Writing Exercise

Writing Exercise

 300 words Only. Due in 6 hours. No Plagiarism.

Writing Exercise

For this assignment, I’d like to see examples of you using in line citations and a reference list when referencing or quoting information you learn in this week’s reading material. The purpose is to get everyone comfortable with the usage and formatting of in line citations and reference lists.

Based on some part of this week’s reading material, please write your own example of a:

  1. Summary
  2. Paraphrase
  3. A cited quote in a sentence 

Data Driven Decision Making Week 1 Introduction to Decision Making: Objectives Asking Questions Using Evidence & Citing Sources

1

Agenda

Instructor Introduction

Course Overview

Gathering evidence + citing your sources

Data Scientists on the Org Chart

Intro to Data Driven Decision Making

Week 1 Homework

What is decision making?

The process of selecting a choice from a range of possible options, with the goal of achieving a specific objective.

From:

Figliuolo, Mike. (2018). Decision Making Strategies. Retrieved 8 October 2018, from https://www.lynda.com/Business-Skills-tutorials/Defining-decision-making/186697/373496-4.html?org=neu.edu

Decision making steps

1. Define ObjectiveAsking good questions (Week 1)
2. Reduce ambiguity and risk Collect data Analyze dataAsking good questions (Week 1) Data collection (Week 2) Data analysis (Week 3)
3. Make a choice (alone or as a group)Making the decision (Week 4) Leadership (Week 5)
4. Execute
5. Measure and adjust accordingFollow up (Week 6)

Modified from: Figliuolo, Mike. (2018). Decision Making Strategies. Retrieved 8 October 2018,

from https://www.lynda.com/Business-Skills-tutorials/Defining-decision-making/186697/373496-4.html?org=neu.edu

Note

Not always this linear

In each step of this process, decisions need to be made

Decision making steps

1. Define ObjectiveAsking good questions (Week 1)
2. Reduce ambiguity and risk Collect data Analyze dataAsking good questions (Week 1) Data collection (Week 2) Data analysis (Week 3)
3. Make a choice (alone or as a group)Making the decision (Week 4) Leadership (Week 4)
4. Execute
5. Measure and adjust accordingMonitoring outcomes (Week 5) Follow up (Week 6)

Modified from: Figliuolo, Mike. (2018). Decision Making Strategies. Retrieved 8 October 2018, from https://www.lynda.com/Business-Skills-tutorials/Defining-decision-making/186697/373496-4.html?org=neu.edu

Weekly Topics

Week 1

Gathering evidence / using citations

Introduction to Decision Making

Overview

Defining objectives

Asking questions

Presentation and Communication (supplemental video)

Week 2

Data Collection

Defining metrics

Data Management

Week 3

Data Analysis

Overview & Learning Methods

Machine Learning/Artificial Intelligence

Statistical Diagnostics

Data Software

Week 4

Making the decision

Leadership

Week 5

Monitoring outcomes

Communication

Visualization

Week 6

Statistical Review

Follow up decisions

Textbook

Randy Bartlett: A Practitioner’s Guide to Business Analytics

Available FREE from Northeastern Library.

You can search for it from the main library.northeastern.edu page or the following link:

https://onesearch.library.northeastern.edu/permalink/f/365rt0/NEU_ALMA51303612770001401

From there:

Under View Online, click O’Reilly Learning Platform link

On new page, select the Select your institution dropdown and choose Not listed? Click here.

In the new box, select “Already a user? Click here.”

On the new page, click Sign In with Google

Use your Northeastern email address and password to sign in.

High level view & theory of data-driven decision making

Cases of data-driven decision making

Throughout the course, we will follow and pretend we’re involved in the decision making processes at two companies, which represent common data-driven decision making situations:

Salesforce – Closing the gender-pay gap

Big question that requires pulling data from many sources, defining metrics, putting it all together into a recommendation.

Netflix – Building + improving recommendation/personalization systems

Recommendation systems make automated decisions about what to present to users. They are also constantly updated for better results.

