Upload File

1 cs 133 - introduction to computational and data sciencecaora/cs133/slides/day1.pdfcourse grade...

  • Home
  • Documents
  • 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the
46
CS 133 - Introduction to Computational and Data Science Instructor: Renzhi Cao Computer Science Department Pacific Lutheran University Spring 2017

Upload: others

Post on 04-Nov-2020

1 views

Category:

Documents


0 download

Report

TRANSCRIPT

Page 1: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

CS 133 - Introduction to Computational and Data Science

Instructor: Renzhi Cao Computer Science Department

Pacific Lutheran University Spring 2017

1

Page 2: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

• Office: MCLT 248 • Office hours: In class website (cs.plu.edu/133) • Office Phone: 535-7409 • Email: [email protected]

About me

Renzhi Cao Prof. Cao ? Cow

• Data Science • Machine learning • Bioinformatics

Pictures from: https://www.google.com/search?q=cow&biw=1920&bih=911&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiOt5zlierOAhUE02MKHVbwDY8Q_AUIBigB#imgrc=0dSVh7Vlup1KqM%3A

2

mailto:[email protected]
Page 3: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

About guest speaker

Assistant Professor : Kyoungnam Catherine Ha

• Marketing Finance Interface • Marketing Analytics • Customer Relationship Management

3

School of Business

Page 4: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

About you

• Names • Where are you from? • Your major? • Hobbies? Movie? Song? Sports? Book? TV show?

What you did over your break? …

4

Page 5: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

What is this course?

What have I gotten myself into?

5

Page 6: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

What is this course?

6

Mathematics?Statistics?

Computer Science?Programming?

Biology?Economics?

Page 7: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

What is this course?

7

It’s a little bit of all of those….

Page 8: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

What is this course?

• Computer programming • Write software using Python and R

• Understand Computer Science as a discipline.

8

Page 9: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

9

JavaPython

Ruby

CC++ C#

Objective CSwift

Perl

Scheme

JavaScript

GoScala

Programming languages

R

Page 10: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

DO I HAVE WHAT IT TAKES TO SUCCEED AT PROGRAMMING?

Page 11: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Image source: http://www.saveursdunet.com/wp-content/uploads/2012/12/Mr-and-Mrs-geek.png

Page 12: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

WITH THE RIGHT ATTITUDE

AND A BIT OF HARD WORK

YOU CAN LEARN PROGRAM

… AND EVEN ENJOY IT!

Page 13: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the
Page 14: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

PROGRAMMING HAS A LOT IN COMMON WITH THINGS PEOPLE DO EVERY DAY

Page 15: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Musical notation captures a set of instructions that can be understood and ‘executed’

Page 16: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Knitting languages describe different types of stitches, how many rows to knit, etc.

PATTERN: 1. Knit one row. 2. Purl one row.3. Pattern Row: K1, K2 together, K6, WF K1, WF K6, K2 together twice, K6, WF K1, WF K6, K2 together, K1 4. Keep repeating these rows until you reach your desired length.

Knitting languages describe different types of stitches, how many rows to knit, etc.

Page 17: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Cooking also has special instructions and notations

Page 18: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Similar to learning to drive, programming requires learning many new things at once

Page 19: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Much like reading maps, we often think of our programs abstractly and from different perspectives

Page 20: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

20

One big difference with computers though, is that you must be extremely precise

Page 21: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the
Page 22: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

MINDSETS

Based on the work of Stanford Psychologist Carol Dweck

Page 23: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Growth MindsetFixed Mindset

intelligence is static

intelligence can be developed

Based on a graphic by Nigel Holmes available from :http://www.pvusd.net/Departments/GATE/dweck/

Page 24: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

…avoid challenges

Growth MindsetFixed Mindset

CHALLENGES…embrace challenges

intelligence is static

intelligence can be developed

Based on a graphic by Nigel Holmes available from :http://www.pvusd.net/Departments/GATE/dweck/

Page 25: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

…avoid challenges

…give up easily

Growth MindsetFixed Mindset

CHALLENGES

OBSTACLES

…embrace challenges

…persist in the face of setbacks

intelligence is static

intelligence can be developed

Based on a graphic by Nigel Holmes available from :http://www.pvusd.net/Departments/GATE/dweck/

