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Educational Opportunities in
Big Data
Could current Big Gaps in Talent fill the
void and Big Market Demand?
Dr. KRS Murthy [email protected]
(408)-464-3333
Big Gaps in Big Data Talent
McKinsey Global Institute has projected
that by 2018, the United States alone
could face a shortage of as many as
190,000 people with deep analytical
skills.
Means Data Scientists
Worldwide Big Gap in Big Data Talent
• The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise
• 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.
• Gaps in Qualified & Experienced Big Data Educators
• Serious gap in Big Data Strategists at SMB and large companies, Federal and State Governments in USA, Canada, EU, Asia, South America, Africa and even Australia & NZ.
• Global – 5 to 10 times these numbers – 7.5M to 15M
India - IIMs, IITs, NIT, NIIT
offer big data programs • Nasscom has created an analytics interest group
(comprising about 18 to 19 companies) to help define core competencies and provide training.
• August 2014 - Nasscom points out, “We are in the process of designing common content for training professionals and students in big data and analytics.
• Besides conducting workshops in Bangalore and Hyderabad, we are collaborating with companies and academicians to draft up a common data curriculum.
• It will take us around 18 to 24 months to create a roadmap for this.” – Slow……………….!
India - Shiv Nadar University
China – Big Data Programs • Big Data Analytics Master Program -May 2014
• Renmin University
• The Big Data Analytics Master Program and Innovation Platform
• Five colleges, including Renmin University of China, Peking University, University of Chinese Academy of Sciences, Central University of Finance and Economics, Capital University of Economics and Business
• Signed the cooperation agreement with the government and the industry
• Fifty people are anticipated to enter the first term.
• The courses began in the fall semester.
United Kingdom – Big Data
• University of Essex MSc Big Data Program
• Data Science - University of Glasgow
• Data Science and Analytics MSc | Brunel
University London
• Brunel University London
• Sheffield Hallam University
• Big Data – University of Stirling
• Scotland UK - Datatechnology, advanced
analytics and industrial and scientific
applications.
Type of Expertise Needed • Technical Expertise is needed to bring NoSQL databases or
Hadoop clusters into production.
• Data Expertise is needed to take advantage of data mining, text mining, forecasting and machine learning techniques.
• Strategic Expertise is needed for corporate, industry vertical state or national level strategy
• Marketing and Sales Perspectives are need for Sales and Marketing Professionals
• Big Data Architects need technical, data and system solution level expertise
• Project and Functional Management of Big Data Projects requires overall understanding not necessarily hands-on expertise.
• CIO, CTO, Chief Big Data Officer, Chief Security Officer, CFO COO and CEO require different levels of technology and business understanding and expertise.
Training your current team for Big
Data • Technical Expertise: your existing DBAs,
database developers & data-warehousing
pros could learn new tricks
• Moving from a conventional database to a
massively parallel processing (MPP)
database platform is not a huge leap for
your talented DBA
• The right person will be energized by the
new challenge
Big Data Platform Vendor Courses
• All big data platform vendors offer courses
• The vendors also let you play in a sandbox by
downloading their big data platforms
• Online & hands-on programs could be
complemented
• Many private companies offer corporate and
individual training
• Market & Vertical Specific Domain Expertise is
as important as generic courses
• On the job additional training is imperative
Big Data Courses & Degrees
1. All of these Big Data Courses, Workshops,
Certificate & Degree programs are geared
to candidates who already have
undergraduate degrees
2. Most favor professionals with three or
more years of work experience.
3. In many cases part-time options are
available, so students can continue to work
as they learn more about big data analytics.
University Programs in Big Data
• Columbia has its Institute for Data
Sciences
• Harvard has its Institute for Applied
Computational Science
• University of California, Berkeley has
its AMPLab (which explores the role
of algorithms, machines and people in
big data analytics)
Analytics in Business Schools
• More than half of these schools are offering fairly new masters programs in business analytics.
• These tend to be interdisciplinary degrees sponsored by schools of business.
