Lecture I: Course Overview, Intro to Data Science, and R Data Science for Business Analytics

Thibault Vatter Department of Statistics, Columbia University and HEC Lausanne, UNIL

26.02.2018

Outline

1 Course overview

2 Intro to data science

3 R

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A little about me Born and raised in Geneva Education: I I I

B.Sc. Physics (EPFL, ’10) M.Sc. Physics with minor in Financial Engineering (EPFL, ’12) Ph.D. Statistics (HEC Lausanne, ’16)

Worked a bit as a quant in finance Currently: I I

Post-doctoral fellow at Columbia University Live in New York city

Hobbies: I I I

T. Vatter

Flying planes Watching bay area teams (go 49ers and Warriors!) Beers (formerly at Satellite, now in Brooklyn micro-breweries) Lecture I: Course Overview, Intro to Data Science, and R

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The basics Every two weeks: 02.26/03.12/03.26/04.09/04.30/05.14/05.28 Lectures: I

focus on introducing the concepts

I

8:15-9:00am/9:15-10:00am + 1:15-3:00pm/3:15-4:00pm

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classroom 237, Internef building

Exercise sessions: I

focus on the assignments and project

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10:15-11:00am/11:15-12:00pm + 3:15-4:00pm/4:15-5:00pm

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lab room 143, Internef building

TA: Natasha Tagasovska, [email protected]

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Grading 4 assignments (50%) and one project (50%) I I

Detailed reports for each assignment and final project Presentation during last lecture for the project

Final grade I

According to P4 GRADE =

I I

i=1

HWi · 12.5 + PR · 50 100

HWi for i = {1, 2, 3, 4} and PR are from 0 to 100 GRADE will then be adjusted from 1 to 6

Groups of 1 or 2 members I I I

Email to Natasha with the group members One email per group is enough Deadline for group registration is March 19

Grades based on academic performance only! T. Vatter

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Learning outcomes Manage and analyze data Develop data products Use data science in a business context

source: r4ds.had.co.nz

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Lectures Date 02.26 02.26 03.12 03.12 03.26 03.26 04.09 04.09 04.30 04.30 05.14 05.14 05.28 05.28 T. Vatter

(am) (pm) (am) (pm) (am) (pm) (am) (pm) (am) (pm) (am) (pm) (am) (pm)

Topic Intro R workflow and RMarkdown Wrangling (I) Visualization (I) Wrangling (II) Visualization (II) Modeling (I) Modeling (II) Shiny Guest presentation Big data Hadoop and Spark Projects presentations Projects presentations

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Lab sessions Date 02.26 02.26 03.12 03.12 03.26 03.26 04.09 04.09 04.30 04.30 05.14 05.14

T. Vatter

(am) (pm) (am) (pm) (am) (pm) (am) (pm) (am) (pm) (am) (pm)

Topic R Refresher Workflow, RMarkdown, data w. & v. (I) Project Workflow, RMarkdown, data w. & v. (I) Project Data w. & v. (II), modeling (I and II) Project Data w. & v. (II), modeling (I and II) Project Shiny app Project Spark

Lecture I: Course Overview, Intro to Data Science, and R

HW# / HW1 / HW1 / HW2 / HW2 / HW3 / HW4

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Milestones

Date 03.26 04.09 04.30 05.14 05.14 05.28 05.28

Assignment HW1 Project proposal HW2 HW3 Project update HW4 Project report

To be submitted before midnight of the due date No late submission without medical certificate

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Course website

All lecture notes, the syllabus, assignments, and additional resources are available at:

https://tvatter.github.io/dsfba 2018/

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Resources

R for data science The CRAN website Rstudio cheat sheets Much more in the resources section of the course website

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Best place to look for answers?