Weekly assignments

Reading + video materials

Real life examples

Practical “tutorials” (usually LinkedIn Learning)

Email-essay (5x, 50 pts/each)

Write an email based on one of the cases, as if you’re a member of the data science team

Weekly prompts based on that week’s material

Slides (4x, 50 pts/each)

Make a presentation, generally for a team meeting presenting the information in the email-essay

One week you will present in class (sign up on Wiki)

Exercises (3x, 25-100 pts/each)

Skill building

Short Essay (3x, 50 pts/each)

Prompts generally based on the weekly readings

Discussion (6x, 25 pts/each)

Write about what you read/watched/learned this week

Collaborative work

For scenario-based assignments, you may work together with one partner

Which assignments are specified in weekly Assignments section:

Green = Individual work 

Maroon = Partner work (partner not required)

Details

Partners will turn in the same document in TurnItIn

I know the similarity score will be high

Put both names on the assignment

Week-to-week can change partners, stop or start working with partner

Week 1 Email-Essay

(1) Email-essay scenario:  You are an data scientist working for Salesforce or Netflix. The project in the case you’ve chosen is being launched. Your manager sends you to a multi-team kickoff meeting with executives leading the project. In the meeting you learn about the project’s objective(s) and context. (Information you will learn, in real life, from the Salesforce/Netflix articles.) After the meeting you write your manager an email describing:

the objective(s) of the project

the context for the objective (why this objective?)

a list of questions you think the data science team needs to answer to make recommendations for the project

Scenario based writing

Imagine yourself as a data scientist or analyst at Salesforce or Netflix

Imagine yourself involved the decision making process

When in process specified in each week’s prompt

What would you be thinking and doing?

What would you recommend?

Use course materials for information and ideas

Practice writing clear professional emails as you will day to day as a data scientist or analyst

Week 1 Slides

(2) Slides: Your boss likes your email and asks you to present it in a kickoff meeting for the new project to the data science team. The meeting content should answer questions that your teammates would have going into the project and be a space for the team to discuss your list of questions and expand on it. Create slides based on your writing assignment that cover:

the objective(s) of the project

the context for the objective (why this objective?)

a list of questions you think the data science team needs to answer to make recommendations for the project

Slides

Building and presenting slides is an essential professional skill

Scenario-based assignment to practice that skill

Different communication format than email

Speaking + visuals

Text more concise

Supplemental lecture this week: “Presentations & Slides”

Course Material > Week 1 > Lectures and Videos

In class presentation of Slides

Every student (or partner pair) will present their weekly slides once during one class session

Present slides from previous week

Sign up on Presentation Signup Wiki

You may either:

Present “live” in class, by sharing your screen during the Zoom meeting

Record you presentation as an mp4 and I’ll play it in the class Zoom meeting

If partner presentation, both must speak

Class schedule with Homework

WeekDatesTopicAssignments
   Discussion primary response due Saturdays 11:59p Everything else due (the next) Monday 11:59p Except final for grading deadline reasons
1July 6-12Introduction Objectives Asking Questions Using Evidence & Citing SourcesIntroductory post Reading/videos Email-essay Slides Short Answer Writing Exercise Discussion posts
2July 13-19Data Collection Defining metrics Data ManagementReading/videos Email-essay Slides Data doc exercise Discussion posts
3July 20-26Data Analysis Data SoftwareReading/videos Email essay Slides Short answer Discussion
4July 27 – August 2Making the decision LeadershipReading/videos Email essay Slides Short Answer Discussion
5  August 3-9  Monitoring outcomes Communication Visualization  Reading/videos Email essay Dashboard/Outcome report Discussion
6  August 10-16Statistical Review Follow up decisionsDiscussion Final email-essay (Optional) Extra Credit Slides All due Sun, August 16, 11:59pm  

Course Grading

AssignmentPointsNotesPercentage
Introductory Post25 2.3%
6 Weekly Discussions – Discussion Board15025 points each14.0%
5 Weekly Email-Essays25050 points each23.3%
4 Weekly Slides10025 points each9.3%
3 Short Answer15050 points each14.0%
Writing Exercise25Week 12.3%
Data description exercise50Week 24.7%
Dashboard/Outcome visualization exercise100Week 59.3%
Presentation of slides25one week’s slides2.3%
Final Email-Essay200 18.6%
Extra Credit – Final Slides1515 extra credit points not summed into total2.3%
Total1075 100%