Page 26: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

…avoid challenges

…give up easily

…see effort as fruitless or worse

Growth MindsetFixed Mindset

CHALLENGES

OBSTACLES

EFFORT

…embrace challenges

…persist in the face of setbacks

…see effort as the path to mastery

intelligence is static

intelligence can be developed

Based on a graphic by Nigel Holmes available from :http://www.pvusd.net/Departments/GATE/dweck/

Page 27: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

…avoid challenges

…give up easily

…see effort as fruitless or worse

Growth MindsetFixed Mindset

…ignore useful negative feedback

CHALLENGES

OBSTACLES

EFFORT

CRITICISM

…embrace challenges

…persist in the face of setbacks

…see effort as the path to mastery

…learn from criticism

intelligence is static

intelligence can be developed

Based on a graphic by Nigel Holmes available from :http://www.pvusd.net/Departments/GATE/dweck/

Page 28: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

…avoid challenges

…give up easily

…see effort as fruitless or worse

Growth MindsetFixed Mindset

…ignore useful negative feedback

...feel threatened by the success of

others

CHALLENGES

OBSTACLES

EFFORT

CRITICISM

SUCCESS OF OTHERS

…embrace challenges

…persist in the face of setbacks

…see effort as the path to mastery

…learn from criticism

…find lessons and inspiration in the success of others

intelligence is static

intelligence can be developed

Based on a graphic by Nigel Holmes available from :http://www.pvusd.net/Departments/GATE/dweck/

Page 29: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

29

What mindset do you have?

Page 30: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Syllabus

Page 31: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

31

Attendance

Attendance•Expected to attend every class•YOU are responsible for missed materialsClassroom Conduct•Come to class on time•Turn off electronic devices•Refrain from private conversations (voice or electronic)•Refrain from activities unrelated to current tasks in class•Treat others with respect and dignity

Page 32: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Data Science from Scratch: First Principles With Python. First Edition. Joel Grus. R Programming for Data Science. Roger Peng

32

Section 1: Tuesday, Thursday 09:55-11:40, Morken #203 (Dr. Cao)

Text book and meeting times

Page 33: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Course website

https://www.cs.plu.edu/~caora/cs133/

33

Page 34: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Course Goals • Develop important problem solving skills by programming • Explore the Python and R programming language • Better understand Computer Science as a discipline • Have fun writing computer programs and analyzing data!

34

Page 35: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

Course GradeInnovation project and class

participation – 10% • Use knowledge learned from the class,

propose ideas and analyze the data • Participate in each lecture Projects - 20% • Around two projects during the semester • Late assignments will be docked 25% per

day (every 24 hours).

Exams – 15% • One mid-term exam Quizzes and homework - 40% • 5 to 8 quizzes or homework • drop lowest score • no makeup quizzes Final Project --15% • Comprehensive, required for all students

• Consist of a report, a program and a presentation

Overall Score Grade100% -- 90% A / A-

90% -- 80% B+ / B / B-

80% -- 70% C+ / C / C-

70% -- 60% D+ / D / D-

60% -- 0% E

:

Page 36: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

36

I. Introduction to computation

▪Requires as many steps as people:▪ 4 people – 4 steps▪ 16 people – 16 steps▪ 32 people – 32 steps

▪Each person spends most of their time sitting idle:▪ 4 people – Each person idle 75% of the time▪ 16 people – Each person idle 94% of the time▪ 32 people – Each person idle 97% of the time

1. Write new birthday2. Eliminate later birthday

1. Write new birthday2. Eliminate later birthday

1. Write new birthday2. Eliminate later birthday

Stop

Start

1. Write new birthday2. Eliminate later birthday

Finding the earliest birthday - method 1

Page 37: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

37

I. Introduction to computation

Finding the earliest birthday - method 2▪Simultaneous events mean fewer steps:▪ 4 people – 2 steps▪ 16 people – 4 steps▪ 32 people – 5 steps

Stop

1. Compare birthdays2. Eliminate later birthday

Start Start

1. Compare birthdays2. Eliminate later birthday

Start Start

1. Compare birthdays2. Eliminate later birthday

▪ Fewer steps mean less idle time:▪ 4 people – idle ≤ 50% of time▪ 16 people – idle ≤ 75% of time▪ 32 people – idle ≤ 80% of time

Conclusion #1: Computers can’t see the “big picture” – only the immediate task at hand.Conclusion #2: Not all programs are equal – some are faster or more flexible than others.

Page 38: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

38

II. Problem-Solving

A. Understand the Problem▪ Do you understand all the words & terms that are being used? ▪ What are you being asked to find or show?▪ Is there enough information to solve the problem?▪ Can you draw a picture that might help?

