The Hype of Data Science?

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The Hype of Data Science?📊📈

In today’s modern era, everyone hears about few buzz words repeatedly such as AI, Machine Learning, Deep learning, and even many more across various social media platforms, newspapers, and from friends.

Back in 2012, Harvard Business Review stated: “Data Scientist is The Sexiest Job of the 21st Century.”

Due to the propaganda, excellent salary, and demand for jobs most of them have unrealistic expectations on this field what is about.

Further, there is a myth about data science is only about building Artificial Intelligence, Machine Learning, and building Deep Learning models all the time.

My Experience

Many connections and friends often will reach out to me from LinkedIn and various social media platforms to query Data Science.

When I tried to interact with them, the first question I ask ‘why did you choose this field as a career?

The straightaway answer will be, ‘I want to become a data scientist.

That is okay! but Data Science does not only hold a Data Scientist position. It consists of other roles such as data analyst or data engineer.

Further, they do not even know properly that data science comprises various aspects such as programming languages, statistics, mathematics, and many more.

The surprise factor is that most of them already started their path by pursuing a master’s program / online course in it.

But in Reality!

Data Science generally holds multidisciplinary, including Data Engineering, Data Analytics, Machine Learning, Predictive Analytics, Software Engineering, and even more.

Most of the task in data science is about data collection, cleaning, and storage which will account for at least 70% of the work.

Data Scientist / Data Science is not about all-time performing Machine learning, deep learning models. Data Science is about understanding business problems and solving them using appropriate tools and techniques.

Source: Karthigainathan

Before considering Data Science put these questions in front of you? 🤔

· Are you enjoy working with the huge volume of data and find meaningful patterns from it?

· Are you good at Statistics, Probability, Mathematics?

· Are you good at querying large volumes of Data?

· Are you good at programming language?

· Are you good at solving real-world problems?

· Are you a proactive learner?

To be an expert in this field one must be ready to harvest time, effort, energy patience, interest every day or until you go away!

Data Science is not simple, and no one can master this field within a day or week. Involves regular exercise of various things like programming, understanding the algorithms, brushing up stats and probability, and even more.

Understand your area of interest. For ex. If you like working on data pipelines/collection/storage, then choose as Data Engineer. If you like querying the database and providing insights choose as Data Analyst. If you love building models, math and programming choose as Data Scientist.

Source: Karthigainathan
Source: Karthigainathan

Start to work on doing actual data projects on the topics or domains you love. Gather the data, clean the data, and do some analysis to solve a business problem or use case. Make the groundwork solid and steadily carry over to build ML models.

For practice and preparation, you can get all answers from your buddy! Who?

Source: Google

Use appropriate keywords to search and you will get the solution.

In the end, Data Scientist is just a title word, it will not bring any glory until you are having enough knowledge. Experience and knowledge only will bring value and remain forever but not the fancy title.

The role and duties of a Data Scientist also differ from firm to firm, there is no static assigned of tasks.

Currently, the demand for Data Scientist jobs still resides but, in the future, more roles will be available for Data Analyst/Engineer. This is because due to the evolution of various APIs and Auto ML in the market each day and most of the tasks done by Data scientists will be replaced by advanced APIs by having minimal manpower.


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