Class attendance

You are expected to watch every lecture

You can watch classes “live” on Zoom

You can watch the recorded sessions later

There will be no participation grade or tracking of your watching

Late work

You will be allowed one 7 days extension for any reason

Further extensions require an “emergency” or other urgent issue

Just email me

Gathering evidence + citing your sources

Gathering evidence

In this class, our data is reports of how companies perform data-driven decision making:

Factual information from trusted sources

Theory/options from trusted sources

Sources

Case studies

Magazine articles

Textbook

LinkedIn Learning videos, etc

Blog posts

Assignments =

these facts and theories + your own insights

Course Materials > Week 1 > scroll

Course materials

High level view of topics, theory, & short examples

Textbook

LinkedIn Learning/etc videos

Details of cases

Salesforce article from The Atlantic

Netflix article from Thrillist

Netflix Tech blog

Examples from other companies

Links to everything but the Textbook each week on Blackboard

Course Materials > Week # > Supplemental materials

Sometimes link is in folder/sometimes directly on list

CLICK

Further research

Salesforce

Google “Salesforce equal pay”

Look at what the sources are

Further research

Salesforce

Google “Salesforce equal pay”

Look at what the sources are

Further research

Salesforce

Google “Salesforce equal pay”

Look at what the sources are

Follow link to original article

Further research

Salesforce

Google “Salesforce equal pay”

Look at what the sources are

Follow link to original article

Look for longer articles

Look for keywords (here, names) to search

Further research

Salesforce

Google “Salesforce equal pay”

Look at what the sources are

Follow link to original article

Look for longer articles

Look for keywords (here, names) to search

Citing sources

Now you have sources of information/data, you need to cite them.

Why?

Back up our facts

Give credit

Where?

Bibliography/Reference list – At end of text, with full information

Inline citations – in text after each fact/opinion/info referenced, partial information

Bibliography/Reference list

What it looks like – from a peer-reviewed neuroscience paper

Anastassiou, C.A., Perin, R., Buzsáki, G., Markram, H., and Koch, C. (2015). Cell type- and activity-dependent extracellular correlates of intracellular spiking. J. Neurophysiol. 114, 608–623.

Ballini, M., Muller, J., Livi, P., Yihui Chen, Frey, U., Stettler, A., Shadmani, A., Viswam, V., Lloyd Jones, I., Jackel, D., et al. (2014). A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro. IEEE J. Solid-State Circuits 49, 2705–2719.

Berenyi, A., Somogyvari, Z., Nagy, A.J., Roux, L., Long, J.D., Fujisawa, S., Stark, E., Leonardo, A., Harris, T.D., and Buzsaki, G. (2014). Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. J. Neurophysiol. 111, 1132–1149.

Blanche, T.J., Spacek, M.A., Hetke, J.F., and Swindale, N.V. (2005). Polytrodes: high-density silicon electrode arrays for large-scale multiunit recording. J. Neurophysiol. 93, 2987–3000.

Buzsáki, G. (2004). Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446–451.

Chagnac-Amitai, Y., and Connors, B.W. (1989). Synchronized excitation and inhibition driven by intrinsically bursting neurons in neocortex. J. Neurophysiol. 62, 1149–1162.

Chung, J.E., Magland, J.F., Barnett, A.H., Tolosa, V.M., Tooker, A.C., Lee, K.Y., Shah, K.G., Felix, S.H., Frank, L.M., and Greengard, L.F. (2017). A Fully Automated Approach to Spike Sorting. Neuron 95, 1381-1394.e6.

It goes on for 6 pages

Bibliography/Reference list

Formatting rules, instructions on Purdue OWL (links in Course Material)

Bibliography/Reference list

Formatting rules, instructions on Purdue OWL (links in Course Material)

Left to right:

https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/in_text_citations_author_authors.html
https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_articles_in_periodicals.html
https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_books.html
https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_electronic_sources.html

Bibliography/Reference list

Browser plugins for references to online sources

E.g. Cite This For Me: Web Citer

Check result: sometimes it doesn’t figure out the date or author

Installed from:

https://chrome.google.com/webstore/detail/cite-this-for-me-web-cite/nnnmhgkokpalnmbeighfomegjfkklkle?hl=en

Inline citations

From Anita Lok’s Week 1 Email-Essay

To provide context on our discussion, they have been tasked to help understand the value of a manager at Google where the culture is technocratic and the organizational structure is flat. Historically, employees of Google, who are high skilled engineers, have not seen the value of having managers and believe they distract them from doing their work. As the organization grows beyond its 37,000 employees, Larry Page and Sergey Brin, the founders of Google, believes that managers hold an important role in communicating strategy, prioritizing projects, facilitating collaboration, supporting career development and ensuring that processes and systems aligned with company goals (Garvin, 2013).

