Info Tech - major paper
jkjohn123
OtherStudentResponses.docxStudent Post 1:- ( jathin 3 b)
Moura church works as a Data leader at Pateron. Pateron is a platform that unites the content creators and its fans on a membership basis and provide the exclusive content. Their business model is to get the fans subscribed for their favorite content creators. She mainly talked about how exactly their team data scientists operate and on what type of data are they working on. She spoke about the traits that are important when they seek a data scientist. She went over the common pitfalls that she has noticed in her tennure as a data scientst. She briefed on how one should opt for a particular technique when trying to solve a problem by using data sciences. Their major goal was to analyze the payments to find the fraud payments and help Patreon saving its revenue and good will.
b. Discuss the key points made by the author about data scientists. Provide your opinions, perspectives, and ideas including your agreement or disagreement with the points made.
The author has talked about some of the important aspects on how a data scientist should think of and how one can improve their thinking who are not familiar to business model thinking. She talked about the pitfalls she looks for in her data science applicants, I strongly agree with her words “Maslow’s hammer”. I have noticed in my career that developers tend to use skill/technique they have acquired recently. One of such scenario was where we had to edit a huge file based on some parameters. In this scenario i have seen the developer started developing an application in java to manipulate the file. I see this as time consuming and bad approach when looking at the time allocated. Some times the solution is simple and we tend to complicate it. I would have done the task using a simple script in shell or python which are quick and efficient in doing the job. I agree with her points on one should have the right technical stack and communication skills.
c. Share key takeaways that can be applied to either your professional or academic aspirations.
The key take aways that can be applied to my professional aspirations from this article are mainly the passion one should have towards the role and the mission we are working for. Business value thinking is something helps me in both my academic and professional career.How to approach a problem and picking the right strategy is really important. I would like to apply this in my professional life when a business requirement comes up for an IT solution. Analyzing the requirement and studying similar model or case study’s that were carried out in the area’s required.
References :
Var, I. (1998). Multivariate data analysis. vectors, 8(2), 125-136.
Lohnes, P. R. (1971). Multivariate data analysis. J. Wiley
Student Post 2:- ( naresh – week 3b)
The core functions that are supported by the data science team are product analytics, business analytics, core research and as well as business intelligence. The product analytics refers to the process of determining and measuring the product performance. Business analytics involves metrics and activities that are related o the marketing, finance, sales and other business teams. Core research involves studying techniques and methods which helps in driving the entire business of the organization. Business intelligence refers to the process of making improvements to the data in order to make it more accessible and easy to interpret.
The main traits or qualities of the data scientists are strong technical knowledge in languages such as SQL, python and the other statistical languages such as R. And communication which plays an important role in conveying the results to the stakeholders. Willingness and passion to solve complex problems in real life. Some of the traits that are considered as the pitfalls are desire to apply methodology to all the problems without actually considering the type.
The key points made for the data scientists are to improve their strategic thinking by reading various books available in the market and look at the various case studies which include similar kind of problems. And I think these two suggestions are really meaningful and as well as important. I believe that handling huge amounts of data requires various strategies to make effective decisions. So reading books that enhance our strategic thinking is highly essential. And by reading case studies, it is always enhances our practical knowledge.
The key takeaways from this article are the traits needed for the data scientist and the traits that are not needed which helps in avoiding the mistakes made by the data scientist. I mostly work on the application development and involve very little in handling the data as that it managed by another team. But now that I read this article I can apply the knowledge gained in my area. Such as avoiding applying the methodologies without understanding the type of the problem. I understand it is important to understand the type of the problem and then choose the method to solve the problem.
Reference
Jung, H. (2019). How to avoid the worst mistake every data scientist can make – using these2 crucial steps. Retrieved from https://towardsdatascience.com/how-to-avoid-the-worst-mistake-every-data-scientist-can-make-using-these-2-crucial-steps-a25a90b0995
Student Post 3:- (Naresh Kumar Pydi week 3 a)
National Institute of Standards and Technology researchers and scientist have done many findings in betterment of the analysis on the huge quantities of the data. There were many tools that were built for this purpose. With great efforts they finally published framework after many draft versions that was a combination of 1000 experts and NIST, industries, government and academics.
The pros of this framework include the steps in deploying the software tools which can be used in the process of data analysis of the computing platform. This can be operated in both the cloud-based platform or personal computer or an individual laptop. This framework has the ability in exchanging the platforms by substituting the advanced algorithms. This stands as the reference for creating environments for the creations of tools. Interoperability has been increasing rapidly and is most important when considered data pours in telescope ranging, physical experiments as platforms are growing in large number.
This framework does fail in providing the common language for large number of the stakeholders. The patterns, standards and the specifications will not be endorsed in various forms if they are not met to the expectations of the customer who use this framework in data analysis. In case of the similar data sets, this framework may not be as efficient as working for an individual different data set. The open reference architectures that are developed specially in solving the typical datasets some fail in providing the output in the estimated format.
There are several opportunities that are provided by the framework in improving and analyzing systems, process and the components of the Big Data. The conceptual model has been developed for agnostic technologies and the vendor context. This framework also provide functional and technical references for the consumers, agencies and the department in discussing, categorizing and understanding the solutions of the Big Data.
Reference
Chad, B. (2019). NIST final ‘Big Data’ framework will help make sense of our data-drenched age. Retrieved from https://www.nist.gov/news-events/news/2019/10/nist-final-big-data-framework-will-help-make-sense-our-data-drenched-age
Student Post 4: - ( atin Yalamanchi 3a)
Huge amount of data is being generated these days ranging from experiments to minute sensors which are integral part of today’s Internet of things (ITO). Analyzing these huge data sets computationally to derive a pattern or trends related to the data sources can be termed as bid data. There are lots of tools which are developed and available in the market for analyzing the data. National Institute of Standards and Technology (NIST) has worked with some of the professionals from the industry to come up with a framework with wide specifications in order to develop tools based on the need. This framework provides assistance to the developers in deploying the software tools that need for the analysis. NIST architecture helps in building a tool that can scale from a small laptop to a large-scale cloud computing based on the need. The reference architecture will help the developer in decision making and using Artificial Intelligence and Machine learning the newest advancements in the Data analytics.
Discuss the pros, cons, and opportunities for using this framework.
Pros:
Platform independent, NIST will help in guiding the developer irrespective of the platform he/she uses for the development.
Supports in moving from one platform to different platform and algorithms
Supports scaling form desktops to cloud based setup with multi node processing
Interoperability of the framework will help in solving wide range of data intensive problems.
Compatible with the latest the technological advancements.
Cons:
- Evolving Framework,
- Authentications are not part of the specifications for external systems
- Gaps in the security and privacy sub groups when compared to traditional implementations
References:
https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1500-7r2.pdf
Student Response 5:-
n Week One you constructed an essay that incorporated a business analytics problem to be solved within a specific industry. Review your previous work and expand on it.
Construct an essay specific to your industry and the potential problem to be solved that outlines your proposed exploratory data analytics approach.
(b) Identify five types of data that would be useful in solving this problem.