B. Come Up With a Plan▪ Guess and check, make a list, or draw a picture.▪ Look for a pattern, or find a key equation.▪ Try solving a simplified version of the problem.▪ Work backwards.

C. Carry Out the Plan▪ Be aware that you may run into roadblocks or dead-ends!▪ Check to see if your results make sense.▪ Don’t be afraid to start over!

D. Make Your Solution Computer-Friendly▪ Imagine you are writing to a student not in this class.▪ Keep things brief… but make sure that you don’t leave anything out.▪ Write a step-by-step list of instructions… like writing a recipe.

Page 39: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

39

II. Problem-Solving

Some Practice Questions Here are a few problems to think about. Use the strategies from the previous slide, and write down at least three facts or observations that you think are important when it comes to solving the problem. We’ll discuss the pros and cons of each fact/observation before trying to solve the problems.

1. Same birthday. You and your classmates want to know if there are students sharing the same birthday. You have everyone’s birthday date (Month and Day), how do you quickly find it out?

2. Pizza Prices. You're trying to decide what size pizza to order, and have the choice of a 12" pizza for $13 or a 14" pizza for $16. Which one gives you the most pizza per dollar?

3. Finding the Day of the Week. What day of the week is 23 December 2017? What about 23 December 2087?

Page 40: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

40

III. Environment setup

Python

•Go to https://www.python.org/downloads/•Download Python 2.7.11•Select the option “add python.exe to Path”•Select “Will be installed in local hard drive”•Double click on the file and click next/yes for all questions

•Wait a moment……•Click Finish•Done!

https://www.python.org/downloads/
https://www.python.org/downloads/
Page 41: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

41

III. Environment setup

Atom

•Go to https://atom.io/•Click on Download installer•Open the file and follow the instructions

•You are ready to go!

https://atom.io/
https://atom.io/
Page 42: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

42

III. Environment setup

Anaconda

•I am aware that the book talks about Anaconda. Here is my opinion about it:

•It is really good! •If you are interested in installing Anaconda instead of Python and pip… Go for it!

•In class we will use regular Python and Atom, but I don’t want to stop you in any way from exploring other software

Page 43: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

43

III. Environment setup

R

•Go to https://www.r-project.org/•Click on Download CRAN•Select the area that it is closest to you (Fred Hutchinson Cancer Research Center, Seattle, WA)

•Select your operating system•Select “base”•Download file•Click on the file and click next on all the prompts. (leave the default values)

•Click Finish!

https://www.r-project.org/
https://www.r-project.org/
https://www.r-project.org/
Page 44: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

44

Resources

Before you leave today… - apply for a curly account

https://www.cs.plu.edu/hub/accounts/requests/new

Finish survey about your background (available on course website):

https://www.surveymonkey.com/r/LFFGBQ2

For next time… - Check the book if you would like to buy one - Check out class website and Sakai regularly: https://www.cs.plu.edu/~caora/cs133/

https://www.cs.plu.edu/~caora/cs133/
Page 45: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

45

Resources

Account for the first few days (active through Feb 22, 2017):

user name: firstday password: Spring17

Page 46: 1 CS 133 - Introduction to Computational and Data Sciencecaora/cs133/Slides/Day1.pdfCourse Grade Innovation project and class participation – 10% • Use knowledge learned from the

46

Resources

Create an Account…– Open the Firefox browser– Go to https://www.cs.plu.edu/hub/– Click on Request link– Review PLU Policies– Click on I agree link


Sweden Day1

Rc101 day1

Green Day1

Day1 tenses

Day1 Tanaka

Bootstrap day1

CS133 Input and output devices

Chem10171 day1

UoLCMI Day1

RDBMS - day1

Day1 Paint

day1 Switches

Day1 final

Judgement day1

FOREX101 Day1

Handouts - DAY1

P5utda day1

Day1 slides

Allure Day1

Showdaily2015 day1

Department Aerospace Engineering - IIT BombayDepartment Biosciences and Bioengineering: Day1, Slot day1, fn,: Day1, Slot day1, an,: Day1, Slot day1, ev,: Day2, Slot 1, fn,: LC301,

Workshop Day1

Android Day1

Enhancements Day1

Lecture Day1

Coaching day1

Day1 - Overview

Day1 aieee1

Javascript Day1

Intel Day1

Day1 Foundation

day1 session2

Showdaily day1

Upa2010 day1

Python Day1