Is Data Science Really Worth it?

Audacious Coder.
5 min readFeb 29, 2020

Hello Fellow techies and tech enthusiasts, just landed into the year 2020 like …

Let's have a catch-up session from last year where I decided to dedicate this blog to AI and its branches. We covered Introduction to Artificial Intelligence. In this beautiful decade, we will dive deeper into what AI constitutes and how we can build models that can help solve worldwide problems. We will now get started on data which is a crucial component of this study.

What the hell is this data everyone talks of?

Data is defined as quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media. Hmmm, thank you guru99 for making life harder than it is already.

I would personally define data as fuel for the future. It is like the mitochondria of a cell #bioreference.🌚🌚🌚

Why is data important?

  • From when we began to use electronic devices, we have exchanged large amounts of data. As the world is fast growing we need to know how to use this data for businesses and in health, schools, agriculture. Come to think of it 90% of companies rely on client data in order to improve the user's experience in order to retain their clients hence increase revenue.
  • Another reason why having data is important is that it simplifies work by speeding up the decision-making process. This is whereby you can decide to develop visualization graphs based on the data and derive insights fast that will impact the business depending on what you wanted to gain from the data.
  • Data can also enable a business to understand its user’s preferences whereby the more users purchases a good can tell the sellers may be to increase stock as the goods bring in high sales.

So in order for this data to make such accurate and dependable information, there comes the back end scientists who deal with this data... These people are called Data scientists.

What is data science and who is a data scientist?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Data scientists are professionals responsible for collecting, analyzing and interpreting extremely large amounts of data.

What skills do you require in order to use the name data scientist?

  • Statistics.
  • At least one programming language — R/ Python.
  • Data Extraction, Transformation, and Loading.
  • Data Wrangling and Data Exploration.
  • Machine Learning Algorithms.
  • Advanced Machine Learning (Deep Learning)
  • Data Visualization.

Briefly explaining the other roles mentioned above.

A data analyst’s job is to take that data and use it to help companies make better business decisions. This could mean figuring out how to price new materials for the market, how to reduce transportation costs, solve issues that cost the company money, or determine how many people should be working on Saturdays.

Machine Learning Engineers are required to have exceptional mathematical skills in order to perform computations and work with the algorithms involved in this type of programming. Communication skills are also important because machine learning engineers will need to explain their process to people who are not programming experts and some positions also require machine learning engineers to publish articles on their work. Strong analytical skills are needed since the work that machine learning engineers do involve projecting outcomes and isolating issues that need to be resolved to make programs more effective.

Data Engineers' role is to conceptualize and generate infrastructure that allows big data to be accessed and analyzed, reformulating existing frameworks to optimize their functioning, testing such structures to ensure that they are fit for use, preparing raw data for manipulation by Data Scientists and detecting and correcting errors in your work. These roles may differ in each business.

From this, we can say a data scientist is a full package.💪

Major Advantages of Data Science

  • It's high in demand. I t is the sexiest job in town.
  • No more BORING tasks. Once you get a dose of understanding data, it becomes a hobby.
  • These ninjas are highly paid due to the amount of work that needs to be done.
  • Data science makes products smarter as they get insights from past client responses on a product they can recommend better products and make smart decisions in the future.
  • Data science can be used in each industry. You need not to major only in technology. With knowledge in finance, health, manufacturing, insurance, agriculture and the rest if not all. You can dive into data science and make a difference in your area of specialty.

Cons of Data Science.

  • It is freaking broad. To be a full-stack data scientist (as seen in the diagram above), Lord have mercy on your brain cells😂but nothing is impossible.
  • Data privacy -in case of a private data leak that may endanger the company or an individual or data falls into the wrong hands, you will be the individual under investigation.

Generally, speaking from experience. Data science is an awesome career path. It sharpens your critical thinking, creative thinking, and problem-solving abilities. It also opens up your mind to a world of many possibilities.

Lastly, if you want to join the field of data science, I recommend these sites(Coursera and Udacity)which offer free courses and scholarships for learning data science. Hope you enjoyed this brief article. See you next time as we dive deep into the world of data.

NB. You are in the +254 and you are looking for an institution to help you become an intermediate data scientist hands-on, I recommend Moringa School data science course. Moringa School






Audacious Coder.

Hyper-active Data Scientist | STEMINIST | Neuroscience Enthusiast | Dancer |Writer of DS/ML articles😄