Data Science Master Course
This course will provide a great kick-start by starting with Python, Data Visualization, Scraping, Data Cleaning and applying various ML Algos like Linear Regression, Logistic Regression, Decision Trees, Naive Bayes & so on. The content covers a vast domain including Sentiment Analysis, Temporal Data Modelling, Database Management Systems. The course is very practical and ensures sufficient hands-on practice by solving assignments & challenges.
Course Language
English, Hindi
Also get
  • Placement and Internship assistance through Hiring Blocks
  • Resume building tips
  • Interview preparation
  • Free access to Online Course
  • Skill enhancement classes
Also get
  • Placement and Internship assistance through Hiring Blocks
  • Resume building tips
  • Interview preparation
  • Free access to Online Course
  • Skill enhancement classes
Get free access to our online course and also get
a wildcraft bag absolutely free!
Course Content
Download Syllabus
Python Introduction
  • Data Science Quickstart Mode
  • Python 3.7 overview
  • Python Basics
  • Control Flow
  • Data Structures
  • Functions
  • OOPS and Modules
  • File Handling in Python
Mathematical Concepts and Data Visualisation
  • Linear Algebra
  • Data Visualisation
  • Probability Distribution & Statistics
  • Numpy, Scipy, and Scientific computation with Python
  • Data Analysis using Pandas
  • Data Acquisition - Web Scrapping, Web APIs
  • Data Scraping, Handling, Cleaning
  • Importing/Exporting Data from/to various sources
  • Identify missing/outliers data
  • Normalizing and Formatting data
Machine Learning Algorithms
  • K-Nearest Neighbour search
  • K-means clustering
  • Linear Regression
  • Logistic Regression
  • Decision trees and Naive Bayes
  • Random Forest Classifiers
  • Manual Ensembling Vs. Automated Ensembling
  • Bagging, Boosting of trees
  • Ada Boost and XGBoost
  • Support Vector Machines
  • SVD and Principal Component Analysis
Segmentation and Time Series
  • Introduction to Segmentation
  • Subjective Vs Objective, Heuristic Vs. Statistical
  • Heuristic, Behavioral Segmentation Techniques
  • Hierarchical Clustering vs Spectral Clustering
  • Feature selection and importance
  • Principle component Analysis (PCA)
  • Time Series - Components
  • Time Series - Averages, Smoothening, AR Models
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
Deep Learning
  • Neural Architectures and Training
  • Deep learning methods
  • Convolutions and the GoogLe Net
  • Dimensions revisited: The Auto-encoder
  • Recurrent and Combined Architectures
  • Transfer Learning
  • What is SQL Anyway?
  • Relational vs. Transactional Models
  • Creating Tables
  • Adding Comments to SQL
  • Basics of Filtering with SQL
  • Advanced Filtering: IN, OR, and NOT
  • Using Wildcards in SQL
  • Sorting and Math Operations
  • Joining Tables: An Introduction
  • Data Governance and Profiling
  • Using SQL for Data Science
Sentiment Analysis
  • What is Sentiment Analysis
  • Types of Sentiment Analysis
  • How Does Sentiment Analysis Work
  • Introduction to VADER
  • Tweet Sentiment Analysis with VADER
Projects and case studies
  • Face Recognition
  • Image classification and Object detection
  • Text/Poem generating bot
  • Recommender systems
  • PokeMon Classification
  • Web Crawler
  • Cartpole Player
  • Dominant Color Extraction
  • Fashion Retailing Forecasting
Center and Schedule
Starting from
5th June
Weekend Batch
Mon Wed Fri Sat
Princey Yadav
Starting from
5th June
Weekend Batch
Mon Wed Fri Sat
Manu S Pillai
Manu S Pillai
Manu is a Deep Learning Researcher and an avid python programmer. He has multiple research papers published in international conferences and journals. His interest lies in Computer Vision and is currently doing independant research in the same domain. He has also won multiple hackathons including Smart Odisha Hackathon and was awarded by the Chief Minister ot Odisha, Shri Naveen Patnaik. He can be found at
Princey Yadav
Princey is a post graduate from IIIT Delhi and did her graduation from NSIT Delhi. She joined Coding Blocks due to her passion for teaching. She has prior experience as a Software Engineer and has done research in the field of Computational Biology. Her interests include Machine learning, Data science and Game development. She is a pythonista and likes to draw and watch anime for fun.
Where our Alumni Work
Frequently Asked Questions
Find answers to the questions popping up in your head here.
Who should do this course?
This field intensively deals with mathematics, for analysis of data and algorithms. A decent mathematical background is a necessary and we engineers definitely excel at that! If you have had a mathematical background at school and covered engineering mathematics (such as probability, statistics, linear algebra etc/), you're good to go! Maybe you could just brush up the topics.
I know competitive programming. should I do this course?
Machine Learning requires extensive knowledge of mathematics, which you also gain while handling problems in competitive programming. This course shall equip with the right tools to handle huge amounts of data and derive meaningful conclusions from data crunching. Handling real-world problems is exactly what machine learning is all about, a skill very useful in development tasks. competitive programming equips you with critical thinking, but machine learning teaches you how to apply that skill and solve complex problems.
Will this help me in interviews?
Machine Learning and Data science has been called as the ‘Hottest job of the 21st century’. If you learn this course well, you’ll be able to impress quite a lot of interviewers across various interviews.
I don’t pass all the prerequisite criteria. Should I enroll?
Even if you don’t possess understanding of all the prerequisites, we shall help you cover every topic in detail and provide overview before diving deep into machine learning and data science. Python is a relatively easy language to learn, and you can pick up the basics very quickly. Therefore, you’ll have ample amount of time before the course to brush-up/learn the fundamentals.