Informative Story

Let us explore the world of data science.

In short, data science is about extracting meaningful information from data to answer questions such as “what happened”, “why it happened”, “what will happen in the future,” and “what actions should we take. ” It involves applying a triad of knowledge (computer science, mathematics, and domain) and is often practiced by professionals such as data analysts, data scientists, and machine learning engineer. Data science is often applied in fields such as healthcare, finance, and business, though it can be useful in pretty much any field.

As you can probably tell from the very brief description above, data science is a cool field to get into simply because of the knowledge and the skillset you’ll gain and the things you’ll be able to do using them. I believe that the best way to study data science and to get into the field is to let passion guide you, so let us dig in a little deeper to find out what exactly is so cool about data science.

What Is Data Science?

Data science involves roughly three categories of knowledge: computer science, mathematics, and the domain knowledge.

• Computer science knowledge
  How to manipulate your computer to make it do what you want it to do. e.g., how to use a programming language to manipulate data
• Mathematical knowledge
  How to use mathematical concepts to analyze data.
  e.g., how to use a mathematical model to model data
• Domain knowledge
  How to correctly apply your data science skills to the field of your choice
  e.g., how to look at financial data

These categories of knowledge combine to let you work with data, which involves collecting, cleaning, analyzing, interpreting, and presenting data.

• Data collection
  Collecting the data you’ll need to answer the questions being asked.
• Data cleaning
  Formatting the data in a way that is fit for analysis.
• Data analysis
  Analyzing data using various data science tools and techniques.
• Data interpretation
  Interpreting the result of the analysis.
• Data presentation
  Presenting what has been deduced to those who asked the questions.

Some of these steps may overlap and/or iterate.

When Is Data Science Used?

Simply put, data science is used when questions are asked. There are four types of analysis to answer frequently asked questions: Descriptive, diagnostic, predictive, and prescriptive analysis.

• Descriptive analysis
  Answers “what happened?” by looking into past data.
• Diagnostic analysis
  Answers “why did it happen?” by finding patterns and relationships in the data and identifying causes of outcomes.
• Predictive analysis
  Answers “what is likely to happen in the future?” by predicting the future based on the patterns and trends in the data.
• Prescriptive analysis
  Answers “what should we do?” by determining the best next move based on the result of descriptive, diagnostic, and predictive analysis.

Where Is Data Science Used?

Data science can be used anywhere data exists, and most anything can be turned into data. Some of the obvious applications of data science include:

• Healthcare: Predicting disease and personalizing treatment
• Finance: Assessing risks and detecting fraud
• Business: Analyzing customer behavior and personalizing ads
• Education: Analyzing student performance and improving curriculums
• Transportation: Modeling traffic patterns and optimizing routes

Who Works in Data Science?

Data science professionals may have position titles such as:

• Data analyst
  Analyzes data to answer given business questions. Presents the result of analysis to those who asked the questions to help find a better business strategy.
• Data scientist
  Determines the right questions to ask based on what the client needs and collects, cleans, analyzes, and interprets data to answer them and presents the result to the client.
• Machine learning engineer/Research scientist
  Trains computers to learn from data by building machine learning algorithms.

Why Study Data Science?

A quick search online will tell you a hundred reasons to study data science, and they are very good reasons. The thing is, though, not everybody cares so much about how lucrative it is.

So, here is my answer: Study data science if you find it interesting at all. Give it a try, and you’ll know if it’s for you or not. If you don’t feel that spark when studying data science, then it may not be for you.

I think passion is the single most important reason to study data science, but I’ve never seen anybody talk about it. My advice is, don’t do it for anything but yourself.

How to Study Data Science?

The best way to study data science is to have fun; it took me six years to realize this.

Do it your way. Find a style that suits you. Start from what you find interesting. It may feel as though there are many things to learn just to get started, and it can be overwhelming. The truth is that you don’t need to know anything to get started: you’ll learn along the way. Just remember that everybody’s gotta start somewhere, and take one step at a time.

My recommended first step is to take a free course that lets you practice a lot; websites such as Kaggle and W3Schools offer such courses for free even without signing up. This will be enough for getting started on actually working with data.

Joy is the biggest drive, and the most important thing is to keep it up. When you love what you are doing, you will keep going. Take one step at a time; don’t think about getting there. Enjoy the journey and you’ll be there before you know it.

Conclusion

This was a very brief overview exploring some of the most commonly asked questions regarding data science. If you found it interesting, I suggest you search more about it online; there’s always more to learn, which is another thing good about data science.

Thank you for reading. Have a great day!