We are aware that Data science, no doubt, is considered as the sexist job of the 21st century. Also, the demand for data scientist job roles is increasing every day. As of 2020, the average data scientist in the US makes over US$113,000 a year. All these happen since we are living in the age of data explosions. Data is omnipresent and is also a key that fuels the digital transformation of the world by relying on modern technologies like Artificial Intelligence, Machine Learning, IoT, and Big Data, and so on. Recall the time your keyboard gave you auto-suggested words while typing, or you saw an ad of antique clock pieces on Facebook, which you were actually searching on Amazon a few days back. These are some of the real-life applications of data science.
Data Science is basically a common term. This branch includes several scientific methods, math, statistics, and other tools to analyze and manipulate data on a variety of models and raw data to get information. The role of a data scientist revolves around includes data mining by using APIs or building ETL pipelines, data cleaning using programming languages like R or Python, examining data from multiple disconnected sources, and many more. But have you ever wonder how you would explain the job roles and responsibilities to your children? Or what would you say if a school goer kid (or any data science ignorant) asks you questions about data science? Well, maybe we at Analytics Insight can help you here!
Here is a fun activity to teach kids some of the critical concepts of data science or processes involved in Big Data.
Data Collection: First, ask your kids to collect any commonly used object or thing is your house (legos, leaves, clothes, utensils, etc.). Let us consider a pile of clothes in the house. Make sure it is a collected heap of different types and colors of clothes. Also keep clothes like onesies, sock-boots, stocking, jumpsuits separately hidden.
Data Segregation and Analysis: Ask your kids to take out any particular type of cloth item as per their choice. E.g., one can select to topwears, while others will work on bottomwears. Then segregate the cloth chosen items into the different piles yourself. Ask your child to observe why certain solid print t-shirts fit in one collection while jeans go in another group, notice the characteristics features of these clothes that set them apart. Then ask them to explain why they choose to put all the clothes in a different pile and the key features they used to sort them out.
Data Patterns: Start stacking the items on the basis of a color code—E.g. White-Black-Blue-Green-Red-White-Black-Blue, and so on. After finishing a pile of 4-5 clothes per piece, ask them to think what pattern you think the next five items will be in. After they are able to guess the correct answer, encourage them to sort clothes by themselves now. If they are not able to guess right, have them keep guessing until they get it, or give them a hint that they may have already seen the objects.
Afterward, you can add clothes with other colors or different print patterns and ask kids to categorize them too by repeating the previous process.
Random Data elements: Introduce the previously hidden cloth items to the pile. After the randomly set of hidden clothes are revealed, question kids about the type of pile it can be added to. E.g., take a pair of jumpsuits can ask if it will fit in the t-shirt pile or pants pile or both or a totally new pile? Let them know that although by appearance jumpsuit looks different, it is also a part of clothes and hence has its own unique use too. Though this step might seem tricky and confusing to kids first, the more they are allowed to practice piling clothes in separate heaps, the better they can understand it.
Data Forecasting: Ask your children to take a pause and imagine a situation where they need to use seasonal clothes like sweaters, raincoats, woolen caps, etc. Then if possible, add them to the existing unsorted pile. Explain to the kids why they think people need to use the seasonal clothes and how sweaters, sweatshirts can either be put in a new pile or added to the t-shirt pile. Keep encouraging them to sort clothes, themselves until they have got all clothes neatly stacked into distinct piles.
Data Visualization: After the completion of the previous steps, question the kids to take a moment and recall the patterns they followed while sorting the clothes and how accurately they adhered to it. Draw a graph to illustrate how they did the activity process and highlight where they made mistakes and suggest to them how they can improve. Keep track of these results to encourage more practice trials. Chances are they will finally understand the whole processes involved and get better results almost every time.
We never know that the next round of Data scientists who will be filling our shoes are right in our house, complaining over school homework, running around with stray dogs. Whether they become data scientists or not, having these skills on a resume is beneficial. Also, studies tend to show that most people learn best by doing, so why not start with simpler activities at home.