The goals of Project Oxygen would be understanding the value of a manager at Google and help determine important management merits that would improve management effectiveness and have a positive impact on employee well-being and productivity. They have assembled a small team together to help tackle this issue. Additionally, it will be important to use this information to help change the perceptions held by employees that managers are not valuable.

Setty indicated that the guiding principle of this team is to be hypothesis-driven and help solve these problems through data (Garvin, 2013). With that in mind, it will be important to follow the four analytics based decision making acts (Bartlett, 2013, p.55-59):

Include at each fact or idea from one of your sources

Inline citations

From Purdue OWL

https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/in_text_citations_the_basics.html

Exercise: Summary, Paraphrase, Quotation

https://owl.purdue.edu/owl/research_and_citation/using_research/quoting_paraphrasing_and_summarizing/quoting_paraphrasing_and_summarizing.html
https://owl.purdue.edu/owl/research_and_citation/using_research/quoting_paraphrasing_and_summarizing/paraphrasing_sample_essay.html

To practice citing your sources, this week includes an exercise asking you to:

Summarize, Paraphrase, + Quote

Exercise: Summary, Paraphrase, Quotation

https://owl.purdue.edu/owl/research_and_citation/using_research/quoting_paraphrasing_and_summarizing/quoting_paraphrasing_and_summarizing.html
https://owl.purdue.edu/owl/research_and_citation/using_research/quoting_paraphrasing_and_summarizing/paraphrasing_sample_essay.html

To practice citing your sources, this week includes an exercise asking you to:

Summarize, Paraphrase, + Quote

Also, when you paraphrase, summarize, or otherwise put material in your own words, I can see your comprehension. Good for your grade.

Week 1 Homework – Writing Exercise

For this assignment, I’d like to see examples of you using in line citations and a reference list when referencing or quoting information you learn in this week’s reading material. The purpose is to get everyone comfortable with the usage and formatting of in line citations and reference lists.

Based on some part of this week’s reading material, please write your own example of a:

Summary

Paraphrase

Quote

More instructions and examples in Week 1 > Assignments.

You can pull sentences/paragraphs from your other assignments.

Writing Center

https://cssh.northeastern.edu/writingcenter/tutoring/online-appointments/

Data Scientists on the Org Chart

Data scientists on the org chart

Data scientist? Analyst? Business intelligence?

I use the terms interchangeably to mean people:

working directly with data in the company

doing sophisticated data manipulation and advanced analytics

(Not everyone uses interchangeably.)

Data scientists on the org chart

What roles do data scientists/analysts have in companies?

Many! Roughly, they can be:

Master of much

Managing everything from design and implementations of data systems to supporting colleagues to in depth analyses

Common at start-ups, small companies, companies just investing in data science

A part of a specific team, e.g. Product or Marketing

Run analyses and support analyses for that team, may be involved in design and implementation of data systems for that team

Data science teams

Service-oriented: Team works together to support data systems and analyses across company

Data science/Algorithm focused company: Data science team may be essential to design of product. May be incorporated in to tech team/algorithm team

At large companies, there may be many data science groups, with more specific functions

Data scientists on the org chart

Rough view of a service-focused data science team

Data science team

Executives

Product

Marketing

Etc

Requests for data, data access, analyses

Data, data access, analyses

IT

Engineering

Requests for functionality, access

Functionality, access

Data scientists on the org chart

Cockcroft, Adrian (2012).  Netflix Global Cloud Architecture. Slideshare.net. Retrieved 30 October 2018, from https://www.slideshare.net/adrianco/netflix-global-cloud/49

(2012)

Data scientists on the org chart

Cockcroft, Adrian (2012).  Netflix Global Cloud Architecture. Slideshare.net. Retrieved 30 October 2018, from https://www.slideshare.net/adrianco/netflix-global-cloud/49

(2012)

Introduction to Data Driven Decision Making

What is decision making?

