Data Science Masters Degree Program by Eckovation

Learn the principles of data science and tools like Python, Machine Learning, R Programming, Tableau and more

Created By: Akshat Goel

Course Duration : 6 Month

₹29,999

109 Ratings | 151 Enrollments

Includes

  • Lifetime access to content

  • 24 x 7 access to mentors and experts

  • Certificate of completion and Internship

  • Guaranteed Placement Assistance

What will I learn?

  • Learn about Machine Learning, R, Python, Deep Learning and Spark

  • Understanding how data impacts a company's growth

  • Build 6 industry grade projects

Description

Harvard Business Review, the most sought after the international magazine has termed DATA SCIENCE as the “Most Sexiest Job” of the 21st century. With an average salary of 8 lakhs per annum in India, it has indeed become the most in-demand skill for computer graduates. And if you have proven skills in the domain, it becomes easier to ladder up your career in the regarded field.

Here comes this certification course! In the Master Degree Program in Data Science, you will not only learn the know-how of various aspects of Data Analysis but will eventually have a Verified Certificate. This will help you showcase your skill sets in the job interviews that you will face afterward.

The curriculum of this course is designed by some of the most influential Data Science leaders. So all you have to do is just to be regular with the course contents. The course starts with the Introduction to Data Science and then glide up the level with the platforms where we can work on. The sequence of the modules of this online course goes as follows:

PYTHON

PYTHON has become the most commonly used programming language. Recently many effective tools have been developed on Python, which makes it easy to analyze a huge amount of Data in Python. You will learn in this course how to use functions and import packages in python. Data Science in Python will equip you with the concepts of Machine Learning and Deep Learning which are also very important.

R

R is a programming language designed especially for statistical computations. R is widely used among statisticians and Data Miners. Here you will learn how to import your Data on R, transform it into the most useful structure, visualize it and then model it. R lets you have the best interpretation of your data through detailed visualization. You will also learn how to clean data and produce usable plots out of it. At the end of this section, you will be able to explore R Data structures and syntaxes, work with Data and transform it as per your needs

TABLEAU

TABLEAU is one of the fastest evolving Business Intelligence (BI) and Data visualization tool. It lets you produce the most interactive Data visualizations. You will learn how to use queries on relational databases in this section and have a perfect visualization of the resultant data set. It is very fast and easy to learn tool for Data analysis. You will also be able to connect data from a variety of sources such as Texts, Excel files, Databases and Big Data queries as well. You will also go through the various types of charts available in Tableau.

The course doesn’t have any pre-requisites. So anyone can easily learn all the required concepts through the contents of this online course and accelerate their way to a shining career in Data Science.

If you have any doubts or queries, you may Contact us at 9711608586 (Mr. RITESH)

Along with DATA SCIENCE COURSE, the following courses will also give you an extra advantage over others

Details of Guaranteed Internship: 

i) Why? - We believe acquiring a new skill without working on an actual project is wasteful spending of your time and resources. For your holistic learning experience, both course and internship should be combined.

ii) Who? - Internship is guaranteed by Eckovation to all Eckovation Alumni - those who complete atleast a course with us.

iii) Where? - Internship will be either with Eckovation or one of our partner organisations. You will be able to access internship.eckovation.com once you register for a course with us. Our Internship Portal will feature profiles of our alumni and also list internship opportunities by various organisations where alumni can apply.

iv) How many? - The internship will be related to the skill(s) which you've acquired from Eckovation platform. In case of multiple skills, multiple internship opportunities may be provided.

v) Nature of internship? - The internships will be work from home, i.e., you will be able to work on the internship project from anywhere anytime.

vi) When? - Internship will start only after your course completion in either Summer/December Vacation depending on which comes first. In-semester internships can also be considered on case-by-case basis.

vii) Certificate - A separate internship certificate will be provided at the end of internship period. course description

Guaranteed Placement assistance to successful candidates

Successeful candidates will receive resume building sessions and reviews on their resume. Mock interviews and personal guidance in exceling interviews for the role of Data Scientists or of similar profiles.

