Showing posts with label Big Data. Show all posts

Business Understanding, The first stage of Data Science

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Ruinas de Ujarrás, Ujarrás, Cartago, Costa Rica

Photo by Jonathan Acuña (2018)

Business Understanding

The first stage of Data Science

By Prof. Jonathan Acuña-Solano, M. Ed.
Head of Curriculum Development Academic Department Centro Cultural Costarricense-Norteamericano	Senior Language Professor School of English Faculty of Social Sciences Universidad Latina de Costa Rica
Sunday, August 18, 2019 Post 338 / DS Log 7

Data Science is quite peculiar when it comes to the steps one has to follow in order to start the process of comprehending what exactly needs to be addressed when a company’s problem needs to be explored, understood, and then resolved. But why is it so essential to establish the “business understanding” at the start of the Data Science methodology? Let’s explore some potential answers to this question especially when linked to a very specific problem.

“Business understanding is the first stage of the data science methodology; it provides clarity about the problem to be solved and the data that should be used” (Laureate Education Inc., 2018). When a data scientist is given a problem that needs to be explained and then solved, that query posed by company stakeholders can be misunderstood if this process is not carried out; it is a matter of perception in the end. The company need to explain the problem they have in full to the scientist(s); details cannot be omitted. The scientist(s) must comprehend the problem to help the company formulate the right questions to obtain the right guidance to gather data. With these discussions among the company and the scientist(s), business understanding will help determine that kind of methodological approach needed: descriptive or predictive.

Business Problem Sample:	1) The scientist has to sit with the school’s academic stakeholders to be explained what exactly the problem is and its current implications for the institution, its reputation, and how graduate students are perceived in the work market in the country.
A language school whose students are not obtaining the expected CEFR outcome at the end of its program
2) Understanding how the school is being affected by a poor performance of their graduating students will help stakehoders to formulate the right question(s) to ask to set the data requirements to gather information.	3) This open and sincere discussion with the school academic stakeholders will provide room to comprehend why the problem needs to be given a solution, the reasons why the current state of affairs has to be amended.

“To establish business understanding, structured discussions with different stakeholders must happen so that the research focus (goals and objectives) can be classified” (Laureate Education Inc., 2018). It is crucial to clarify at this point that these “discussions” cannot just be held to ask company contributors what they want in terms of the enterprise’s problem; consultations have to be organized to get to the gist of the research focus needed to find solutions to the problem. Team members must come from different areas in a company; business understanding cannot just be provided by one single individual. And through all these deliberations goals and objectives also need to be clearly stated in the minds of company’s stakeholders and the data scientist(s). Forgetting this simple step in business understanding may trigger the wrong results.

Business Problem Sample:	1) Rounds of discussions must be organized with the school academic stakeholders to fully clarify what the gist of the project and its scope are. The research focus needs to be clearly stated because results can be wrong.
A language school whose students are not obtaining the expected CEFR outcome at the end of its program
2) The group of academic contributors must come from different areas of the department. This is not just about an academic director’s perception; it has to come from all areas that constituted the department.	3) All members of the academic team must have clearly stated -in their mind- the goals and objectives of finding the reasons why students are not achieving the correct CEFR level. Everyone has to embrace the project.

“Once a business understanding is in place, key business partners can remain engaged and provide support and guidance to project members” (Laureate Education Inc., 2018). Company’s team members must remain part of the project; they cannot detach themselves from the data scientist(s) assigned to the business strategy to find solutions to a problem. As active members in the process, company’s partners are present to support the data analysts by providing them with any other vital information (such as databases) to walk the right track towards the finding of (an) answer(s) to the stakeholders’ question(s). The engagement we are talking about is manifested in the establishment of a business understanding and in the guidance needed when scientists may be missing a piece of the puzzle to comprehend the company’s problem.

Business Problem Sample:	1) Though there are structured conversations among the academic stakeholders, they need to remain part of the project and not just stay aside and wait for results. These people help in discovering solutions.
A language school whose students are not obtaining the expected CEFR outcome at the end of its program
2) Academic collaborators support the data scientist(s) when they contribute with information to consolidate the business understanding here linked to the poor CEFR performance of the school’s language learners.	3) Academic engagement will be present all across the process since as team players, they can provide the data science group with guidance especially when a piece of the puzzle is missing in its right position.