• In some cases it's an MBA degree with a specialization in analytics and information management
• New York University and Rutgers
Business Meets Analytics
• Business meets Analytics program
that can be completed in one year or
less
• North Carolina State University
• Drexel University
• Louisiana State University
• Canada's York University
Statistics & Operations Research
• Applied learning
• Business and big data oriented
programs
• University of Cincinnati
• University of Tennessee
Big Data Application to Marketing
• Big Data Analytics as applied to marketing
• Bentley University
• DePaul University
• Insurance & Financial Services verticals
• University of Illinois at Urbana-
Champaign, where State Farm has a
research center that offers tuition
assistance and internship opportunities
Murthy’s Ideas for Big Data Courses & Degrees
• Big Data – Privacy, Security
• Big Data – Role of Memory
• Big Data – Role of Networking
• Big Data – Servers
• Big Data – Educational Tools
• Big Data – Data Science
• Big Data – Visualization
• Big Data – Investments – Venture, Private Equity and Equipment Lease Financing
• Big Data for HR and Recruiting Professionals
• Big Data for C and VP Levels
• Big Data for Sales & Marketing Professionals
• Big Data in Banking, Retail, Hospitality, Blue Economy, Energy
• Big Data in Infrastructures – Wireless Sensor Networks, IOT or IOET
• Big Data – Standards
• How to teach Big Data – University, College, Vocational Schools, K-12
• Hardware for Big Data – Installation, Operation and M aintenance
Introduction to Big Data
• Defining Big Data
• The four dimensions of Big Data: volume, velocity, variety, veracity
• Introducing the Storage, Map-Reduce and Query Stack
• Delivering business benefit from Big Data
• Establishing the business importance of Big Data
• Addressing the challenge of extracting useful data
• Integrating Big Data with traditional data
Storing Big Data
• Analyzing your data
characteristics
• Selecting data sources for
analysis
• Eliminating redundant data
• Establishing the role of NoSQL
Overview of Big Data stores • Data models: key value, graph, document, column-
family
• Hadoop Distributed File System
• HBase
• Hive
• Cassandra
• Hypertable
• Amazon S3
• BigTable
• DynamoDB
• MongoDB
• Redis
• Riak
• Neo4J
Selecting Big Data stores
• Choosing the correct data stores
based on your data characteristics
• Moving code to data
• Implementing polyglot data store
solutions
• Aligning business goals to the
appropriate data store
Processing Big Data
• Integrating disparate data stores
• Mapping data to the programming
framework
• Connecting and extracting data
from storage
• Transforming data for processing
• Subdividing data in preparation for
Hadoop Map-Reduce
Employing Hadoop MapReduce
• Creating the components of Hadoop
Map-Reduce jobs
• Distributing data processing across
server farms
• Executing Hadoop Map-Reduce jobs
• Monitoring the progress of job flows
Hadoop Map - Reduce
• The building blocks of Hadoop Map-Reduce
• Distinguishing Hadoop daemons
• Investigating the Hadoop Distributed File System (HDFS)
• Selecting appropriate execution modes: local, pseudo-distributed and fully distributed
Streaming Data
• Handling streaming data
• Comparing real-time processing
models
• Leveraging Storm to extract live
events
• Lightning-fast processing with Spark
and Shark
Analyzing Tools & Techniques
• Tools and Techniques to Analyze Big
Data
• Abstracting Hadoop Map-Reduce jobs
with Pig
• Communicating with Hadoop in Pig Latin
• Executing commands using the Grunt
Shell
• Streamlining high-level processing
Big Data Ad Hoc Query
• Performing ad hoc Big Data querying
with Hive
• Persisting data in the Hive MegaStore
• Performing queries with HiveQL
• Investigating Hive file formats
Business Value
• Creating business value from
extracted data
• Mining data with Mahout
• Visualizing processed results with
reporting tools
• Querying in real time with Impala
Big Data Strategy
• Developing a Big Data Strategy
• Defining a Big Data strategy for your
organization
• Establishing your Big Data needs
• Meeting business goals with timely
data
• Evaluating commercial Big Data tools
• Managing organizational expectations
Data Analytics with Business Focus
• Enabling analytic innovation
• Focusing on business importance
• Framing the problem
• Selecting the correct tools
• Achieving timely results
Big Data Solution Implementation
• Implementing a Big Data Solution
• Selecting suitable vendors and
hosting options
• Balancing costs against business
value
• Keeping ahead of the curve
IBM E Book
Big Data University courses
Database (DB2) category
