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Role of Computer Science in Data Science World

Role of Computer Science in Data Science World

Introduction

Are you from a computer science background and moving into data science? Are you planning to learn coding being from a non-programming background in data science? Then you need not worry because in this blog we will be talking about the importance of computer science in the data science world. Furthermore, we will also be looking at why is it necessary to be fluent with coding(basic at least) in the data science world.

Before enumerating the role of computer science in the data science world, let us clear our understanding of the above two terms. This will allow us to be on the same page before we reason out the importance of coding in data science.

What is Computer Science

Computer Science is the study of computers and computational systems. Unlike electrical and computer engineers, computer scientists deal mostly with software and software systems; this includes their theory, design, development, and application.

Principal areas of study within Computer Science include artificial intelligence, computer systems, and networks, security, database systems, human-computer interaction, vision and graphics, numerical analysis, programming languages, software engineering, bioinformatics and theory of computing.

What is Data Science

Data science is the umbrella under which all these terminologies take the shelter. Data science is a like a complete subject which has different stages within itself. Suppose a retailer wants to forecast the sales of an X item present in its inventory in the coming month. This is known as a business problem and data science aims to provide optimized solutions for the same.

Data science enables us to solve this business problem with a series of well-defined steps.

1: Collecting data
2: Pre-processing data
3: Analysing data
4: Driving insights and generating BI reports
5: Taking decision based on insights

Generally, these are the steps we mostly follow to solve a business problem. All the terminologies related to data science falls under different steps which we are going to understand just in a while. Different terminologies fall under different steps listed above.

data science skills | Dimensionless

Data science as you can see is an amalgamation of Business, maths and computer science. A computer engineer is familiar with the entire CS aspect of it and much of maths sections is also covered. Hence, there is no denying fact that Computer science engineers will have a little advantage while beginning their career as data scientists.

Application of computer science in data science

After understanding the difference between Computer Science and Data Science, we will look at the areas in data science where computer science is employed

  1. Data Collection (Big data and data engineering)

    Computer science gives you an edge in understanding and working hands-on with aspects of BIG Data. Big data works mainly on important concepts like map-reduce, master-slave concepts etc. These concepts are something by which most of the computer engineers are aware of. Hence, familiarity with these concepts enables a head start in learning these technologies and using them effectively for the complex cases.

  2. Data Pre-Processing (Cleaning, SQL)

    Data extraction involves heavy usage of SQL in data sciences. SQL is one of a primary skill in data sciences. SQL is something which is never an alien term to Computer Engineers as most of them are/should be adept in it. Computer science engineers are taught the databases and their management in and out and hence knowledge of SQL is elementary to them.

  3. Analysis(EDA etc)

    For data analysis, knowledge of one of the programming language (R or Python mostly)is elementary. Being proficient in one of these languages grants the learner an ability to quickly get started with complex ETL operations. Additionally, the ability to understand and implement code quickly can enable you to go one extra mile while doing your analysis. Also, it reduces your time spent on such tasks as one is already through all the basic concepts.

  4. Insights( Machine Learning/Deep Learning)

    Computer scientists invented the name machine learning, and it’s part of computer science, so in that sense, it’s 100% computer science. Furthermore, computer scientists view machine learning as “algorithms for making good predictions.” Unlike statisticians, computer scientists are interested in the efficiency of the algorithms and often blur the distinction between the model and how the model is fit. Additionally, they are not too interested in how we got the data or in models as representations of some underlying truth. For them, machine learning is black boxes making predictions. And computer science has, for the most part, dominated statistics when it comes to making good predictions.

  5. Visual Reports(Visualisations)

    Visualizations are an important aspect of data science. Although Data science has multiple tools available for visualization, complex representation requires that extra coding effort. Complex enhancements in visualizations may require some technical aspect of changing few extra parameters of the base library or even the framework you are working with.

Pros of Computer Science knowledge in Data Science

  1. Headstart with all technical aspect of data science
  2. Ability to design, scale and optimise technical solutions
  3. Interpreting algorithm/tool behaviour for different business use cases
  4. Bringing a fresh perspective of looking at a business problem
  5. Proficiency with most of the hands-on coding work

Cons of Computer Science knowledge in Data Science

  1. May end up with a fixed mindset of doing things the “Computer Science” way.
  2. You have to catch up with a lot of business knowledge and applications
  3. Need to pay greater attention to maths and statistics as they are vital aspects of data science

Conclusion

In this blog, we had a look at the various application of computer science in the data science industry. No wonder that because of multiple applications of computer science in the data science industry, computer engineers find it easy, to begin with. Also, at no point in time, we imply that only computer science graduates can excel in the data science domain. Although, being a bachelor in computer science has its own perils in the science field. But, it also comes with its own set of disadvantages like lack of business knowledge and statistics. Anyone can excel in data science who can master all three aspects of it regardless of their bachelor degrees. All you need is right guidance outside and motivation within. Additionally, we at Dimensionless Technologies, provide hands-on training on Data Science, Big Data and NLP. You can check our courses here.

Furthermore, for more blogs on data science, visit our blog section here.

Also, you may also want to have a look at some of our previous blogs below.

Will Julia Replace Python and R for Data Science?

Top 10 Data Science Tools (other than SQL Python R)

Introduction to Pandas, NumPy and RegEx in Python