Opportunities in Data Science.ppt

Opportunities in Data Science
Mr. Swapnil Telrandhe
Sr. Data Scientist
FirstCry(BrainBees Pvt. Ltd.)

Outline
 Data, Big Data and Challenges
 Data Science
 Introduction
 Why Data Science
 Data Scientists
 What do they do?
 Major/Concentration in Data

Data All Around
 Lots of data is being collected
and warehoused
 Web data, e-commerce
 Financial transactions, bank/credit transactions
 Online trading and purchasing
 Social Network

How Much Data Do We have?
 Google processes 20 PB a day (2008)
 Facebook has 60 TB of daily logs
 eBay has 6.5 PB of user data + 50 TB/day
(5/2009)
 1000 genomes project: 200 TB
 Cost of 1 TB of disk: $35
 Time to read 1 TB disk: 3 hrs (100 MB/s)

Big Data
 Big Data is any data that is expensive to manage and
hard to extract value from
 Volume
 The size of the data
 Velocity
 The latency of data processing relative to the growing
demand for interactivity
 Variety and Complexity
 the diversity of sources, formats, quality, structures.

Types of Data We Have
 Relational Data (Tables/Transaction/Legacy
Data)
 Text Data (Web)
 Semi-structured Data (XML)
 Graph Data
 Social Network, Semantic Web (RDF), …
 Streaming Data

What To Do With These Data?
 Aggregation and Statistics
 Data warehousing and OLAP
 Indexing, Searching, and Querying
 Keyword based search
 Pattern matching (XML/RDF)
 Knowledge discovery
 Data Mining
 Statistical Modeling

DATA SCIENCE V/S BIG DATA
 They are not the “same thing”
 Big data = crude oil
Big data is about extracting “crude
oil”, transporting it in “mega
tankers”,shipping it through
“pipelines”, and storing it in
“massive silos”

What is Data Science?
 An area that manages, manipulates, extracts,
and interprets knowledge from tremendous
amount of data
 Data science (DS) is a multidisciplinary field of
study with goal to address the challenges in big
data
 Data science principles apply to all data – big
and small

What is Data Science?
 Theories and techniques from many fields and
disciplines are used to investigate and analyze a large
amount of data to help decision makers in many
industries such as science, engineering, economics,
politics, finance, and education
 Computer Science
 Pattern recognition, visualization, data warehousing, High
performance computing, Databases, AI
 Mathematics
 Mathematical Modeling
 Statistics

Real Life Examples
 Companies learn your secrets, shopping
patterns, and preferences
 For example, can we know if a woman is pregnant,
even if she doesn’t want us to know?
 Data Science and election (2008, 2012)
 1 million people installed the Obama Facebook app
that gave access to info on “friends”

What do Data Scientists do?
 National Security
 Cyber Security
 Business Analytics
 Engineering
 Healthcare
 And more ….

Concentration in Data Science
 Mathematics and Applied Mathematics
 Applied Statistics/Data Analysis
 Solid Programming Skills (R, Python, SQL)
 Data Mining
 Data Base Storage and Management
 Machine Learning and discovery

Opportunities in Data Science.ppt

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