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Outline

1 Course overview

2 Intro to data science

3 R

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What is Data Science? Wikipedia: “the extraction of knowledge from data” Precise definition a bit unclear and controversed... Practitioners “agree” on the components of data science: I

database management

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gathering and cleaning

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exploratory analysis

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predictive modeling

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data summary and visualization

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A brief (and opinionated) history 1960, Peter Naur publishes Datalogy: the science of data and its place in education 1962, John Tuckey, The Future of Data Analysis: . . . as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt. . . I have come to feel that my central interest is in data analysis. . . 1974, Peter Naur, Concise Survey of Computer Methods: The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences. 1977, John Tuckey publishes Exploratory Data Analysis T. Vatter

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A brief (and opinionated) history 1989, first Knowledge Discovery in Databases (KDD) workshop (later maturing into the ACM SIGKDD Conference) 1994, Database marketing: Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get you to do so. 1996, International Federation of Classification Societies (IFCS) meet in Tokyo (Data science, classification, and related methods) 1997, C.F. Jeff Wu inaugural’s lecture Statistics = Data Science? for appointment to the H. C. Carver Professorship at the University of Michigan. T. Vatter

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A brief (and opinionated) history 1997, the journal Data Mining and Knowledge Discovery is launched 2001, William Cleveland (Bell Labs) published Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics. [a plan] to enlarge the major areas of technical work of the field of statistics. Because the plan is ambitious and implies substantial change, the altered field will be called “data science.” 2001, Leo Breiman, Statistical Modeling: The Two Cultures There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. T. Vatter

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A brief (and opinionated) history 2002, creation of Data Science Journal (CS focus) 2003, creation of Journal of Data Science (stats focus) 2006, Hadoop 2008, DJ Patil (LinkedIn) and Jeff Hammerbacher (Facebook) coined the term data scientist to define their jobs 2009, reintroduction of the term NoSQL 2009, Hal Varian (chief economist at Google) the sexy job in the next 10 years will be statisticians 2012, Harvard Business Review publishes Data Scientist: The Sexiest Job of the 21st Century 2012, job listings for Data Scientists increased by 10,000% 2014, Bin Yu’s Let us own Data Science speech 2015, DJ Patil as White House’s first Chief Data Scientist T. Vatter

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Applications

Some of the hiring partners of The Data Incubator E-marketing

Credit scoring

Recommender systems

E-commerce

Sport analytics

Government analysis

Biotechnology

Gaming

Image or speech recognition

Price comparisons

Fraud and risk detection

Airline routes planing

Social media

Delivery logistics

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Data scientists?

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The data science toolbox

source: datasciencecentral.com T. Vatter

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Technology ecosystem

source: rosebt.com T. Vatter

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Most popular?

source: kdnuggets.com T. Vatter

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Outline

1 Course overview

2 Intro to data science

3 R

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S and R S I

A statistical programming language

I

First appeared in 1976

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Developed by John Chambers and (in earlier versions) Rick Becker and Allan Wilks of Bell Labs

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John Chambers, [the aim is] o turn ideas into software, quickly and faithfully

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Modern implementation of S

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First appeared in 1993

I

Created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand

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Currently developed by the R Development Core Team

R

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Some “technical” details about R Part of the GNU free software project Source code written primarily in C, Fortran, and R Available for Windows, macOS, and Linux Multi-paradigm: object-oriented, functional, procedural Dynamically typed Scripting language (interpreted) Wide variety of statistical and graphical techniques Easily extensible through functions and packages Read/write from/to various data sources

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What about Excel?

source: fantasyfootballanalytics.net

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Excel is great for certain things...

source: github.com/jdwilson4 T. Vatter

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...but not everything

R’s advantages: Easier automation

Easier to find and fix errors

Better reproducibility

Free & open source

Faster computation

Advanced statistics capabilities

Supports larger data sets State-of-the-art graphics Reads any type of data Runs on many platforms More powerful data manipulation capabilities Easier project organization T. Vatter

Anyone can contribute packages to improve its functionality

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Automation and reproducibility

source: trendct.org T. Vatter

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How about Python?

source: python.org

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CRAN

source: cran.r-project.org T. Vatter

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RStudio An open-source integrated development environment (IDE) RStudio Desktop available for Windows, macOS, and Linux

source: rstudio.com T. Vatter

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Lecture I: Course Overview, Intro to Data Science, and R - GitHub

Lecture I: Course Overview,. Intro to Data Science, and R. Data Science for Business Analytics. Thibault Vatter . Department of Statistics, Columbia University and HEC Lausanne, UNIL. 26.02.2018 ...

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