The process of selecting a choice from a range of possible options, with the goal of achieving a specific objective.

From:

Figliuolo, Mike. (2018). Decision Making Strategies. Retrieved 8 October 2018, from https://www.lynda.com/Business-Skills-tutorials/Defining-decision-making/186697/373496-4.html?org=neu.edu

Deciding on cable/internet

What’s my objective?

Cheapest plan or combination of plans meeting my specifications

What are the options?

My Cable/Internet Decision Making Spreadsheet – Fall 2018

Decision making steps

1. Define ObjectiveAsking good questions (Week 1)
2. Reduce ambiguity and risk Collect data Analyze dataAsking good questions (Week 1) Data collection (Week 2) Data analysis (Week 3)
3. Make a choice (alone or as a group)Making the decision (Week 4) Leadership (Week 5)
4. Execute
5. Measure and adjust accordingFollow up (Week 6)

Modified from: Figliuolo, Mike. (2018). Decision Making Strategies. Retrieved 8 October 2018,

from https://www.lynda.com/Business-Skills-tutorials/Defining-decision-making/186697/373496-4.html?org=neu.edu

Deciding on cable/internet

Reduce ambiguity and risk

Questions I needed to answer:

What plans are available?

What are their features?

What channels are my shows on?

What channels does my husband care about?

What are the costs?

One time fees? Monthly fees? Changes in fees? Cancelation fees?

For me as a new customer? For my husband an existing customer?

How long will we live at this apartment?

My Cable/Internet Decision Making Spreadsheet – Fall 2018

Deciding on cable/internet

Reduce ambiguity and risk

Collected data for each plan/combination of plan

Costs

Features

Analysis

Cost for different time periods (we don’t know how long we’ll live in apartment)

My Cable/Internet Decision Making Spreadsheet

Deciding on cable/internet

Make a decision

What’s cheapest?

Does it have all the channels we want?

How much hassle is it?

Discuss with my husband

Decision

#2

Could have saved a bit of money on the 1, then 2 option, but hassle

My Cable/Internet Decision Making Spreadsheet

Deciding on cable/internet

Execute

Put in order online

Stayed home for installation

Measure and adjust accordingly

Sept 2018 – Sept 2019: Friends split the cost of Hulu with us

Late 2019: Hulu cost went up, friends moved

Late 2019: So many more streaming services!

Spring 2020: Trying other services for free/a month, cause at home always

My Cable/Internet Decision Making Spreadsheet

Data-driven decision making

Data is bigger

Volume, variety, velocity, veracity

The Four V’s of Big Data. (2018). IBM Big Data & Analytics Hub. Retrieved 30 October 2018, from https://www.ibmbigdatahub.com/infographic/four-vs-big-data

Data-driven decision making

Data is bigger

Volume, variety, velocity, veracity

Processing is faster

Faster processors, chips for specific algorithms, database design, solid state drives, etc

Analysis tools are more accessible

Open source software and tools

Tutorials, etc

Better UI/UX

Data-driven decision making

Data is bigger

Volume, variety, velocity, veracity

Processing is faster

Faster processors, chips for specific algorithms, database design, solid state drives, etc

Analysis tools are more accessible

Open source software and tools

Tutorials, etc

Better UI/UX

Ideally, combine this progress into improvements in decreasing the ambiguity of decision making

Why Data/Analytical Driven Decision Making?

Data is about ‘explaining the past’ and ‘predicting the future’:

Bring clarity to issues/define the issue

Provide facts/objectivity

Minimize surprises

Deliver actionable recommendations

Reduce the risk around decisions

Competitive advantage

Describe the customers

Modified from Uwe Hohgrawe

58

Why Data/Analytical Driven Decision Making: Performance and Opportunity!

Data is required to answer performance questions (examples):

Track results of KPI’s (Key Performance Indicators)

Conduct a situational analysis

Data is required to identify key opportunities (examples):

Analysis of the competitive environment

Detect drivers and barriers’

Forecast the range of the opportunity / Develop event-based predictive analytics

Data is required to capture opportunities (examples):

Recognize stakeholders and their motivations

Define KPI’s and targets

Modified from Uwe Hohgrawe

When does a business decision making process begin?

