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

Reflecting on Grammar Teaching: Grammar for Teachers

English Grammar, Grammar, Teaching, Teaching Grammar 0 comments

Jardín Botánico, Bogotá, Colombia

Photo by Jonathan Acuña (2017)

Reflecting on Grammar Teaching

Grammar for Teachers

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

School of English

Faculty of Social Sciences

Universidad Latina de Costa Rica

Friday, August 15, 2019
Post 337

Grammar has been one of those areas in the teaching of a foreign language that attracts many of us language instructors. Using grammar for communication is probably what many of us claim we teach our students so they can use English more effectively in communication. But the definition of what grammar consists of is by far one of those things we really want to state in a few words. Then, what does grammar mean?

The fact is that “the word grammar carries a wide range of connotations” (Crawford, 2013, p. 3). Depending on what part of the teaching spectrum one is standing, grammar can be thought in different ways. That is, a linguist’s definition of it differs greatly from what a language learner understands by it. And what about the language instructor or the curriculum designer’s ideas of what grammar connotes for them? Do all these individuals conceptualize grammar in the same way? Definitely not!

When thinking of the naming of what each persons’ conceptualization of grammar is, what matters most here is what grammar teaching consists of. For Crawford (2013), “grammar teaching consists of two different types of knowledge: teacher knowledge and teaching knowledge” (p. 1). For a better understanding of both constructs proposed by Crawford, consider the following infographic.

The idea behind this visual is to help the reader see what each one entails and what it is expected from us instructors when dealing with one or with the other. Both of the sides stated in the infographic highlight what a grammar teacher is meant to be able to do to help learners become competent users of the language.

Beyond this division of knowledge made, the best way to see why this knowledge is imperative in language teaching is linked to the reasons why native speakers and L2 learners use grammar rules for. Are we all language instructors aware of all these similarities and differences when these two types of speakers are compared? Take a look!

As stated in the visual, language is used for communication. What differs is what we do when we stand as a native speaker of a language and what we want to do as a L2 speaker. Both want to be able to transmit ideas that can be understood by other individuals, whether these are native speakers or foreigners using it for communication. And as explained in the infographic, we just want to be skillful in being able to encode and decode messages for the sake of conversation.

References

Crawford, W. (2013). Teaching Gramamr. Alexandria: TESOL International Association

Post 337 - Reflecting on Grammar Teaching by Jonathan Acuña on Scribd

Thursday, August 15, 2019

Hours Do Count When Learning English Online

Online Instruction, online learning, Online Teaching Practices, Reflective Teaching 0 comments

Panama City, seen from Old Panama, Panama

Photo by Jonathan Acuña (2017)

Hours Do Count

When Learning English Online

A bit of feedback for the self

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

School of English

Faculty of Social Sciences

Universidad Latina de Costa Rica

Sunday, August 11, 2019

Post 336

Have you ever been to Panama City? If you have, the city today has turned into a maze where one can easily get lost with countless skyscrapers overlooking you every step of your way. This same kind of feeling is what I have been experiencing when finishing piloting an English online program with local students over here in my home country, Costa Rica. However, when being in Panama City, it was much easier to use Google Maps or to call an Uber cab to find your way easily in the city. But, when thinking back on learning English in an online context, what is the map that must be followed to see how the CEFR language proficiency levels actually work in an online learning context?

I have often been asked if learning English online is easy when compared to a F2F context. When learners ask me this question, I often recall my former boss’s insight into language learning in a virtual environment. “Distance education is not for everyone,” she said while stating her disbelief and skepticism in developing language mastery without coming to class with a teacher and other students. Her viewpoint did not match mine, especially after being able to achieve serval associate degrees online through English but not linked to studying a foreign language. What I concluded was that all this is not about being tough; it is about being different. Online language learning is possible, but it may take longer than a full F2F program.

English language mastery is a process that does not happen overnight; it requires time for a student to develop his foreign language and in turn its mastery. The question over here is “How long does it take an online language student to develop his command of the target language? And this has to be clearly stated that any amount of hours of language exposure is difficult to calculate despite the number of hours publishing houses claim a series of theirs can help develop a foreign language. Any random amount of time only adds uncertainty when one is looking for studies (the map to cross the maze) that can help you match language development and time needed to achieve a given CEFR level.

What I have been able to experience with several online groups of language students of mine is that each phase is different; time constraints are not the same for each step of the CEFR way. Since every step of the CEFR ladder, or as the inverted “so-called” pyramid included below states, it is important to divide each level into a base one and a plus (+) one as already done by the Global Scale of English (GSE) created by Pearson Education (2016). GSE can help us “improve the quality and relevance of [our] English classes” (Pearson Education, 2016). This toolkit can help us visualize the real learning objectives students are to master at a given point in their language development, along with “grammar and vocabulary to help [teachers] plan lessons that are at the right level for […] students (Pearson Education, 2016).

Based on very empirical evidence taken from my ethnographies and reflective journaling and trying to match them with Pearson Education’s GSE, to have language learners achieve a B1+ level of language commands, some 600 hrs are needed. Adult learners who participated in four 45-hr courses for 15 weeks (totaling 60 weeks) of a four-level program could not make it to B1. And why not? The reason has to do with the time needed for consolidation and for internalization of the new content covered along the program. What was witnessed is the importance and relevance of dividing CEFR levels into two (A1 & A1+) to help thee adult students work on course content and achieve language mastery.

As stated above, with just empirical evidence and reflective journaling, 600 hours of language work is necessary to achieve a B1+ for an online student who has a platform and synchronous sessions with an instructor and a class. The way our courses were constituted included 45 hours of synchronous sessions to work on the content that students previously worked on the course language platform. On the other hand, this online asynchronous work is meant to be self-regulated by the learners and monitored by the instructor. And though students probably work some 120 hours on online course content in the platform, they barely have “real mastery” of that content. Autonomous study must be done along with online videoconferencing sessions where they can really get to work the course content in meaningful ways.

To conclude, it is a must to revisit the program structure to modify it to really herd students toward a B1 level. Many more hours of class practice are needed even though these hours are just dedicated to practice. Practicing extensively in tough content for the students is also mandatory to make these hours true learning and CEFR level achievement for all students.

References

Pearson Education (2016). GSE Teacher Toolkit. Retrieved from English.Com: https://www.english.com/gse/teacher-toolkit/user/lo?page=1&sort=gse;asc&gseRange=22;26&audience=GL

Sunday, August 11, 2019

Gray Matter in Data Science: Some Insights in Methodology

#EdChat, Big Data, Data Science, The Data Scientist 1comments

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