What is Data Science - Applications and Careers in Data Science (1)

Applications and Careers in Data Science

Data Science Application Domains

How Should Companies Get Started in Data Science?

- first thing to do is to start capturing data, and archive it. do not overwrite it. data never gets old

- keep data, capture it, archive it, make sure nothing goes to waste

- have proper documentation

 

- start measuring things!

- data science inside a company is going to be as valuable as the data collected

 

- if sth is not measured, it's very difficult to improve it or to change it

- very first step is measurement

- if companies have existing data, then they should start looking at it and cleaning it

- don't have existing data, should start collecting it

 

- look for a team who love to work as a data scientist

- have employees that they are interested on data science

- have a team and each of them have strengths and interests in data science

 

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또다른 교수님 등장~

그리고 다양한 사람들이 등장해서 자신들의 의견을 펼침

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Old Problems, New Dat Science Solutions

- all organizations ultimately use data science to discover optimum solutions to exsiting problems

 

Uber

- collects real-time user data to discover how many drivers are available.

- if more needed, they should allow a surge charge to attract more drivers (surge : 밀려들다, 휩싸다, 급등)

- uses data to put the right number of drivers in the right place, at the right time, for a cost the rider is willing to pay

 

Toronto Transportation Commision

- great strides in solving an old problem with traffic flows, restructing those flows in and around the city (stride : 성큼성큼 걷다, 진전)

- using data science tools and analysis

- gathered data to better understand streetcar operations, and identify areas for interventions

- analyzed customer complaints data

- used probe data to better understand traffic performance on main routes and created a team to better capitalize on big data for both planning, operations and evaluation (probe : 조사하다, 살피다, 조사)

 

Environment

- data science can also play a proactive role (proactive : 상황을 앞서서 주도하는, 사전 대책을 강구하는)

- to figure out the environmental problem, the team used robotic boats, buoys, and camera-equipped drones to measure physical, chemical, and biological data in lakes where cyanobacteria is detected, collecting large amount of data related to the lakes and the development of the harmful blooms 

- the information collected will lead to better predictions of when and where cyanobacterial blooms take place, enabling proactive approaches to protect public health in recreational lakes and in those that supply drinking water

- such interdisciplinary training prepares the next generation of scientists to address societal issues with the proper modernized data science tools (interdisciplinary : 여러 학문 분야가 관련된)

- takes gathering a lot of data, cleaning and preparing it, and analyzing it to gain the insight needed to develop better solutions for today's enterprises

 

To get a better solution that is efficient

- must identify the problem and establish a clear unerstanding for it

- gather the data for analysis

- identify the right tools to use

- develop a data strategy

- case studies are also helpful in customizing a potential solution

 

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그냥 전체적으로 관련된 예시를 말해주는 내용이라...

너무 크게 집중해서 듣지 않아도 될 것 같다

환경 얘기할 때 무슨 박테리아 뭐시기 박테리아 얘기할 때 죽는줄

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Applications of Data Science

- data science and big data is making an undeniable impact on businesses, financial analytics, ...

- businesses can gain enormous value from the insights data science

- Recommenndation Engine is a common application of data science

- comapnies like amazon, netflix, spotify use specific recommendations derived from customer preferences and historical behavior

- google collects the data from your device and your current location, and recommends you some various data you wanted

 

Buisness Impact

- mckinsey& company said that data science was going to become the key basis of competition. supporting new waves of productivity, growth, and innovation

- UPS announced that it was using data from customers, drivers, and vehicles in a new route guidance system aimed to save time, money, and fuel

- Data science will fundamentally change the way businesses compete and operate!!

- eg. Netflix can be confident that a show will be a hit before filming even begins by analyzing users preference for certain directors and acting talent, and discovering which combinations people enjoy

- with data science, netflix knows what people want before they do

 

How data science is saving lives

- Data science can provide targeted information to help healthcare professionals give the best treatment to patients, or help predict natural disasters so that people can prepare early, and much more

- in healthcare, data scientists use predictive analytics developed from data mining, data modeling, statistics, and machine learning to find the best options for patients : examines all known factors for a disease

- recommends appropriate tests, suitable trials, and any suggested treatments

 

data mining

- mining data from medical records for different types of medical research

- developing more sophisticated big data analytics capabilities helps healthcare organizations move from basic descriptive analytics towards predictive insights

- use of predictive analytics tools is improving and providing new data analysis in a multitude of ways, alerting populations to danger faster than ever before

- large, high-quality data sets can be used to predict the occurrence of numerous types of natural disasters, which can be the difference between life and death for thousands of people

 

Data science tools enable organizations to analyse vast quantities of data from widely different sources, and present that information in a way that allows data scientists to gain new knowldege, in some cases, saving hundreds of lives.

