data scientist enablement roadmap 1.0

Data Scientist Enablement Roadmap

Advanced Center of ExcellenceModern Renaissance CorporationIn Collaboration with SONO team and others

We thank our community of committed and passionate volunteers, experts, educators, innovators, benefactors, advisers, advocates and supportersWe are also grateful to the outstanding support and encouragement from SONO team as well as other organizations like Open Courseware Consortium, MIT, IBM, HortonWorks, Stanford University, and Caltech etc.

Acknowledgement

Principles

Philanthropy through Free and Open Education, Knowledge Dissemination and Social Innovation

Synthesize Data Science, Big Data Architecture, Technology Platforms and Systems Engineering for Decision Making

Collaboration, Crowdsourcing and Innovation Diffusion

Emphasis on Knowledge, Skills and Abilities (KSAs) over Abstract Mathematics or Theoretical Profundity

Principles without programs are platitudes.- George Bernard Shaw

Industry needs Data Scientists with versatile background in Machine Learning, Statistics, Big Data Architecture, Advanced Analytics, Evidence-Oriented Systems Engineering.

Motivation

Aspiring Data Scientists, Big Data Engineers also need well-rounded education, mentorship from experts as well as practical skills

Goals

Prepare the students, practitioners to have set of T-shaped practical-skills emphasizing depth and breadth of a range of relevant disciplines and capabilities in Data/Decision Sciences and Big Data Architecture/Engineering.

Make the course delivery easy, engaging and engendering.

Data Science Enablement Roadmap - 2014

1 + 3 Courses gets you Master’s Level Certificate

Fast track toData Science

Ramping up Machine Learning with R

Modern Data Platforms

Advanced Techniques inBig Data Analytics

Data Science Enablement Roadmap - Future

Possible extensions in future

Fast track toData Science

Ramping with R

Modern Data Platforms

Data Mining Process Methodologies and Tools

Machine Learning/AI

Advanced Techniques inBig Data Analytics

Data Visualization

Introductory course with NO pre requisites. Topics include Algorithms, Statistical Inference, Data Analysis, Model Building, Validation, Calibration,Data at rest and in motion, Causality, Meaning of Data, Data Engineering, Hadoop, R, Machine Learning,Data Mining, Visualization, Applications, Case Studies, variety of tools and techniques etc.

Fast track to Data Science (DSE 400)

Prerequisite: DSE 400Applied Statistics, Machine Learning, Data Mining, Graphing, Analytics and VisualizationUse cases, Industry Applications

Ramping up with R (DSE 501)

Prerequisite: DSE 400Employ Hadoop and Hadoop Ecosystem to enable Enterprises handle data explosion and derive actionable analytics.MapReduce, Pig, Hive, NoSQL, Zookeeper etc.Also introduce streams computing with Storm and KafkaCase Studies: Fraud Prevention, Product Recommendation, Epidemic Prediction etc.

Modern Data Platforms (DSE 502)

Modern Data Platforms (Contd ...)

Prerequisite: DSE 400Explore and implement Machine Learning Algorithms such as Classification, Clustering, Ranking, Recommendation, Neural Networks, Adaptive Learning.

Also to include Knowledge Engineering, Expert Systems, Ontologies, NLP and Reasoning

Machine Learning and AI (DSE 503)

Prerequisite: DSE 400Decision TreesRegressionClassificationClusteringAssociation Rules etc.

Data Mining Process and Methodologies (DSE 504)

Prerequisite: DSE 400Story Telling/Data JournalismData Visualization MethodologyTools and TechniquesHTML5, d3.js, BIRT, Prefuse

Data Visualization (DSE 505)

Prerequisite: DSE 400On Demand Data IntegrationData VirtualizationEnterprise Data HubAnalytics DashboardsBig Data AppliancesAnalytics as a ServiceOpen Stack and SavannahPrivacy and Security

Advanced Techniques for Big Data Analytics ( DSE 600)

Advanced Techniques for Big Data Analytics ( contd ...)

DSE 2014 stream is set to commence on Jan 19, 2004For more details, visit DSE 400 Announcement Page <http://bit.ly/18zPE1j>To Enroll for DSE 400 visit Enrollment Page

<http://soknocommunity.com/xoops/modules/xforms/?form_id=2>This presentation can also accessed at

Data Scientist Enrollment Roadmap 1.0 <http://bit.ly/1c2wC2P>We welcome thoughts and suggestions. Write to us at <datascience400@gmail.com>

Next Steps

Data Jujitsu - The Art of Turning Data into a Product by DJ PatelData Science eBook Dr. Vincent GarvilleData Scientist - Sexiest Job of 21 Century (HBR)Doing Data Science by Rachel ShuttData Visualization: a successful design process by Andy KirkDisruptive Possibilities: How Big Data Changes Everything by Jeffrey NeedhamHow to process, analyze and Visualize Data (MIT OCW) Knowledge-based Systems (MIT OCW) Learning from Data (Caltech)Statistical Thinking and Data Analysis (MIT OCW) The Complete Guide to Business Analytics (Collection) By: Thomas H. DavenportThink Bayes; Think Python; and Think Stats by Aleen Downey

References

Learn from the masters - Johann Wolfgang von Goethe

Google White papers on GFS, MapReduce, BigtableReal-time Analytics with Big Data - Facebook Case StudyInteractive Data Visualization by Scott MurrayData, Models and Decisions (MIT OCW)Communicating Data (MIT Sloan School of Management OCW)Unleashing the Power of Hadoop - DBTA Thought Leadership SeriesHow Educators Can Narrow Big Data Skills Gap - Jeff Bertloucci, Data Visualization with D3.js CookbookWhat is Data Science?Agile Data Science

References (contd …)

Thank You