Curriculum

Introduction to Python

  • Python and its Applications

  • Installation and Configuration

  • Working with IDLE (Integrated Development & Learning Environment)

  • “Hello World” Program

  • Data Types

  • Variables

String manipulation

  • String Concatenation

  • Indexing

  • Slicing of Strings

  • Typecasting and its Applications

  • Escape character

  • User inputs

Data structures

  • Lists

  • Tuples

  • Differences between lists and tuples

  • Dictionaries

  • Set

  • Practical applications of Data Structures

Control loops

  • If-else loop

  • For loop

  • While loop

  • Problems based on control loops

Functions

  • Inbuilt functions in Python

  • Function definition

  • Function calling

  • Problems based on functions

Object Oriented Programming

  • Introduction to Object Oriented Programming Design Paradigm

  • Classes and objects

  • Constructor Function

  • Class Variables

  • Class Methods

  • Static Methods

  • Practical implementation of Object Oriented Programming

Modules and packages

  • Creation of Python modules

  • Packages

  • Installation and usage of pip (package manager)

  • Importing modules

Exception handling

  • Introduction to exceptions

  • Try and except block

  • ‘Finally’ keyword

  • File handling

  • Networking

Python for Data Science

  • Numpy

  • Scipy

  • Pandas

  • Matplotlib

  • Scikit learn

Introduction Big Data and Hadoop

  • Introduction to the Course

  • Introduction to Big Data

  • Need for Handling Big Data

  • Structure of Big Data

  • Storage Technique

  • Application of Big Data

  • Big Data - Impact on IT

  • Overview of Big data Solutions

Hadoop Ecosystem

  • HDFS

  • Architecture (HDFS)

  • Hadoop Clusters

  • Hadoop Ecosystem

Cloudera VM Installations

  • Cloudera VM Overview

  • Cloudera VM Installation

  • VM Player Installation

  • Single Node Cluster Installation and Setup

  • Multi Node Cluster Installation and Setup

Hadoop Distributed File System

  • HDFS

  • HDFS Daemons

  • Writing Files to HDFS

  • Re-replicating Missing replicas

  • Checkpoints and Journals

  • Data Node Startup

  • Data Node Heartbeats

HDFS Shell Command

  • HDFS Shell Command

  • Practice

Introduction to Machine Learning

Regressions

  • Simple Linear Regression

  • Multiple Linear Regression

  • Polynomial Regression

  • Logistic Regression

  • SVR

  • Decision Tree

  • Random Forest

Classification

  • KNN

  • SVM

  • Kernel SVM

  • Naive Bayes

  • Decision Tree

  • Random Forest

Clustering

  • K means

  • Hierarchical

PCA/LDA

  • PCA

  • LDA

  • Kernel PCA

Recommended System development

Natural Language Processing

  • Text Classification

  • Language Modeling and Sequence

  • Tagging

  • Vector space modeling

  • Sequence to sequence tasks

Neural Networks

  • Introduction to Deep Learning and Neural

  • Networks

  • Introduction to Tensorflow

  • Tensorflow installation

  • Tensorflow basics

  • Artificial Neural Network

Introduction to R

  • Overview of the course

  • Intro to Data Analysis

  • Intro to R - Basics

Basic R Programming

  • Vectors

  • Matrices

  • Factors

  • Data Frames

  • Lists

  • Conditions

  • Loops

  • Functions

  • Apply Functions

  • Utility functions

  • Regular Expressions

  • Time and Date

Tidyverse

  • Data wrangling

  • Data visualization

  • Grouping and summarizing

  • Types of visualizations

Importing Data

  • Importing data from flat files with utils

  • readr & data.table

  • Importing Excel data

  • Reproducible Excel work with XLConnect

  • Importing data from databases

  • Importing data from the web

  • Importing data from statistical software packages

Cleaning Data

  • Introduction and exploring raw data

  • Tidying data

  • Preparing data for analysis

  • Putting it all together

Case Study and Assignment 1

  • Ticket Sales Data

  • MBTA Ridership Data

  • World Food Facts

  • School Attendance Data

dplyr: Data Manipulation

  • Introduction to dplyr and tbls

  • Select and mutate

  • Filter and arrange

  • Summarize and the pipe operator

  • Group_by and working with databases

dplyr: Joining Data

  • Mutating joins

  • Filtering joins and set operations

  • Assembling data

  • Advanced joining

  • Case study

ggplot 2: Data Visualization

  • Introduction

  • Data

  • Aesthetics

  • Geometries

  • qplot and wrap-up

Case Study and Assignment 2: Sentiment Analysis

  • Tweets across the United States

  • Shakespeare gets Sentimental

  • Analyzing TV News

  • Singing a Happy Song (or Sad?!)

