Data Science And Big Data Analytics Course

Transcription

1 Data Science And Big Data Analytics Course Copyright 2014 EMC Corpora3on. All Rights Reserved. Introduc3on and Course Agenda 1

2 Introduc3on and Course Agenda 2

3 Introduc3on and Course Agenda The following topics are covered in this module: Overall course goal, objec3ves, and high- level flow Intended audience and expected background Classroom and lab environments Introduc3on and Course Agenda 3

4 Overall Course Goal The goal of the Data Science And Big Data Analy0cs Course is for you to be able to immediately par,cipate as a Data Science team member on big data and other analy0cs projects! Data Scien0st p- o- v! Open! Prac0cal Copyright 2014 EMC Corpora3on. All Rights Reserved. Introduc3on and Course Agenda 4

5 Intended Audience Individuals seeking to develop an understanding of Data Science from the perspec3ve of a prac3cing Data Scien3st:! Managers of teams of business intelligence, analy3cs, and big data professionals! Current business and data analysts looking to add big data analy3cs to their skills! Data and database professionals looking to exploit their analy3c skills in a big data environment! Recent college graduates and graduate students looking to move into the world of data science and big data! Individuals seeking to take advantage of the EMC Proven Professional Data Scien3st Associate (EMCDSA) cer3fica3on Introduc3on and Course Agenda 5

6 Expected Background Strong mathema3cal, quan3ta3ve capability Experience with sta3s3cal methods and basic proficiency with a sta3s3cal sowware package, such as R or RStudio, Minitab, Matlab, SAS, or SPSS Experience with the condi3oning and management of business data including databases Basic programming skills, preferably including SQL Introduc3on and Course Agenda 6

7 Course Objectives Upon comple3on of this course, you should be able to: Immediately par3cipate and contribute as a data science team member on big data and other analy3cs projects by:! Deploy a structured lifecycle approach to data science and big data analy3cs projects! Reframe a business challenge as an analy3cs challenge! Apply analy3c techniques and tools to analyze big data, create sta3s3cal models, and iden3fy insights that can lead to ac3onable results! Select op3mal visualiza3on techniques to clearly communicate analy3c insights to business sponsors and others! Use tools such as R and RStudio, MapReduce/Hadoop, in- database analy3cs, and window and MADlib func3ons Explain how advanced analy3cs can be leveraged to create compe33ve advantage and how the data scien3st role and skills differ from those of a tradi3onal business intelligence analyst Introduc3on and Course Agenda 7

8 Please Briefly Introduce Yourself Name Company Work Loca3on Role, and how analy3cs relates to it Analy3cs Exper3se What you would like to achieve as a result of adending this course Introduc3on and Course Agenda 8

9 Course Modules and Navigation Icons Data Science and Big Data Analytics 1. Introduction to Big Data Analytics 2. Data Analytics Lifecycle + Lab 3. Review of Basic Data Analytics Methods Using R + Labs 4. Advanced Analytics - Theory & Methods + Labs 5. Advanced Analytics - Technology & Tools + Labs 6. The Endgame, or Putting it All Together + Final Lab Introduc3on and Course Agenda 9

10 Topics : Data Science and Big Data Analytics Course Introduction to Big Data Analytics + Data Analytics Lifecycle Big Data Overview State of the Practice in Analytics The Data Scientist Big Data Analytics in Industry Verticals Data Analytics Lifecycle Review of Basic Data Analytic Methods Using R Using R to Look at Data - Introduction to R Analyzing and Exploring the Data Statistics for Model Building and Evaluation Advanced Analytics Theory and Methods K-means Clustering Association Rules Linear Regression Logistic Regression Naive Bayesian Classifier Decision Trees Time Series Analysis Text Analysis Advanced Analytics - Technology and Tools Analytics for Unstructured Data (MapReduce and Hadoop) The Hadoop Ecosystem In-database Analytics SQL Essentials Advanced SQL and MADlib for Indatabase Analytics The Endgame, or Putting it All Together + Final Lab on Big Data Analytics Operationalizing an Analytics Project Creating the Final Deliverables Data Visualization Techniques + Final Lab Application of the Data Analytics Lifecycle to a Big Data Analytics Challenge Introduc3on and Course Agenda 10

11 The Classroom Environment Loca3ons! Restrooms! Cafeteria coffee! Network / Phone Access! Smoking Area! First Aid! Water cooler - coffee Hours of Class! 8:30am 5pm each days! Lunch typically 12:00! Approximately 60% lecture, 40% lab Introduc3on and Course Agenda 11

12 The Lab Environment Hardware:! VMWare Servers! Individual Virtual Machines SoWware Open Source:! Data stored in Greenplum Community Edi3on Database (GPDB)! Access from desktop browsers " MicrosoW & Apple Mac! Analy3cs via: " Rstudio, R " PSQL interface for GPDB " Hadoop " MADlib RStudio Introduc3on and Course Agenda 12

13 Course Materials Student Reference Guide:! Lecture slides! Appendix: " References " Quick reference guides LINUX PSQL R Student Lab Guide:! Lab instruc3ons Introduc3on and Course Agenda 13

14 Classroom Etiquette Limit usage of personal electronic devices! Cell phones/pdas (set to vibrate if possible)! Laptops (preferably closed during lecture)! If your phone rings, answer it as you step out of the classroom Food and drink are allowed in classroom Inform the instructor (and lab partner, if you have one) of all absences from classroom sessions! Excessive absences will be interpreted as non- adendance at the class Although we encourage collabora3on during the class, please treat the data files, code and lab as intellectual property of EMC Educa,on Services.! Please do not redistribute without the consent of EMC Educa3on Services Copyright 2014 EMC Corpora3on. All Rights Reserved. Introduc3on and Course Agenda 14