Introduction to Data Science

(Analytics Tools)Dr. Rodolfo C. Raga JR.

Angeles University Foundation

June 28 - 30, 2018

Topic Outline

• What is Data Science

• Data Science -- Why all the excitement?

• Why Learn Data Science

• 5 V’s of Big Data

• Types of Data

Data Science – A Definition

Data Science is the science which uses computer science, statistics and machine learning, visualization and human-computer interactions to collect, clean, integrate, analyze, visualize, interact with data to create data products.

3

Data Analysis is not a new concept!

R.A. Fisher

Howard

Dresner

Peter LuhnW.E.

Deming

Why all the excitement about Data?

• The amount of data produced in the world exceeds 2.5 Exabyte a day;

• Communication capabilities increase at a rate of over 30% per year and the amount of information collected increases by 20% annually.

• The number of data-based applications is large and continuously growing as is the range of fields in which they are applied—medicine, finance, urban planning, smart cities and more.

Why Learn Data Science?

Data is the new Oil.. literally

Source: World Economic Forum

Data is the new Oil.. literally

Source: World Economic Forum

Data Science and Engineering is now a new

Course

What we can do with Data Science…

. . . predict the buying behavior and decision criteria of your prospects weeks before your competition?

. . . gain first-mover advantage by introducing new products and services to micro-segments that haven't been identified by competitors?

. . . evaluate the impact of your marketing campaigns hourly and make adjustments in real-time?

. . . improve customer experience scores that grow products per customer, reduce attrition, and leverage the power of customer recommendations for new business?

. . . predict likely failures of critical equipment and processes?

More Industry Adopting Data Science

Data Analytics vs. Statistical Analysis

Statistical AnalysisUtilizes statistical and/or mathematical techniques Used based on theoretical foundationSeeks to identify a significant level to address hypotheses or RQs

Data AnalyticsUtilizes data mining techniquesIdentifies inexplicable or novel relationships/trendsSeeks to visualize the data to allow the observation of relationships/trends

5 Vs of Big Data

• Raw Data: Volume

• Change over time: Velocity

• Data types: Variety

• Data Quality: Veracity

• Information for Decision Making: Value

Data Volume

Data Velocity

Data Variety

Data Veracity

The Data Science Business Advantage

Data Warehouse vs. Data Lake

CS 561, Lecture 1

DATA SCIENCE ANALYTICS

DATA SCIENCE ANALYTICS

Probabil

ity

based

Rule

based

Past Future

Some Definitions

• Data: The facts and figures collected, analyzed, and summarized for presentation and interpretation

• Variable: A characteristic or a quantity of interest that can take on different values

• Observation: Set of values corresponding to a set of variables

• Variation: The difference in a variable measured over observations

• Random variable/uncertain variable: A quantity whose values are not known with certainty

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Types of Data

Categorical

Defined categories

Non-numeric, but could be represented in a database as numbers

Boolean variables are types of

categorical data

Examples: marital status, political party, eye color, profession, location

Numeric

Discrete

Counted items

Whole numbers

Examples: no.of children, no.of handsets

Continuous

Measured characteristics

Can take on manydifferent values

Examples: height, weight, speed, bank balances

Data Quality Predictors

1. Are the data attributes Relevant?

2. Are the data attributes Connected?

3. Are the data attributes Accurate?

4. Is there enough Data?

Data Quality Predictors

1. Are the data attributes Relevant?

Data Quality Predictors

2. Are the data attributes Connected?

Data Quality Predictors

3. Are the data attributes Accurate?

Accuracy refers to the closeness of a measured value to

a standard or known value.

Precision refers to the closeness of two or more

measurements to each other

Precision is independent of accuracy and vice-versa. Data

can be very precise but inaccurate. It can also be accurate

but imprecise.

Data Quality Predictors

3. Are the data attributes Accurate?

Data Quality Predictors

4. Is there enough Data?