DATA SCIENCE

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DATA SCIENCE

  • gcadmin
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    Every day, we generate over 2.5 quintillion bytes of data around the world. Now the question becomes, what do we do with this enormous amount of data? Or, what can we possibly do with it? One, data is an excellent tool for planning and predicting the future of human existence. Think of the importance of fields like, weather forecasting, machine learning, artificial intelligence, cybersecurity, these are areas that heavily rely on data and can achieve little without it. So, what is the importance of data science in the mix? especially in Nigeria where most of our data are either not recorded or wasted. How does securing our data come to the rescue? You can become a data scientist in order to help us, find out!

    WHAT IS DATA SCIENCE

    Let’s start by introducing the concept of structured and unstructured data. The structured data are the traditional forms of data, well organized, of a specific format, and possibly contained in a well-defined scheme like a database. Unstructured data, on the other hand, are collections of data from different sources, hence, they are never of the same format. An unstructured data can comprise of text, audio and video data, numerical data; hence, it becomes increasingly difficult to handle such data. Unlike structured data that you can simply plug into a spreadsheet (like excel) and analyze, how can you possibly analyze an unstructured data in spreadsheet?

    Likewise, there is an increasing need to derive information from wide sources of data. Since, most of the data we generate are unstructured data, scientists had to birth a new field called data science which will see to the organization and analysis of such data. Besides, data Science uses various tools, like algorithms, and machine learning principles with the aim to discover hidden patterns from the raw data. While this is akin to the work of a statistician and data analyst, data scientists create models for making predictions of future occurrences. It achieves this novel feat using predictive analytics, prescriptive analytics and machine learning.

    Importance of Data Science in the Modern World

    Data science is very important in today’s world considering the following:

    • Assume you run a business online, say an e-fashion store, you have customers but desire more patronage. What if, from your customer’s past browsing history, purchase history, age and income, gender etc., you can recommend the product to them with more precision? Wouldn’t that be awesome! That my friend, is one of the amazing things you can do with data science. Now you know why you get advert of sales of products you viewed online even when you are no longer in the site where they are sold.
    • How about, in weather forecasting, data from ships, aircrafts, satellites etc. are sourced and analyzed to build models that forecast the weather but also help in predicting the occurrence of any natural calamities. So, hmm… data science can also save lives!

     

    • Financial modeling

    QUIZ

    Answer the following questions in the comment section below:

    • What is financial modelling?
    • According to your own understanding define data science?

    CAREER PATH TO BECOMING A DATA SCIENTIST

    Fig 2. Data science diagram courtesy data science society

    From the diagram, you will observe that in order to become a data scientist you need knowledge from the field of mathematics, programming, machine learning and domain knowledge of the areas.

    First step: you should start off your journey by understanding exactly what the role of a data scientist really is, what all the paths are and how they all fit together. Find out the different terminologies related to data science and how they all they separate from each other.

    Statistics and Mathematics: this is where you acquaint yourself with one of the most important tools of data science. Acquaint yourself with the knowledge and application of mathematical and statistical theories like, probability, inferential statistics, combinatorics, regression analysis etc., and get a hang of possible ways to apply them in data analysis.

    Programming: this is where you learn to process data science; designing or building a computer program to complete specific tasks. You can learn computer programming languages like Python or R programming language.

    Learning Machine Learning: this is where you learn the science of making computers perform tasks without being direct or straightforward.

    Deep Learning:  here you would learn about neural networks and pick up a popular deep learning framework called Keras or any other preferred framework.

    Computer Vision: This is also very important in the industry these days.

    Natural Language Processing (NLP): Experts are highly in demand and this is a great time to start working in Natural Language Processing.

    The domain area is the area you would love to apply the knowledge of data science to. For example, business, health, government, then, you must have a knowledge of processes and how things work in the area.  Therefore, it follows that, you can be a data scientist in your domain area, be it medicine, law, biology, business etc.

    CONCLUSION

    The learning curve of the data science career is a marathon and not a sprint. Therefore, you must pace your journey. Depending on how many hours you can put in per week, you can acquire all the skills in about six months to one year. Remember, anyone can learn anything, all you need is to begin from a point, take steps, “slow and steady” they say, wins the race.

    ACTIVITY

    Explain the data science career curve

    • This topic was modified 7 months, 3 weeks ago by gcadmin.
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