May I welcome you to the 21st century filled with shocking wonders or are you familiar with a handful of them already?

We have seen a lot unfold in the science and technology world from bioluminescence to hovercraft to the driverless car and even to data science. If we have achieved a great deal in just 18% of the whole century, then I hope I live long to experience more and more of these breathtaking innovations.

We wouldn’t stop being wowed by relevant emerging fields like biotechnology. If you love cocktail so much you would spend your last salary on, then this technology is for you. Biotechnology is a technology that uses biological systems of a living organism to make or induce a process, result or product for basic reasons. Biotechnology is found in the usage of yeast to make bread and even brew wine. Do you see why I said as a wine lover, you’d love this technology?

Another field that is crazily on the rise now is DATA SCIENCE. Now, this starts to feel like rocket science; part of your mind may ask what science has to do with data. Data is a pile of raw; you can call them junks that need to be well structured to make sense while science is the study of the natural and physical world using models and data. Does it make sense to bring them together now?

^{Raw Data from Pixabay under CC0 License}

Why do we have to go through all of this hell of work to major on unending large data? Since data is useless unless or until interpreted, then there is a need for a transformation. What data science tries to accomplish in layman terms is to process raw data into information, where information is a well-structured version of data.

So can we have a professional definition of data science?

Data science is a combination of fields; Statistics, Mathematics and Computer Science where each field uses its knowledge of calculation, processes and computer algorithm to get what could be used to better understand a situation or a process from a data.

I had a data science and AI (Artificial Intelligence) boot camp early this year and prior to then, I had no idea what data science was all about. All I knew then was a little of AI where robotics began. Thanks to an Indian movie I watched which opened my eyes to the world of this great technology. Imagine learning how you can build up a machine from the scratch, do some mathematics, write a bunch of line of codes and get something function like a human. Waoh! What a time to be alive!

Have you ever sat down to think all these could be possible? Like how you don’t have to contact every public institute in your country to get data about a particular health case or how you don’t have to sit to draw a conclusion from yearly data manually about different areas?

Data science comprises of a handful of other areas which are data cleaning, extraction, modeling, machine learning, text mining, natural language processing, sentiment analysis and some couple more.

What exactly can be done in data science?

As mentioned earlier, data science is a huge combination of great areas that affect the everyday life in every sector. Sectors like health, banking, telecommunications, and more have had a massive change. So, what can be done in this powerful and ever-improving field?

Data extraction

We have come to a century where what took a longer period to do in the previous centuries is now been down in a snap of a finger. Data extraction is simply the method of extracting data by scraping from the net or web. As I said earlier, the health sector does not need to run helter-skelter to get data or draw a conclusion about a particular program. All they need do is to retrieve data from data sources online. This data is then loaded to the database of the programming language in use. Extraction of data can also be called web scraping. When extraction of data is done, it comes from unarranged and unstructured data and then cleaning of the data follows. So what is data cleaning?

Data cleaning

When you get let say fresh vegetables from the market, you just don’t go on to cook it that way, you have to wash or rinse. I do my washing with hot water though, makes it kind of softer. If you are a great cook like me, I know you’d agree. Same goes for the raw data we get online. In that sense, data cleaning is finding ways to work better on your data. Your data can’t be looking all rough and expect to get quite a decent result or output. Let me break it down here, when you scrape the net for data, you can’t have a complete dataset. Somehow, some things would be missing. Take a data about the prediction of weather which has years, months, seasons and some other factors divided into rows and columns in the dataset as an example. More often than not, we do not have a complete dataset whether in a row or a column and this hinders our analysis in whether modeling or some other analysis. In order to ensure we have a smooth planning, data cleaning is therefore a priority.
Let move on right ahead to data modeling

Data modeling

Continuing with our vegetable gist, there is far more than one way to cook the vegetables, whether briefly cooking or ensuring it takes enough time. In the long run, we’ll have our vegetables just in two different varieties. In data modeling, what is done here is to use ways or processes to model a data. After your raw data has been scraped and cleaned well, what has to be done next is modeling especially if you work for a data-using industry. As a data scientist who models data, you help to build a relationship in the company by informing them of how well their products can still behave. Companies that have a remarkable record of been of top performance use data modeling to further predict how well they would survive in times to come. For an upcoming or established manager, ensure to employ the use modeling. It is sure to save you time and expenses. This is the 21st century!

Sentiment analysis

Sentiment analysis has found a big use in the product or servicing industry like Twitter, Airtel, Facebook and other telecommunication industry. Sentiment analysis is based on people’s opinion and what they publicly say about a product and service rendered. For example, Airtel, a telecommunication company in Nigeria uses sentiment analysis to get people’s say or opinion about their services. This works about by lines of codes written in languages about how to identify negative and positive tweets from their users. After identification, a graph or chart is created by a command to see the results. A conclusion is then made whether the product or service rendered is satisfactory. This particular analysis has yielded result and it helps companies make decisions on how they can better render their services.