 

The Final Deliverable (deliverable : 상품, 제품)

- ultimate purpose of analytics is to communicate findings to the concerend who might use these insights to formulate policy or strategy (formulate : 만들어내다, 표현하다)

- Large consulting firms, such as McKinsey and Deloitte, routinely generate analytics-driven reports to communicate their findings and, in the process, establish their expertise in specific knowledge domians.

- The Deloitte report uses time series plots to illustrate trends in markets

- One of the Deloitte's report uses data and analytics to generate the likely economic scenarios, and it builds a powerful narrative in support of the thesis statement that the U.S. economy is doing much better than most would like to believe (narrative : 묘사, 서술)

- Deloitte's report is a good example of storytelling with data and encourage you to read the report to decide yourself whether the deliverable would have been equally powerful without the narrative

 

- Before the authors started their analysis, they must have discussed the scope of the final deliverable.

- They would have deliberated the key message of the report and then looked for the data and analytics they needed to make their case(deliberate : 고의의, 의도적인, 신중히 생각하다)

- The initial planning and conceptualizing of the final deliverable is therefore extremely important for producing a compelling document. (compelling : 주목하지 않을 수 없는, 강렬한, 설득력 있는)

 

Embarking on analytics, without due consideration to the final deliverable, is likely to result in a poor-quality document where the analytics and narrative would struggle to blend.

 

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이 강의 전체에서 제일 중요한 내용은 여기였지 않을까 싶다

근데 이 내용을 영상 말고 줄글로 줘서...

집중 안 되는데 읽느라 넘 힘들었다 ㅠㅠ

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Practice Quiz : The Final Deliverable

Q. What is the ultimate purpose of analytics in the context of delivering insights and findings?

A. To summarize findings in tables and plots for communication

 

Q. What is the primary advantage of utilizing big data clusters?

A. They allow for easy distribution and parallel data processing.

 

Q. What is the key message in the Deloitte report, "United States Economic Forecast"?

A. The report presents a positive view of the robustness of the U.S. economy

 

Lesson Summary : Data Science Applications Domain

Why Data Science?

- discover optimal solutions

- establish a clear understanding of the problem

 

Measurement

- is the first step

- determine how to capture and gather the data you need

- don't overwrite old data

 

Data strategy

- clean

- identify tools

- consider case studies

- develop models

- take time to refine best practices, but benefits are worth it (refine : 정제하다, 개선하다)

 

Recommendation engines

- leveraging data to reveal patterns

- making recommendations related to searches and preferences

 

Informing business decisions

- create efficient routes for their drivers

- place drivers in the right place at the right time for the right place

 

Competitive Advantages

- correctly analyze massive amounts of data

- confidently predict customer behavior

 

Saving lives in healthcare

- predictive analytics can help find the best options for patients

- examine disease factors such as : gene markers, conditions, environmental factors

- help physicians to :  recomment appropriate tests, suitable trials, suggest treatments

 

Predict natural disasters

- infrom our understanding of: earthquakes, hurricanes, tornados, floods, volcanic eruptions

 

Final deliverable

- explain conclusions

- often in the form of a research paper or report

- establish your narratives : powerful, convincing, evidence-based

 

Practice Quiz : Data Science Application Domains

Q. How Does Data Science use predictive analytics and large-scale data analysis to contribute to healthcare and disaster preparedness?

A. By recommending appropriate tests, trials, and treatments for patients using predictive analytics

 

Q. Imagine you're an e-commerce company looking to enhance customer experiences and boost sales. How could data science help you achieve this goal?

A. By using algorithms to suggest products based on individual customer preferences

 

Q. As a manager of an online fashion store, you're concerned about customers adding items to their cart but not completing purchases. How can data science assist you in solving this problem and boosting conversion rates?

A. Analyzing customer behavior to find reasons for cart abandonment and applying strategies to address them.