The Data Science Journey Begins. Data Science 101 Beginning begins
By: Temidayo Adeniyi
Tags: Learning Data Science, Intro To Data Science, Getting Started, Data Science, Data Science 4 Beginners

Its another session, going by our schedule and time arrangement it is meant to be Question and Answer day, but the mid-week session happens to be Holiday break and we deem it fit to ensure that we as well permit other to observe and enjoy their relaxation been that all through the week they are working all round the clock.

Hence, this time we had another time with one of the community leads, by name is oni Stephen and He walks us through the basics and set us up without further delay.

catch-up with the whole session right here as we recap that for you all to follow through.

Stephen started by showing concern and ask how our weekend is going and also do some few check if we have new community member joined since the last meetup to know how to get them up to speed plus if any of those currently within do any research during the course of the week or do any further reading around data science to share with us the knowledge obtained and we all learn together.

He also asks since by default it is meant to be Question and Answer day if by chance anybody has questions and we can tackle that before venturing further into the topic of the day.

We are encourage to have someone at least from the community that let us know that she read something about Neural Networks and we ask that she share what she read during the course of the week with us, while she is putting things together to ensure that we all get her summary and due to time, Stephen kick-off with a little revise/summary from the last meeting.

Last week, we discuss the tools used for data science, where we made mention of Microsoft Excel, SPSS, and others.

Actually, these tools are old, but they are still very well relevant. Statisticians still use SPSS and Office Data Analyst still use Excel, But in all Python and R are the major game changer that is been used in Data Science world. And now R is majorly used by updated Statisticians while Python is majorly used by Developers, He could not agree more than to go with Python simply because Python can do more of what R will do and even covers more of the areas that R is lacking or not covering with so much support that it harnesses too.

By this time we have a new community member joined and she introduced herself and in other to maximise the time we as well seize that time to also accommodate Eniola as regards the summary of the neural networks she read during the week.

Stephen goes further to let us know that The field of Data Science is a little bit broad. He let us know that we have the Data Analyst who analyze data in other to gain an insight into the data, then he further ask us to check on this article to give us an insight and understanding of what he meant by that. https://steveoni.github.io/s_good and this one here too https://medium.com/@steveoni/premier-league-visualisation-26a4b29d8c47

Furthermore, he states that we as well have the Machine learning engineers and explained that they are those that create a predictive model from big data and bring it to production, such as the recommendations engine and the likes. He promises that we will consider the rest in the course of the next session.

In his word: Now, let get to business.

The best point to start with is from data analyst point. For us to create a predictive model or use the data we acquire effectively we must know how to make an inquiry into the data and get insight as to what the data is all about.

So in the likes of tools, the tools used to analyse data are Pandas, Matplotlib, Seaborn. taken them one after the other.

Pandas is used for data manipulation. Most of the real-life data are not organised or well structured. Pandas help in organizing data and shape it into what we desire and then gain insight into them.

Matplotlib on another side help us to represent data in a visualised form.

A little digression, right at this point, Eniola summary comes in and in her word put thus:

Summary of what I learnt:

Neural networks involves the use of data sets to train a computer to predict future outcomes. Neural networks identify similar patterns and correlations in data sets using a particular algorithm, from which it gives out significant outputs.

So in other to put the house in order, we then take that in and put on hold while we continue to come back to the summary.

oni Stephen continues to elaborate that One important stuff about data science is that.. the only thing computer understand is finite maths.

So because of this, almost everything you do in data science, from your tabular data to image, sound e.t.c that you will manipulate are represented in terms of a matrix.

But don’t be afraid if you are not skilled in maths. You will get to understand the type of matrix you need has time goes on.

At this very point, some resources were shared to get the community on a ride for good. The first resources to get us up and running was share thus:

For the matrix manipulation, we will be using numpy.. yet you don't need maths, all you need to know is what goes in and what comes out.