As a first stage in Data Science, business understanding is decisive and imperative. Lack of understanding among all participants in a data science team can lead to formulating wrong questions and obtaining inaccurate answers. In the example used in this presentation of facts associated to the language school, there are plenty of people involved in the search for an answer as to why their learners are not achieving the mastery of the CEFR level the program aims at. Their participation in the process to find answers to the questions they pose as central will determine the goals, objectives, and scope of the possible answers they can obtain. As stakeholders they can make better decisions in regards to what needs to me done to help their students become competent English speakers.

References

Laureate Education Inc. (2018). Asking Questions with Data Science. Retrieved from One Faculty: https://dtl.laureate.net/webapps/blackboard/content/listContent.jsp?course_id=_165016_1&content_id=_801203_1&mode=reset

Post 338 - Business Understanding by Jonathan Acuña on Scribd

Sunday, August 18, 2019

Gray Matter in Data Science: Some Insights in Methodology

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Capilla del Rosario, Santo Domingo Church, Quito, Ecuador

Photo by Jonathan Acuña (2018)

Gray Matter in Data Science

Some Insights in Methodology

By Prof. Jonathan Acuña-Solano, M. Ed.

School of English

Faculty of Social Sciences

Universidad Latina de Costa Rica

Saturday, August 3, 2019

Post 335 / DS Log 6

When one talks about information found in Big Data, info can be referred as gray matter. Why gray? The answer is simply; it turns gray because in the process of making use of Data Science, when a question is asked, the response cannot be referred as black or white. In other words, the grayness is the result of having various ways of answering a question (problem) stated by stakeholders. Based on Dr. Mustaza Haider (Laureate Education Inc., 2018), a data scientist will not find answers clearly. Responses won’t be found quickly and easily; they are the gray matter that needs to be molded into the answers that institutions need to describe what is currently happening or what could come in the future.

Based on Laureate Education Inc. (2018), Data Science Metholodology has to follow certain sequential steps to “yield” answers to questions made by stakeholders in an institution. This methodology is here to turn gray matter into a visible black-and-white “object” that can be manipulated, analyzed, and understood. Take a look at the methodological model.

1	Business Understanding	The process begins with the search for clarification about the research focus.
2	Analytic Approach	Next step is to find clarification from the stakeholders who are asking the question.
3	Data Requirements	After that, the data scientist prepares the parameters to meet the desired outcome.
4	Data Collection	The following phase consists of the actual collection of data to answer the question.
5	Data Understanding	Next stage refers to using the collected data to construct the data set for cleansing.
6	Data Preparation	The subsequent maneuver is the actual cleansing of the data from “dirty data.”
7	Modeling	Next procedure implies the development of a descriptive or predictive model for the data.
8	Evaluation	Next in line is to evaluate the depurated data to see if it provides insight into the question.
9	Deployment	Our before-last step is to “move strategically” the collected data to “push out” an answer.
10	Feedback	Finally, refinement of the model created is ready to run or to get adjusted.
Created by Prof. Jonathan Acuña with information from Laureate Education Inc. (2018)

Parallel to these methodological considerations, the following infographic presents what happens when these processes (or methodological steps) are transformed into questions that need to be fully answered to get to provide an answer to what is being asked by institutional stakeholders.

To conclude, the Data Science Methodology used to answer a question is precise and must be followed to the very detail. Doing otherwise will probably trigger answers that do not provide any light to problem a company wants to explain or an issue that can materialize in the short or long run.

References

Laureate Education Inc. (2018). Things Data Science People Say. [Video File]. Retrieved from https://dtl.laureate.net/webapps/blackboard/content/listContent.jsp?course_id=_165016_1&content_id=_801203_1&mode=reset

Gray Areas in DS by Jonathan Acuña on Scribd

Saturday, August 03, 2019

Deriving Insights of Value

#EdChat, Big Data, Data Science, Data-Driven Teaching, The Data Scientist 0 comments

Quartier de la Sorbonne, Paris, France

Photo by Jonathan Acuña (2018)

Deriving Insights of Value

A simple problem, but what’s the answer?