Text Mining

Text mining as the word implies is the process of looking into a large resource maybe book, article, or post that are usually unarranged text data to know what it is about or to gather information in the process identifying topics, keywords, a particular pattern and some other characteristics. If I take for example Romeo and Juliet the book, I will be able to know what exactly the book is all about without reading the book at all. This process is done by downloading the book and calling it in with special commands to check. An analysis is done on it and as I will always say, a few lines of code are written and it arranges the most used words in the book. This especially would be good for books lover in that it gives them easy access to any resource they need. Some bloggers are been arrested in some countries due to what they publish on blogs. Text mining is to be thanked for this since security agencies have started to employ the method. Text mining is for sure going to render the traditional keyword search ultimately useless in the years to come.
Other cool stuff that is done in data science are natural language processing, machine language and some other cool stuff.

Finally, where and how has data science found usage in the ever-rising data world?

By now, you the reader and I cannot take a blind look at data science even with the little I have discussed. As aforementioned, data is used by telecommunication industry, product and services industries, the stock exchange world, security agencies, and even our most precious health sector. For proper understanding, I’d point out some very interesting areas that have embraced this jewel and could go heartbroken if it doesn’t exist again.
The areas are but not limited to

Health industry

A whole lot of doctors aren’t always happy seeing same people they had treated before. It sometimes shows a lack of proper care by the patient or incompetence from the doctor’s side. How can this gap be bridged? Hospitals and health centers have now started to look into using data to predict why a once treated patient can be readmitted. The hospitals check through patient’s record like crime rate, salary or income, occupation and address to get a clear perception about the reason behind their readmission.

Companies- Services and Products

Gone are those days when traditional methods of advertisement thrived. People ran from a place to another so as just to sell their products off. If you are a Nigerian or live in Nigeria, you’d relate well with this. Data science while coming up saw this and said ‘Say no more, we’re taking advertisement worldwide’. How is this possible? Data analysts or data scientists can carefully sit with the data they are presented and pitch their products to a chain of individuals as per inference drawn from their analysis. This has helped companies focus more and not punching the air. A company like Amazon uses your past purchasing records to notify you of products that are related to the ones you bought in their history. Companies use bot to attend to their customers and Steemit too has a whole lot of bots in its database. I had an online party on discord last week Friday and with the tracks discord’s bot played, I must say data science saved my Friday night. LOLx

Social media- Image recognition

If my late grandpa was told he could unlock an IPhone with face recognition, he would have planned to live longer than he did. Data science is behind IPhone users unlocking and locking their mobiles with their faces. More usage is found in WhatsApp web. WhatsApp web is used on a PC to connect your mobile based app with the system’s. All you need do is to scan a barcode on your PC’S browser with the help of your mobile. Also, take a look at Facebook, once you upload a picture with a couple of friends, the system immediately suggest the names of those it thinks are present in the image and it always comes out with the right suggestions. I, of recent read that Facebook was planning to make its users save the stress of incessant logins by introducing facial recognition to their system. So now, the influence hackers think they possess over Facebook accounts would reduce drastically! All these are owed up to data science.

Telecommunication industry

May I tell you that here in Nigeria; we have seen the rise and fall of different telecommunication companies. The fall may be traced back to a target customer goal not been achieved. In data science, we have a process called sentiment analysis. Airtel, MTN, and Glo as a telecommunication company in Nigeria, for example, use twitter majorly via API (Application Programming Interface) keys, tokens and some lengthy line of code to draw what people have said about them thus far. The result shows the negative or positive words as coded from the start. The words are drawn from what people tweeted for a while as also defined in the coding. This simple and sure method has helped companies stay in business.

Other areas that use data science as a progress route includes prediction of a good location for products or services rendered, speech recognition, airline industry, stocks.

Conclusion

Questions may arise on how you can start off a career in data science or even joke around it. It’s pretty simple. Let take a photographer as a case study, all he/she needs as an amateur photographer is the zeal to watch people do their funny poses and take a shot, a professional camera and probably some tutorials. With time he becomes something of a guru. So also is a data scientist, you must love to solve problems, find solution to seemingly uneasy tasks, and the time to keep practicing even with few jobs coming in as well.

^{Articificial intelligence and man from Pixabay under CC0 License}

A friend of mine named Joseph graduated as an animal production and health scientist but immediately on hearing about the Bootcamp embraced it and he was the first to apply. He foresaw the opportunities that awaited him if he took part in the training. Fortunately, he’s more fired up to stay on data science’s part to a better future than I am. He as at the last time we spoke works with a data science company in Kano, Nigeria. He has also had work flow in from Fiverr and Upwork and got paid well. That is just on the flip side, my main aim of putting up this story is to tell how easy it is to start up a career in data science.

In the programming world, HTML, CSS, JAVA and some other programming languages are used to write codes that give us our well-designed site like Steemit. So also are there languages used to code in data science. Two well-known programs are Python and the R. Personally, I use R to code my analysis and I would show us some things using R programming language in a future post.

Thanks for your time. Don’t forget to embrace DATA SCIENCE!

Sources

• Quora
• Wikipedia
• Techopedia
• Medium
• Toward data science