Pandas: https://pandas.pydata.org/pandas- docs/stable/getting_started/10min.html

Another resource to get the community running on basics of Python needed to fly was also given right and here is the link to the resources: http://cs231n.github.io/python-numpy-tutorial/

From the link learn about python,Numpy, SciPy and it contains some cool stuff on manipulating an image in terms of a matrix.

Moving on the community was also giving this:

And for Matplotlib:https://stackabuse.com/a-brief-introduction-to-matplotlib-for-data-visualization/

So, to ensure the community is on the track oni Stephen further highlight that:

All this(shared resources) will be done in python. He as well asked if everybody in the community know how to install python.

Once you have installed python, then install anaconda. Anaconda contains every tool needed. and quickly add that every of the community member should inform the community of any challenge they might face in going through the resources shared and installation and in his word it read thus:

let’s know when you face any challenges.. we are here to help.

Temidayo, take a random sample on feedback from the community to measure interaction, presentation and validity of delivery and resources shared.

Adeniyi Temidayo put it thus: This feedback is the data that help us to know and channel how the sessions are presented, and to also buttress the fact that we consider absolute beginner and enthusiast first… So to some, it might look like we move slow, but then we move in a sequential pattern such that we can go back in time and generate a comprehensive note from there sessions.

Remember we put on hold the summary of Eniola? Okay!

Temidayo, then revisits that to further break things down.

Taking us back a bit, to clarifies some few things. Can we relate with this as Eniola😇 puts it, collected data, let’s refer to the first session, after study (analysis) and derive/make sense out of this. These data were then fed into the computer to train with(analytics).

(analytics) predicting future outcomes based on past events(avenue for which we are able to collect data).

From what is observed the neural network then observed a patterns (something in common) and it correlations and with algorithm (simply out a pattern of order to follow, to as well achieve good/best results among other obtainable results, we will understand algorithm better but let’s keeps it simple at this very stage), then a significant output that looks perfect or with less/very minimal error.

At this very point, Eniola cuts in to ask a quick question and her question is as follows:

Let’s assume you want to analyze a particular research work but you don’t have access to data.

Is there any technique for data prediction/generation with a good level of accuracy?

To get clarity of the question, Adeniyi Temidayo probe further for clarification, by asking for clarity thus, do you mean access to the data used in the research work or data in general? then, she clarified that she refers to Data for the research work.

response and answer given by Adeniyi Temidayo to the question asked by Eniola are as follows:

1./Getting data from the research work will be very relevant and good data but in the case, no access, personal point of view, is to adapt the research work with very similar data that can be used such that we can have some sort of near accuracy, but data is still needed and something very well similar or best close to the research work will be of paramount use.

2./Data must exist one way or another and/except for playing with that is when you can just generate and as of this moment I don’t think I have heard of anything like data prediction/generation without data in any form.

Then Joshua, another lead of the community shared more point in other to give a good response to the question thus;

Joshua: Hi Eniola,



Although there are tools in some software like SPSS, R and Excel that facilitates data generation(using simulation techniques) for known statistical distributions e.g Poisson and uniform distributions, the approach is not feasible for all scenarios.

For example, data can be generated for some sort of research work on the queuing system since we know that the arrival process, for example, can be random (Poisson distribution) and the service system can be exponentially distributed. However, in situations where we can not guess the distribution of the data, we need to utilize the conventional methods of getting data.

The kind of research should also be factored in. If you wanted to carry out a research on, let’s say, “Effects of social media usage on academic performance of freshers in CU”, then it only makes sense to get data from primary or secondary sources. 🙂

If you’re still curious, check out data simulation. And you can also check out common statistical distributions.

Regards

The session was brought to a wrap with Action point expected of all as follows:

[Update/ActionPoint] Let us all go through the resources shared over this weekend and be done with by Monday. then later in the evening on Monday let have challenges faced and we will try as much as possible to proffer support. this one from the weekend till Monday spilling over to Tuesday.