Educational Opportunities in
Big Data
Could current Big Gaps in Talent fill the void and Big Market Demand?
Dr. KRS Murthy
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.
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……….!
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
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
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
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
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
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
Storing Big Data
• Analyzing your data
characteristics
• Selecting data sources for
analysis
• Eliminating redundant data
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
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
Big Data Ad Hoc Query
• Performing ad hoc Big Data querying with Hive
• Persisting data in the Hive MegaStore • Performing queries with HiveQL
Business Value
• Creating business value from extracted data
• Mining data with Mahout
• Visualizing processed results with reporting tools
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
