What is Data Science - Applications and Careers in Data Science (2)

Applications and Careers in Data Science

Careers and Recruiting in Data Science

How Can Someone Become a Data Scientist?

- first skill you need is to know how to program, at least have some computational thinking

- need to know algebra, at least up to analytics, geometry, and hopefully some calculus, basic probability, basic statistics... really have to understand the difference and different statistical distributions, and databse

- have to know a lot of computer science theory and statistics, probability

 

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여러 사람이 나와서 본인의 생각을 이야기했음...

대부분 비슷한듯

프로그래밍, 컴퓨팅적 사고를 어느 정도 할 줄 알아야 하고,

기타 통계학이나 그런 것들을 알면 좋다...

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Recruiting for Data Science

- find the one who has the most resonance with your firm's DNA

- what really matters is who is passionate about the kind of business that you do

- first look for curiosity. Is that person curious about things not just for data science but anything like, are they curious about why this room is painted a certain way, ... or else

- second thing is that do they have sense of humor. you have to have a lighthearted mind about it (lightearted : 근심 걱정 없는, 쾌활한)

- and the last thing would be the technical skills because else things can be taught

 

- some technical component, communicate, relatable (because need to work across different departments)

- good mathematics and statistics background

- problem solving abilities and analysis

- love to play with data, know how to play with the data visualization

 

- ability to present your findings, either verbally, or in a presentation to introduce your results to the others

 

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여기도 다양한 사람들이 의견을 들어보았음...

근데 이게 꼭 DS에만 적용되는 것은 아니고...

어느정도는 그냥 공통적인 내용인듯?

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Careers in Data Science

- We need to know how to shape that data to focus on what to do with it and what it can do for us

- LinkedIn, Glassdoor, Indeed, and Dice track employment trends which show a career in data science moving up the list of most promising jobs to become number on since 2016

- 구구절절 다양한 곳들에서 data science에 대한 인력 부족을 문제로 삼고 있다는 이야기를 했음...

- What motivates someone going into a Data Science Career? : enjoys working with data, enjoy coding, no problem learning math and statistics, good storyteller

 

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DS 분야가 요새 굉장히 떠오르고 있고, 그에 따라 다양한 기업에서 채용하고 싶어한다

가 주 내용임

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Importance of Mathematics and Statistics for Data Science

- get familiar with databases

 

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그냥 약간 찬양영상같은... 이렇게 좋은데 데싸 왜 안 해? 같은... 

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The Report Structure

- Brief report is more to the point and presents a summary of key findings, and detailed report incrementally builds the argument and contains details about other relevant works, research methodology, data sources, and intermediate findings along with the main results

- deliverable follow a prescribed format including the cover page, table of contents, executive summary, detailed contents, acknowledgments, references, and appendices (if needed)

- the cover page should include the title of the report, names of authors, their affiliations, and contacts, the name of the institutional publisher, and the date of publication

- A table of contents(ToC) with main headings and lists of tables and figures offers a glimpse of what lies ahead in the document

- even for a short document, "abstract" or an "executive summary" is also needed

- introductory section is always helpful in setting up the problem for the reader who might be new to the topic and who might need to be gently introduced to the subject matter before being immersed in intricate details (intricate : 복잡한)

- methodology section, should introduce the research methods and data sources you used for the analysis

- results section is where you present your empirical findings starting with descriptive statistics, illustrative graphics

- discussion section is where you craft your main arguments by building on the results you have presented earlier and where you rely on the power of narrative to enable numbers to communicate your thesis to your readers

- conclusion section, you generalize your specific findings and take on a rather marketing approach to promote your findings so that the reader does not remain stuck in the caveats that you have voluntarily outlined earlier

 

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다시 읽고 나니 정말 중요한 부분인 것 같다!!

앞으로 어떤 보고서를 쓰던 이런 형태로 작성을 해야겠다 (과연)

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Have You Done Your Job as a Writer?

- Have you told readers, at the outset, what they might gain by reading your paper?

- Have you made the aim of your work clear?

- Have you explained the significance of your contribution?

- Have you set your work in the appropriate context by giving sufficient background (including a complete set of relevant references) to your work?

- Have you addressed the question of practicality and usefulness?

- Have you identified future developments that might result from your work?

- Have you structured your paper in a clear and logical fashion?

 

Practice Quiz : The Report Structure

Q. When deciding on the structure of a report, what factors should be considered?

A. The length of the document and its purpose

 

Q. What does ETL stand for in the context of data processing?

A. Extract, Transform, Load

 

Q. What is the purpose of including a table of contents (ToC) in a report, even if it's relatively short (five or fewer pages)

A. To offer a glimpse of the document's structure

 

Lesson Summary : Careers and Recruiting in Data Science

Recruiting

- Desired skills : Domain-specific knowledge, analyzing both structured and unstructured data, presenting and storytelling

- companies should develop teams with these skills

 

Excitement results in productivity

- excitement to work with data in their industry

- ask good questions

- eg. A data scientist in retail might not make a good data scientist in IT

 

Curious thinker

- curiosity leads to good questions

- curiosity encourages motivation

 

Logic minded

- think analytically and computationally

- backgoround in mathematics, statistics, and probability

 

Computer Programming

- python, R : statistical analysis

- data storage : structured and unstructured data

- manipulating data

- algorithms

 

Creating the narrative

- communicating

- instructing

- presenting

- storytelling

- synthesizing

 

Reporting

- what is gained

- defined goals

- significance

- context

- practicality and usefulness

- future developments

 

Summary

- caution in trying to find one person with all desired skills

- needs : curious people, understand subject matter, love of data, statistics, mathematics, machine learning, computer programming

- skilled storytelling

 

Practice Quiz : Careers and Recruiting in Data Science

Q. What is one key foundational skill required for someone entering a data science team?

A. Programming skills, algebra, geometry, calculus, basic probability, basic statistics, and knowledge of relational databases

 

Q. Per the expert Dr. Murtaza Haider, what crucial attribute should be prioritized in candidates when forming a data science team?

A. Passion and curiosity, particularly about the specific business domain

 

Q. Based on the Careers in Data Science video, which career has been ranked number one among the most promising jobs since 2016?

A. Data Science

 

A Roadmap to your Data Science Journey