Data Science Training | Data Science Tutorial for Beginners | Data Science with R | Edureka

DATA SCIENCE CERTIFICATION TRAINING https://www.edureka.co/data-science

430$ Billion

430$ Billion
Sexiest Job

430$ Billion
Sexiest Job
Best Job 2016 & 2017

430$ Billion
Sexiest Job
Best Job 2016 & 2017
Avg Salary $150k

Data Science Training

Today’s Training Topics
❖ Data Scientist and Various Paths
❖ Data Science Training at Edureka
❖ What is Data Science?
❖ Data Science Use Cases
❖ Data Science Projects at Edureka
❖ Data Science Demo

So Many Options

Languages & Frameworks

Data Scientist Paths

DATA SCIENCE
@

Data science, also known as data-driven science, is an
interdisciplinary field about scientific methods, processes, and
systems to extract knowledge or insights from data in various
forms, either structured or unstructured.
It is the study of where information comes from, what it
represents and how it can be turned into a valuable resource in
the creation of business and IT strategies.
What is Data Science?

Statistics
Data Science Peripherals

Statistics Prog Languages

Statistics Prog Languages Software

Statistics Prog Languages Software Machine Learning

Statistics Prog Languages Software Machine Learning Big Data

It’s not a New Thing
Statistics

Statistics
Artificial Intelligence

Statistics
Business Intelligence

Statistics
Business Intelligence
Data Analytics

Use of Data Then

Data Preparation
& Cleaning
65%
Analysis & Insights
10%
Strategizing
10%
Modeling &
Iteration
10%
Making
Decision 5%
Roles of Data Analyst - Then

Traditional BI Tools
Challenges

ERA OF DATA SCIENCE

Sales, Data Preparation
& Cleaning,
65% 10%
Sales, Analysis &
Insights,
10% 25%
Strategizing
10% 20%
Modeling &
Iteration
10% 20%
Making
Decision
5% 25%
Roles of Data Scientist - Now

Recommendation Engines Object Detection
Data Science – Use Cases

Voice Assistants Sentimental Analysis
Data Science – Use Cases

Data Acquisition
Data Science Life Cycle

Data Acquisition Data Preparation

Hypothesis &
Modelling

Hypothesis &
Modelling
Evaluation &
Interpretation

Hypothesis &
Modelling
Deployment
Evaluation &
Interpretation

Hypothesis &
Modelling
Deployment
Operation &
Optimization
Evaluation &
Interpretation

Projects @edureka!
The goal of this project is to explore the movie
dataset, given the parameters like: "duration", "movie
title", "gross collection", "budget", "title year", etc.
Movies Collection
The Use-Case scenario is to create recommendations
for customers of a grocery store based upon historical
transaction data.
Recommendation System for Grocery Store

Projects @edureka!
This project focuses on social media analytics. The
problem can be defined as Measuring, Analyzing, and
Interpreting interactions and associations between
people, topics and ideas
Twitter Analysis
This project is about analyzing the data and applying
time series model to forecast the number of bookings
an Airline firm can expect each month
Air Passengers Forecasting

Hi I am john, I need some
baseline for pricing my Villas
and Independent Houses
Real Estate Company Use Case

4
5
6
1
2
3
Data Acquisition / Collection

4
5
6
1
2
3
Data Preparation

4
5
6
1
2
3
Hypothesis and Modelling
F(x) Price
No of bedrooms
Criminal rate
Pollution
Population

4
5
6
1
2
3
Evaluation and Interpretation
F(x)Test set Accurate?

4
5
6
1
2
3
Deployment
Spark Java
F(x) F(x)

4
5
6
1
2
3
Operation and Optimization
New factor from data set
(E.g. Educational institutions)
F(x)
Y(x) Accurate?

Dataset Description
Column Description
CRIM per capita crime rate by town
ZN proportion of residential land zoned for lots over 25,000 sq.ft.
INDUS proportion of non-retail business acres per town.
CHAS Charles River dummy variable (1 if tract bounds river; 0 otherwise)
NOX nitric oxides concentration (parts per 10 million)
RM average number of rooms per dwelling
AGE proportion of owner-occupied units built prior to 1940
DIS weighted distances to five Boston employment centres
RAD index of accessibility to radial highways
TAX full-value property-tax rate per $10,000
PTRATIO pupil-teacher ratio by town
B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
LSTAT % lower status of the population
MEDV Median value of owner-occupied homes in $1000's

Data Science Training | Data Science Tutorial for Beginners | Data Science with R | Edureka

Data Science Training | Data Science Tutorial for Beginners | Data Science with R | Edureka

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Data Science Training | Data Science Tutorial for Beginners | Data Science with R | Edureka