By Prof. Jonathan Acuña-Solano, M. Ed.

School of English

Faculty of Social Sciences

Universidad Latina de Costa Rica

Saturday, July 27, 2019

Post 333 / DS Log 5

As mentioned before, the data scientist teacher is someone who works with data in three different fronts simultaneously. That teacher is a miner, a cleanser, and an analyst. Let’s see his/her role in the following case scenario, a language school whose graduating population is not interested in participating in the institution’s graduation.

1	Problem Identification	In this case scenario the problem is not that only 53% of the possibly graduating learners want to participate in the end-of-the-course ceremony; the real problem to discover is why this is happening. The teacher has to find out the real question to ask to mine and explore data.
2	Data Collection	At this point, the teacher has to start collecting data from as many sources as it is possible including the school’s database, any economic report issued by the Ministry of Economy, printed press and news programs on TV/radio or social media.
3	Data Exploration	With all possible data coming from public or institutional databases, the teacher can start finding the pieces of the puzzle. Information can then be organized for the teacher to start finding commonalities, categories, or names for the reasons that are found.
4	Analysis of Data Collected from Various Sources	The teacher cannot only base the analysis of data from institutional databases. Some sort of triangulation is needed to match data to reveal connections never thought before but that may have a say on why some 43% of possibly graduating students don’t feel encouraged to attend a graduation ceremony.
5	The Storytelling behind the Data	Once the teacher lets the data speak by themselves, s/he listens to what all these is stating as to why learners are uninterested (or interested) in going to a graduation ceremony. Reasons can be triggered by different societal phenomena.
6	Taking Action	As soon as the data tell their story, the teacher has to talk to the institution’s stakeholders, directors, or departmental heads to discuss what needs to be done to either motivate learners to join the graduation celebration or to reconsider if the way the graduation ceremony is actually held should continue to be celebrated in the same way.

Though it is not yet clear why only 53% of learners who finish the institution’s program are willing to go to the graduation ceremony, the case is a good example as to how the “investigation” has to be carried out. And for a better understanding of these notes of mine, please take a look at the infographic that matches what each step of the data science procedure contains along with the case scenario.

Saturday, July 27, 2019

Teachers Exploring Data: The teacher as a data scientist

Big Data, Data Science, Data-Driven Teaching, The Data Scientist 0 comments

Hyde Park, The Long Water, London, UK

Photo by Jonathan Acuña (2018)

Teachers Exploring Data

The teacher as a data scientist

By Prof. Jonathan Acuña-Solano, M. Ed.

School of English

Faculty of Social Sciences

Universidad Latina de Costa Rica

Thursday, July 25, 2019

Post 331 / DS Log 4

Can teachers mine and explore data? A resounding yes as an answer needs to be uttered. Data Science (DS) is not exclusively connected to business management decisions; it can be perfectly manipulated by educators, who -in the search for answers- can make use of information to decide on various education issues. Though DS is not meant to be the cup of tea of every education professional, institutions ought to count with some to help in the visualization and explanation of behavior among students.

Educators interested in Data Science should look for a role in data visualization. These teachers need to encompass a set of traits that can make their mining of data easier:

a) The ability of manipulate data,

b) The talent to analyze data,

c) The capacity to come up with innovative solutions,

d) The skill to present findings, results, and suggestions,

e) The cleverness to tell the story behind the data, and

f) The knack to deal with other stakeholders.

Due to the importance of these traits, teachers playfully manipulating data to make sense of their reality also possess curiosity, analytical strength, excitement about data, and some sort of technical skills to work with data from a database.

Copy the following Google Drive address to see the original infographic and to enlarge it:
https://drive.google.com/file/d/1Jm1_QcrT48if_vWkB_LGSO-s2nTByDCW/view?usp=sharing

Are educators ready to explore data? No doubt that there are many teachers who already work with data empirically. And this empiricism is perfect because it is the basis for helping them develop their competence in this area. Conclusion: as Professor Murtaza Haider stated (2018), he can train someone to use algorithms and software to work with data, but curiosity and the desire to explore data cannot be taught.