data science program 2014.pdf

EduPristine For [Data Science] EduPristine www.edupristine.com

Data Science Course Catalogue

EduPristine For [Data Science] 1

EduPristine For [Data Science]

Objective

Data allows us to gain important insights and make useful predictions, helping individuals, organizations, and businesses devise strategy and drive decision-making on everything from elections and product design to marketing and finance.

Demonstrate your ability to solve problems, contribute insights, and offer solutions by earning a Certificate in data science.

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Is this right for me?

This professional certificate course is for individuals currently working in or aiming to deepen their

skills and expertise for these types of roles:

Data scientist

Business analyst

Information architect

Information technology manager

Information systems manager

Predictive analytics developer

Big Data Engineer

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Program Benefits.

The information about the real-world benefits of Big Data Analytics (Understand the science of examining big data to draw conclusions about past trends or predict future trends)

Examine the strengths and weakness of analytics software programs commonly used in machine learning and artificial intelligence

Skills and information needed to successfully implement Big Data Analytics projects

A Data Science point of view for using analytics to derive business insights from Big Data

Communicate trends and predictions appropriately to different audiences.

Data Visualization concepts.

Hadoop tools and techniques.

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Pre-requisites

Pre-requisites for the course:

To understand the content, derive value, and successfully complete this course, you should have

Basic Statistics methods used in business performance measures.

Strong interest in data science.

Some programming experience. ( In any language).

Individuals with a bachelors degree in engineering, science, math/statistics, finance, computer science, accounting or marketing who enjoy statistical and analytical thinking may excel in this field. Applicants should have completed at least one undergraduate course in statistics, with an undergraduate GPA of 3.0 or higher.

Suggested Readings

We recommend that students refer to the book

Mining of Massive Datasets by Anand Rajaraman and Jeff Ullman

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Any Industry today is a Big Data Industry- and if you want to be one among them, a Data Science Course is a must

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So, what do you do as a Data Scientist?

The Data Scientist is someone who helps and advises the project/cruise Principal Scientist and researchers to document their data sets so that they are properly described

The DS also interacts with PIs and Data Specialists to calibrate, validate, save and archive data.

And for better understanding, meet 2 of our Data Scientists Ray and Rahul

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Hi, I am Ray, I am a data scientist,

I develop machine learning models for detecting fraud & abuse across Yahoo ecosystem.

I worked on prototyping machine learning solutions for mining text chats and web logs to drive intuitive consumer experience.

I have worked on variety of ML projects:

Predicting stages of text chats (using sequence based classier)

Algorithm to predict potential sale converts in text chats for retargeting campaigns

Developed a Python module that makes dealing with CSVs easier.

A framework for handling text chats in Python.

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I am, Rahul - Senior Data Scientist at Twitter

My missions are :-

1. To build smart systems to be directly used in products as well as to help decision making.

2. To answer very hard product related questions via extracting insights through data analysis.

My Daily Job Includes:

Accessing data through Hadoop MapReduce jobs written in Cascading or Pig (sometimes UDFs in Java).

Fitting statistical and machine learning models in R locally, or sometimes directly on Hadoop clusters.

Feature extraction from all kind of Twitter data: social graph, client history, user behavior, tweets etc.

Answering product related questions to extract insights, specify metrics to follow via data analysis.

Presenting results to both engineering and non-engineering teams

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Data Science- Course Modules

Module I - Business Analytics (50Hrs Classroom Session)

Introduction and Data Analytics

Linear Regression

Logistic Regression

Decision Tree and Clustering

Time Series Modeling

Logistic Regression

Market Basket Analysis

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Module II - Case studies (20Hrs Classroom Session)

Case Synopsis

Cross Sell Model Propensity to Cross sell health insurance products to general insurance customers.

Market Mix Modeling Optimization of the promotion expense using Market mix modeling

Churn Analytics Developing a churn model to gauge the propensity of attrition among loyal and profitable customer segment.

Buy Till You Die Model Predicting the future number of transactions a customer will make, thereby calculating the value of the customer in his/her lifetime.

Customer Lifetime Value Analysis Predicting the customer survival along with the profitability to model the life time value of each customer

Telecom Model to Estimate Bill Building a model that can suggest right tariff plan based on estimated bill amount

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Module III - Data Visualization (20Hrs Classroom Session)

Introduction

The visualization design methodology

The Data Visualization Process

Working with Single Data Sources

Using Multiple Data Source

Using Calculations in Tableau

Comparing Measures Against a Goal

Tableau Geo coding, Advanced Mapping

Showing Distributions of Data

Statistics and Forecasting

Dashboard Best Practices

Sharing Your Work

Case Study

Exam/Exam Preparation

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Module IV - Big Data & Hadoop (50Hrs Classroom Session)

Basic Java & Introduction to Hadoop Technology.

Introduction to Unix and Basics of Hadoop

Introduction To Hadoop Distributed File System (HDFS).

Understanding Pseudo Cluster Environment

Understanding - Map-Reduce Basics and Map-Reduce Types and Formats

HIVE

PIG

SQOOP / ZOOKEEPER

Live Project I

HBASE

Live Project II

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Module V - Cloudera preparation (20Hrs Classroom Session)

Hadoop: basic concepts and HDFS

Introduction to MapReduce

Hadoop clusters and Hadoop Ecosystems

Writing a MapReduce program in java

Deeper into Hadoop api

Practical development tools and kits

Partitioner and reducers

Data input and output

Complex MapReduce algorithms

Joining data sets in MapReduce

Sqoop

Hive, Impala, PIG

Oozie

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What We Offer In Data Science

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CERTIFICATION

Cloudera Certification

Fees included

Business Analytics Certification from

EduPristine

Global certification- Tableau Global certification

fees included

Big Data and Hadoop certification from

Edupristine

Data Science from EduPristine

PLACEMENT

Data Scientist C.V Preparation

Placement Assistance

for 1 year

LIVE PROJECT & ASSINGMENTS

Big Data Technology & Tools

Different Analytics Concepts

Data Visualization Project

Data Scientist Project.


What We Offer In Data Science

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CLASSROOM

Weekend Class

Room Sessions

Cloudera Exam

Preparation Session

CV Preparation

Project work

MATERIAL

Relevant Hand Books

for Each Module

Assignments & cases

Hadoop Definitive guide

Online Content

Subject Wise Video Recordings

ONLINE LIVE TRAINING

Brush up session on each Module

Refer Online Videos while practicing on different

tools


What We Offer In Data Science.Contd

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LAB

Hand on Experience on R

Hand on Experience on SAS Language

Hand On Experience on HADOOP Environment

Hand on Experience on Tableau

ONLINE LIVE TRAINING

Brush up session on each Module

Refer Online Videos while practicing on different tools

EduPristine For [Data Science] 18

Total Fees for Data Science 95,000

Credit card- 6 installment facility available*

AVAIL SCHOLARSHIP BENEFITS.45


Thank you!

Contact:

EduPristine www.edupristine.com

EduPristine

702, Raaj Chambers, Old Nagardas Road, Andheri (E),

Mumbai-400 069. INDIA

www.edupristine.com

Ph. +91 22 4211 7474 / 8879986678

data science program 2014.pdf

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