High level goals or vision

Salesforce vision for an equitable workplace

Big shift in product

Improve existing functionality

Netflix improving recommendation system

New features

Lyft/Uber adding Lyft Line/Uber Pool

Something goes wrong

Product failed to sell

Everyday activities

What rate should we offer this client?

Defining objective

What result do we want at the end of the project?

Can start general or specific

Netflix – Improve recommendation algorithm by 10%

Salesforce – Build an equitable workplace

More general objectives need to be broken down into specific, measurable objectives

Salesforce – Build an equitable workplace

Increase number of women in management

Require a certain % women in each management meeting

Determine if there is a gender pay gap

Asking Questions

Once you have an objective, the decision process begins with determining what you need to understand to make the decision

A good way to begin is asking questions.

How? How much? How many?

Why?

How is x defined? (x = salary, retention, etc)

Building a mental (or computational) model of situation

Analyze statements from colleagues. When they say “because” or “as a result”, ask about their reasoning if it’s not clear to you.

More: Watch Asking Great Data Science Questions – Doug Rose, LinkedIn Learning

6 Areas of Questions from “Asking Great Data Science Questions”

Clarify key terms

Root out assumptions

Find errors

See other causes

Uncover misleading statistics

Highlight missing data

Asking Great Data Science Questions – Doug Rose, LinkedIn Learning

Need help imagining a business project?

Watch (optional): Project Management Foundations: Small Projects – Bonnie Biafore, LinkedIn Learning

Overview of how projects at companies would ideally be structured

Deeper dive than you all will need for this class

Homework Scenarios

Cases of data-driven decision making

Throughout the course, we will follow and pretend we’re involved in the decision making processes at two companies, which represent common data-driven decision making situations:

Salesforce – Closing the gender-pay gap

Big question that requires pulling data from many sources, defining metrics, putting it all together into a recommendation.

Netflix – Building + improving a recommendation and personalization systems

Recommendation systems make automated decisions about what to present to users. They are also constantly updated for better results.

Homework Case FAQ

Can I switch between the cases?

Yes, week-to-week you are welcome to switch. Or assignment-to-assignment. (Rare.)

Email-essay prompts often refer to the previous week’s email-essay. If you’re switching one week to the next, would need to think a bit about what you would have written the week before.

Netflix – What case?

Your choice from Netflix’s various documented recommendation/personalization system improvements.

Several possibilities are linked and you’re also welcome to find your own.

When are you in Week 1?

Back in time, at the beginning of Salesforce/Netflix data-driven decision making process

Week 1 Email-Essay

(1) Email-essay scenario:  You are an data scientist working for Salesforce or Netflix. The project in the case you’ve chosen is being launched. Your manager sends you to a multi-team kickoff meeting with executives leading the project. In the meeting you learn about the project’s objective(s) and context. (Information you will learn, in real life, from the Salesforce/Netflix articles.) After the meeting you write your manager an email describing:

the objective(s) of the project

the context for the objective (why this objective?)

a list of questions you think the data science team needs to answer to make recommendations for the project

Objective

Netflix

Improve the recommendations algorithm

Improve customer retention

Salesforce

Build an equitable workplace for women

If there’s a gender pay gap, close it

Context

Salesforce

Data shows that women make less money than men in the workplace

Salesforce wants to be a place where women are treated equitably

Why?

Ethics and values

Employee retention

So far

Extended parental leave

At least 30% women in all meetings

Questions

Salesforce

Top level objective: Build an equitable workplace for women

Objective for this project: Pay women the same amount for the same work

Question for this objective: Are we paying women equal pay for equal work?

What further questions does the data science team need to answer to answer this question?

