
  • Principal Component Analysis

LEVEL-5

MySQL & MongoDB in Practice

  • Introduction of MySQL
  • Installation of MySQL
  • Installation of SQL Workbench
  • CRUD Operations in SQL
  • Introduction to Mongo DB
  • Integration of Python with MySQL
  • What is Mongo DB?
  • SQL vs No SQL DB
  • ACID Property
  • Cap Theorem
  • Where to implement NoSQL DB
  • Understanding of basics like Collection, Document etc.
  • Introduction to JSON

LEVEL-6

Data Visualization

  • Power BI
  • Tableau
  • Seaborn
  • Matplotlib
  • Plotly
  • Exploratory Data Analysis (EDA)
  • Project on EDA

LEVEL-7

Model Hyperparameter Tuning & Project Deployment Toolbox

  • Building Efficient Machine Learning Project Pipelines
  • Machine Learning model hyper-parameter tuning
  • Development of a scalable ML model
  • Designing Web Interface using Python Flask
  • Use of Git and GitHub
  • Use of Docker in industry grade models
  • Introduction to AWS
  • Deployment of Machine Learning Models on AWS
  • Model Maintenances on Cloud in Practice

LEVEL-8

Industry Preparations

  • Resume Scaling
  • LinkedIn Profile Optimization
  • Working on GitHub and Open-Source Contribution
  • Data Structures and Algorithms based questions
  • Mock Interview Session’s
  • Agile Methodology