Data and R

  • Language of data

  • Study types and cautionary tales

  • Sampling strategies and experimental design

  • Case study

Exploratory Data Analysis

  • Exploring Categorical Data

  • Exploring Numerical Data

  • Numerical Summaries

  • Case Study

Case Study and Assignment 3: Exploratory Data Analysis

  • Data cleaning and summarizing with dplyr

  • Data visualization with ggplot2

  • Tidy modeling with broom

  • Joining and tidying

Reporting with R Markdown

  • Authoring R Markdown Reports

  • Embedding Code

  • Compiling Reports

  • Configuring R Markdown (optional)

Project: Fortune teller

  • Problem statement: A Organisation who is facing the issues with the people leaving the organization in a very short interval of the time want to predict whether someone is trying to lave in upcoming six months which will help them do the backup planning. The data is given and We need to predict if someone will leave the organization.

Project: RFM Analysis

  • The company wants to segregate the customers on the basis of the purchase they have done there are various criteria to measure the performance Recency Frequency and Monetary. We need to perform an RFM analysis. RFM depends on data for individual transactions. The data have to include, at the very least, an invoice number, customer identification number, purchase date, and purchase amount. The data set for this project holds information for transactions on a British online retail shopping site. The customers are multinational. The transactions occurred between January 12, 2010, and September 12, 2012

Project: New Movie Recommendation

  • Netflix is best known for its huge movie database and the recommendations they provide to their user. Work on a similar recommendation algorithm to stay at par with data scientists of Netflix and similar platforms

Project: Recommendation Engine

  • This is an advanced recommendation system challenge. In this practice problem, you are given the data of programmers and questions that they have previously solved, along with the time that they took to solve that particular question. As a data scientist, the model you build will help online judges to decide the next level of questions to recommend to a user.

About Instructors

Akshat Goel

IIT Delhi, 8y+ experience

An IIT Delhi graduate and an acclaimed software engineer.

He has over 8 years of programming experience and has worked in major companies like DeNA, Japan etc. 

Over the last decade, he has worked with multiple technologies. He has in-depth working experience starting from backend languages like Java, Node.js, PHP, Python, and frontend langauges like AngularJs, React.js and jQuery.

He has been working with Android and Hadoop since last couple of years.

Reviews

FAQ

How do I enroll in the course?

Click on the Enroll Now button on the top of the page. Then select the suitable package for yourself. Then you will be asked to complete the necessary payment. Once you complete the process, you automatically get enrolled for the course.

What are the modes of payments available?

You can make the due payment via netbanking, debit cards, credit cards or online wallet.

Can cash payment be done for courses?

Cash payment facility is not available. Only online transactions are accepted.

What happens after I complete the payment for the course?

You will receive an email confirming the success of subscription and welcoming you to the course. You will be asked to join a learning group on Eckovation corresponding to the course that you have opted.

Can I get a free trial for the course?

It varies from course to course. Some courses have a free trial available, some courses do not have that feature.

Can I pursue the course in laptop as well as mobile?

Yes. You can pursue the course over your laptop by going to www.eckovation.com . Smartphone users can download the eckovation app from appstore or playstore and login to access your course.

What is the Refund Policy in case I'm not satisfied with the Course?

100% Refund Policy is applicable till 7 days after subscription in case we are not providing what we have promised you earlier. However, after 7 days, no request for refund will be entertained.

I am unable to access the online course. Who should I contact?

You can write an e-mail to hi@eckovation.com. You can also contact your course educator or you can call at +91-9266677335.

Is it required for you to complete the course strictly within the course duration mentioned at the top of the page?

No. You can complete the course before or after the stipulated course duration. It is mentioned just to provide a tentative timeline in case you devote 1-2 hours/day to the course regularly. Infact, you'll also have lifetime access to course material.

Is there any Pre-requisite for this Course?

No, there's no pre-requisite for this course. Everything will be covered in the course, right from the scratch.