‘Data’ Science and the Modern World

Someone asked me to explain data science to a 6-year-old, and this is my attempt at it… Denzel, do you realize that your teacher has a register for checking attendance, and the school has information on your parents (like their contact details) and knows your location? All this information is what we call “data.”

Data Science is like a magic tool that helps the school use this information to make smart decisions. For example, based on details like where you live, what your parents do for a living, and the kids’ ages when they start schooling, Data Science helps the school come up with ideas to encourage more parents to bring their children to school and keep them there. It also helps the school figure out how to earn money to improve things at school.

Let me tell you a fun example from my primary school. They had a cool policy for families with more than three kids — they only had to pay half of the school fees for the fourth child and onwards. That was possible because of Data Science!

I’m excited because I got the chance to teach the basics of data science, and I’ll be sharing all my lesson notes on my Medium profile. You can join me on this journey!

designed in Canva by me

I will start by explaining, individually, ‘Data’, and ‘Science’

Data — traditionally (from my junior high school class) is a collection of raw facts and figures which are meaningless. Information — is processed data that is meaningful. Information is the by-product after data has gone through the information processing cycle. From data input, processing, storage, distribution, and outputs. (Tell me why people use data and information interchangeably! But in our defense, these are textbook definitions that hold ‘some’ truth)

Today, I would describe data as a record of events that have the potential to give a snapshot of the present, explain the past, and predict the future.

Think about the texts you input into Google search boxes, numbers representing account balance, pictures on your Snapchat history in 5 years, and clicks on a YouTube video.

Science — Science is the systematic approach to obtaining knowledge through observation and experimentation.

Data Science is an interdisciplinary approach to extracting knowledge from records (data) and channeling the insights into driving business decisions. In simple terms, data science involves deriving actionable insights from data.

The observation and experimentation aspect of data science asks the ‘how’, ‘why’, ‘who’, and ‘what can be done with the results questions’; the actionable answers will then drive business decisions.

The interdisciplinary nature of data science mainly encompasses statistics, mathematics, computer science, business acumen, ethics, and storytelling among several other fields.

You know it is Data Science when…

image from iStock

1. Fraud and Risk Detection: Banks need to assess the propensity of a customer to pay back their loans or default. Also, banks need to profile customers to validate their documents and identities to prevent impersonation and the use of falsified documents—data science through the fraud and risk detection systems levels these major banking and finance hurdles.
2. E-commerce sites like Amazon can collect statistics on the most and least-rated items which inform the customer base on the right things to purchase based on their popularity amongst other buyers. They are also able to create and present targeted ads based on the customer’s search history.
3. In healthcare, quick and accurate diagnosis has been the goal, and through data science applications several apps and tools have been developed which achieve these results. Google developed LYNA, a tool for identifying breast cancer tumors, there are tools for tracking menstrual flow among other luxury healthcare products. There are more exciting fields like patient-specific healthcare delivery which stems from the fact that no two patients are the same and so doctors now seek to provide medical solutions based on the patients’ lifestyle choices ( active, sedentary), tastes, and preferences ( material choice titanium, wood), among other considerations.
4. In transportation, Google Maps can give a heads-up about traffic conditions, and re-routes trips to help drivers save fuel and time because of a choke of vehicles in one part of town.
5. In the field of sports, with sensors, cameras, and data being generated on the field by athletes while playing, scientists are able to develop training regimens to optimize their performances during play times. The data being collected could be as intricate as water lost during training through sweat or the particular spots on the fields to stand to make the best shots.

All these applications may combine two or several of the following data science implementation techniques

1. Anomaly detection — This groups the data into clusters or groups and it can spot outliers. Consider the fraud detection system, things like signatures and original files have noticeable characteristics, so if any file falls outside the normal (which is pre-defined by the engineer), the system points it out.
2. Pattern recognition — Similar to anomaly detection, this system associates actions and shapes and categorizes them. Retailers can recognize trends in Nike purchases in Ghana or products that would be of interest to a specific demography. Think of tumors being identified or how Spotify or YouTube recommends ads, videos, etc
3. Predictive modeling — based on customer’s past choices and preferences, the model can predict or pre-empt their next line of action. Anyone who has defaulted on loans in their past lives might be in a position to default again given specific parameters.
4. Recommendation engines and personalization systems
5. Emotions and behavioral analysis

What is Data Science Used for?

Data science is used to study data in the following ways: descriptive, diagnostic, predictive, and prescriptive. I explained these concepts as a bonus in my Attempted Designing a Data Architecture post. Check it out

From a beginner’s perspective, what would you have wanted to see in your first-ever data science article?

Resources

1. AWS What is Data Science?

2. IBM Technology What is Data Science?