What does a Data Scientist do?
By: Tharuka Kasthuriarachchi
Tags: Data Engineering, Data Analysis, Data Science

What does a Data Scientist do?

What do I do as a data scientist in my workplace?

Data Science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data. More companies now being realized about data science capabilities which helps them to keep the risk behind in the industry and to remain competitive in the industry.

Whats the process in the data science?

Data Science Process —Graph by author

The data science process begins with a goal or a question that we want to answer. So at first, we are studying the domain and the question which we need to address. This process approximately takes several days to several months depending on the size of the domain and the problem which we need to address. The more we clear, we can give a better solution.

We next need to get appropriate data to help us answer that question. And here more importantly we have to consider 4 main things, “Volume”, “Variety”, “Veracity” and “Velocity” which we identify as 4 V’s in big data. Often it might not be possible to access the ideal data set that we have in mind, so determining the closest possible accessible data is important.

And usually, any data set does not serve as a garnished dish. so upon obtaining the data, we have to pre-process and cleaning the data. This means we are getting raw data and we have to convert into a format that we can use for further analysis. This step is what we call Data Wrangling. As an example, we have to deal with corrupt records or missing values. I’m using Python in this step. Most of the time NumPy and pandas libraries are helping me.

The cleaned data can be explored, to obtain a high level understanding of the information contained. We typically do this by visualizing the data and by calculating summary statistics. This process is referred to as “Exploratory Data Analysis”. In this step “Matplotlib” and some other visualization libraries like “Seaborn” coming to help. At the end of this step, we can obtain very useful insights from the data. As an example, buy plotting the data points of locations of COVID-19 patients on a map you can notice that specific geographic zones in the world which are at risk and which are still safe. And this step also helps to enrich the data set and derive more interesting features.

After this step, machine learning algorithms and appropriate computational and statistical methods will help to step further into analysis and predicting future trends. So this is where the magic happens! Here I’m using a set of machine learning and statistical libraries such as skit-learn, Keras, SciPy and Statsmodels, etc. This phase may also involve validating our model, and interpreting the results.

Finally, we communicate our results concerning the original question which asked. This involves synthesizing results and telling a story about what we’ve learned from the data. Simply we called deploying our model to use across the organization. We present this as a form of a report or as a user interactive tool.

Now, I may have given you the impression that these phases take place one after the other, but in reality, the data science process is iterative and non-linear. This means that we may repeat this data science process several times throughout a project. And we may also move back to a previous phase to repeat it using new knowledge acquired.

And how about skills in data science?

Data scientists typically combine skills in mathematics, statistics, computer science, and programming, and they also should know the domain in which the problem comes. This is key, as it helps us with asking the right questions in the first place, as well as interpreting results.

Data Science Skills Set —Graph by author

I believe that it is important to have both a theoretical understanding,

and practical experience of data science. Therefore, I’m practicing programming exercises as well. But I’m not a professional programmer, but I’m expertise in the python programming language. And meanwhile, I’m experimenting with different machine learning algorithms, statistical concepts by applying to case studies in different domains.

So why Python?

Python has been around since the 90s, so it is a mature programming language, and it works well for people who have never programmed before. It is open-source and free to install. More importantly, Python is a popular choice for data science projects, as there are many libraries available (such as R) that ease the tasks of data processing and analysis.

And finally, I should mention that I have learned most of the things from the Coursera machine learning specialization course which is offered by the “University of Washington” and Coursera big data specialization course which is offered by “University of California San Diego”. I’m writing this article by my own experience at “IQ Pvt ltd” with one year of experience in the role of the junior data scientist. If there is anything to add feel free to comment below.