Homework

Week 1 Homework – Read/Watch/Review

How to cite your sources / use evidence / not plagiarize

Watch: Plagiarism: How to Avoid It – Bainbridge State College, YouTube.com

Read: Plagiarism: What It is and How to Recognize and Avoid It – Indiana University, Bloomington

Read: Using Evidence – Indiana University, Bloomington

Review: APA Formating and Style Guide – Purdue OWL

Instructions for APA format citations and bibliography

Review: Purdue OWL on Quoting, Summarizing, Paraphrasing

Asking questions

Watch: Asking Great Data Science Questions – Doug Rose, Lynda.com

Project Management – if you’re unfamiliar with working on business projects

Watch: Project Management Foundations: Small Projects – Bonnie Biafore, Lynda.com

Bold = required

Italics = optional

73

Week 1 Homework – Read/Watch/Review

Presentation Slides and Communication

Watch: Supplemental lecture on Presentations and Slides

Read: Think Your Way to Clear Writing, Barbara Minto

Textbook reading

Chapter 1 – The Business Analytics Revolution

Chapter 2 – Inside the Corporation

Chapter 6 – Developing a Competitive Advantage

Bold = required

Italics = optional

74

Week 1 Homework – Read/Watch/Review

“Case studies”

These two articles are introductions to the two “case studies” we will be reviewing in class. You will use (at least) one for this week’s writing assignments. If you are short on reading time, read one this week and the other next week. 

Salesforce and the Gender Pay Gap

Read: One Tech Company Just Erased Its Gender Pay Gap, BOURREE LAM, The Atlantic

Read: Equality at Salesforce: The Equal Pay Assessment Update, Cindy Robbins, Salesforce Blog

Netflix Recommendation System

Read: THE NETFLIX PRIZE: HOW A $1 MILLION CONTEST CHANGED BINGE-WATCHING FOREVER, Dan Johnson, Thrillist, Introduction to Netflix recommendation system

Read: Netflix Recommendations: Beyond the 5 stars, Part 1  and Part 2, Xavier Amatriain and Justin Basilico, Netflix Technical Blog

More history of the ongoing recommendation system improvements on the Netflix Technical Blog.

For your assignments, you can use the information from any post here about an improvement Netflix made to their recommendation system.

75

Week 1 Homework – Discussion board

Primary Post (75% grade)

Topic: Discuss a point you found interesting from the either the reading(s) or the video(s).

Requirements

At least one explicit reference to something you learned in the reading or the video, with an inline citation of that reading/video

If you’d like to relate this week’s materials to the global pandemic, or discuss course relevant topics related to data and/or decision making in the global pandemic, this is a space you can do so.

Min. 250 words. Word count does not include references.

One citation and reference list in APA format

Due Saturday by 11:59p

I’m looking for a considered and insightful response, some examples:

Summary of key points of interest from Week’s materials

Why particular points are interesting to you

Further research

Personal insights and opinions

Professional or academic experience relating to week’s topics

What’s missing in the reading

Questions you’re still wondering about

I’ll participate as I have time. If you include specific questions or opinions, I am likely to respond in greater depth.

Week 1 Homework – Discussion board

Secondary Posts (2 posts, 25% of grade)

Topic: Responses to other posts, or responses to responses to your post

Requirements

Min. 50 words.

Word count does not include references.

Due Tuesday* 11:59pm (*extra day because Memorial Day holiday)

Add to the conversation:

What did you relate to?

Do you agree or disagree? Why? (Don’t just say, ”I agree” or “I like what you said” for 50 words)

Can you answer any questions they brought up?

Did you find any further details on what they’re talking about?

Do you have any professional/academic experience you can share with them on the topic?

Ne6lix!OrganizaMon! DevOps!Org!ReporMng!into!Product!Group,!not!ITops!

CEO!–!Reed!HasMngs!

CPO!–!Chief!Product!Officer!–!Neil!Hunt!

VP!R!Cloud!and!Pla6orm!Engineering!R!Yury!

Architecture!

Future!planning! Security!Arch! Efficiency!

AWS!VPC! Hyperguard!

Powerpoint!!!

Pla6orm!and! Persistence! Engineering!

Base!Pla6orm! Zookeeper!

Cassandra!Ops!

AWS!Instances!

Cloud!SoluMons!

Monitoring! Monkeys! Build!Tools!

AWS!Instances! AWS!API!

Cloud!Ops! Reliability! Engineering!

Alert!RouMng! Incident!Lifecycle!

PagerDuty!

PersonalizaMon! Pla6orm!and!

Performance!Eng!

Metadata! Benchmarking! Memcached!

AWS!Instances!

Membership!and! Billing!

Data!sources! Vault!processing!

Cassandra!

Data!Science! Pla6orm!

Business! Intelligence!

Hadoop!on!EMR!

!

0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply