Data Science | In Simpler Words

What is Data Science? Photo by Michael Soledad on Unsplash

Here we go with another buzzword, Data Science. The enigmatic field purely designed for Math geniuses, and is more of a conundrum to those who don’t like dabbling with calculations. Before making these conclusions on this field, why not read through this article to gain a better view of it. Let’s get started 🚀

Definition

According to Wikipedia, data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Not minding these buzzwords, it can be simply referred to, as the study of data which involves developing methods of recording, storing, and analyzing data to effectively extract useful information.

The goal behind this field is to gain insights & knowledge from data, which could be structured or unstructured

It is related to Computer Science but is a separate inter-disciplinary field. Computer Science involves creating programs and algorithms to record and process data. Data Science, on the other hand, covers any type of data analysis, which may, or may not use computers. This field is closely associated with the mathematical aspect of Statistics.

Demand

Everyone wonders why Data Science is in high demand, and to be honest, it is. This is evident due to the exponential rise of companies seeking for mavens in this field.

Now, to the question, “Why is it in demand?”

This is due to the fact that modern organisations generate a huge amount of data on a daily basis. The application of Data Science would help in generating meaningful insights from them.

The Field’s Cycle

There are 5 steps involved. They include:

a. Capturing

This is the first stage and involves the acquisition, entry, signal reception, and extraction of data.

b. Maintenance

Activities involved in this stage include the warehousing, cleansing, storing, and processing of data. It also comprises of data architecture.

c. Processing

The mining, clustering/classification, modelling, and summarization of data are performed in this stage.

d. Analysis

This area plays a major role in gaining meaningful insights from data. Techniques involved include exploration, predictive analysis, regression, text mining, and qualitative analysis.

e. Communication

And now the final stage 😤. It covers pretty advanced concepts such as data reporting and visualisation, Business Intelligence, and decision making.

Who are the Mavens in The Juicy Field?

Wondering why I used the word maven, right 😅? Well, people who are proficient in this discipline, are a peculiar blend of mathematicians and computer scientists. Fascinating, right 😍?

Now, what roles do they play?

They can take a number of roles. This is also due to the fact that Data Science combines several disciplines. Roles in this field could be that of a:

a. Data Scientist

b. Data Analyst

c. Data Architect

d. Business Analyst

e. Data Engineer

f. Database Administrator

Tools & Languages Used

Thinking of diving straight into this field? Curious about the tools, languages, or technologies utilized? Well here are a few languages and technologies Data Scientists get their hands dirty with:

  • Python
  • R
  • SQL
  • Julia
  • Scala
  • Tensorflow

If you’re looking into Data Science, or an established one, I’d recommend you read this detailed article on most tools used 🤗

Conclusion

Data Science truly remains an attractive, tantalizing, and enticing field, since it’s fueled by big data and AI, and having knowledge of it could prove to be helpful

Know someone who keeps asking a plethora of questions on this topic? Kindly do them a favour by sharing this article with them ☺️

Software Engineer && Technical Writer