Getting started with Data Science

Data science seems to have the interest of people from every corner of academia and industry. There are many different directions from which one can Get started in the Data Science field.

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Image credit: www.northeastern.edu

That said, depending on what background you’re coming from, the steps you must take in order to acclimate yourself to the subject and become a proficient data scientist will differ. we will discuss some important points you should keep in mind if you currently work in computer science and are hoping to transition into data science.

Many people hold the misconception that computer science and data science are effectively two names for the same subject; however, though they may be interrelated (i.e., computer science is one building block of data science), they are far from identical.

Tips to Get started with Data science

1. Learn Python

If you’re going to get started with data science, you should spend some time learning Python. A good portion of the work will involve topics like statistical analysis, random simulation, and the application of machine learning models.

Whatever your personal feelings about the language, there is no denying that Python has come to dominate modern-day data science. While learning it isn’t absolutely necessary (for example, R is another popular language with data science capabilities), it is highly recommended.

For starters, Python has a vast array of available packages and modules specifically designed to aid with data science, including but not limited to Pandas, NumPy, SciPy, Scikit-Learn, PyTorch, and Tensorflow. All of these packages have extensive, well-documented capabilities, and are commonly taught as part of data science courses and boot camps. So just do yourself a favor and learn Python.

2. Learn some statistics

Statistics is one of the foundational elements of data science [1], forming the bulk of its theoretical foundations. In fact, before modern data science developed, much of the work resembling it was conducted by talented statisticians. When trying to get started with data science it is important to have some understanding of statistics.

Learning statistics might appear to be a daunting task, but don’t let it hold you back from getting started with data science. While it is definitely a skill you should continually work to improve as a data scientist, far too many people believe they must be mathematical geniuses to become data scientists.

In reality, not every data scientist will need advanced mathematics (such as the kind necessary to write cutting-edge machine learning algorithms) for their day-to-day work. However, having at least basic familiarity with statistics will increase your appreciation for the field as a whole and help you gain keener insights into the problems you solve.

3. Learn to appreciate the humanities

It’s not uncommon for computer science or engineering graduates to feel a slight sense of superiority over their social sciences counterparts because of their higher salaries and more promising job prospects.

Perhaps you also have this attitude, passively absorbed through years of work in a computing-heavy environment. If so, you would do well to change it before getting started with data science.

There are two primary reasons an appreciation for humanities is essential as a data scientist. First, it’s important to realize that one of the essential components of data science is domain expertise. The data you work with only makes sense in context and in many cases the domain expert who explains that context to you will be a member of the humanities. If you’re going to understand the data effectively, you must respect both the domain expert as well as the background information is provided.

Secondly, early work in data science often suffered from ethical issues due to an ill-founded fixation on its quantitative elements. Recently, there has been a growing transition toward a more mixed-methods approach, which often utilizes research techniques from the social sciences in order to prevent algorithmic bias and produce more equitable results.

Long story short, good data science requires the help of the humanities — and as a data scientist, so will you.

Recap On Getting Started with Data Science

Working to learn the above skills will be extremely helpful for you as you transition into data science, here they are compiled together:

  1. Learn Python. It’s the modern-day data science language — don’t get left behind.
  2. Learn statistics. It’s the theoretical foundation for data science — don’t miss out on understanding the basics.
  3. Learn to appreciate the humanities. They’re an important element of good data science — don’t let your ego blind you to progress.

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