The welcome party recap Pt 2
By: Temidayo Adeniyi
Tags: Analysis, Data, Data Science, Data Visualization, Analytics

If you missed Part 1, you can check here for it so that you get a full grasp of the discussion going forward.

We had an awesome session during our first virtual meetup. Participants asked questions and got answers for all, and the facilitators made known some facts to the participants. This is a breakdown of the whole event. Check it out.

Since it was our first meetup, we expected that participants ask questions as it relates to the subject matter. More like, questions any inquisitive person will like to get answers to, before embarking on this Data Science journey.

Responding to questions from community members, the first question from Bayo one of the community member.

Bayo: Is there a special tool(software) needed for data science?.

Adeniyi Temidayo: Taking care of data, arranging or filtering them out can be achieved with Excel while studying and checking how valid or noting pattern can be achieved with some few software with coding via languages like Python and R for efficient use and portability.

Meanwhile, he asked another facilitator to buttress/answer this question too. Find Bunmi’ response below:

Jolayemi Olubunmi: “Old but still relevant tools: SPSS, Excel, etc

Modern tools: Data analysis using Python, R programming language, etc. They have useful libraries embedded that aid data analysis processes and help achieve results faster with less stress. But of course, you need to learn and understand these programming languages to use them and get expected results”

Bayo confirm and acknowledge satisfaction of the response given,

In the absence of questions more questions from the community, Adeniyi Temidayo chose to ask few questions. One of the questions asked is;

“what is readily available data that can be extracted or obtained from a hospital and will as well be relevant in other hospitals that can help in the quick diagnosis of patients?”

Few supplied responses are listed below:

Patient Name

Patient Address

Patient Age

Patient Health Conditions History (like BP, weight, height, sugar level, scan etc)

Adeniyi Temidayo butressing his point. Find his comments below, on questions and valid point that can be drawn from the participant’s response;

so look into this provided data, we need to split these data into chunk to study them individually. Age is necessary if we need to check to see if a particular disease is age-related or specific to a particular age range. Blood Pressure is another one, what are the no-go areas for some BP values, what are the safe area for patients that exercise daily or a sportsman/women? when is weight an issue with regards to age for a patient? is a patient height a function of the hormones? when is sugar level normal for an average person, what is the safe sugar level for old people, what is the range of sugar level for a person that treks or most time on the move that keeps burning sugar when it is available and when it is not… these are the questions that will possibly help to examine each data in their different chunk and later look into which among them is related and can be used together, such that we can see and identify trends and pattern. ( further study might require address but the geographical location will be the right word to use in that case).”

This discussion wraps up the session. It was an awesome session, looking forward to the next.

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