Data Science use cases in IT – Interesting facts

Stewart Brand, an American Writer once said “Once a new technology rolls over you, if you’re not part of the steamroller, you are part of the road. “Meaning, technology is in swift movement and if we fail to catch up with it, we will be down trodden. We all know this because each of us might have encountered this scenario in our lives at one point or the other. This blog attempts to address an important application of technology in this regard – Data Science use cases in IT.

Data science is a technology that has budded recently but has grabbed in it’s clutch whole lot of businesses. Be it any industry – manufacturing, healthcare or IT.  In this blog, let us focus on the application of data science in IT and software development and how the upgradation with data science can benefit these sectors. Let us also get to know the answer of if you are to advance your career by learning data science, what should you do.

You might also want to read: How Robust and Impactful Is Data Science in Healthcare Sector?

Data science use cases in IT

data science use cases in IT

The integration of software and data science helps IT in ‘n’ number of ways. Say, for instance – in writing efficient SQL queries with programming knowledge or in the case of API’s. i.e.., for easier building, deployment of model pipelines. The merging of data science with software also helps in effective data cleaning, thanks to data science software skills!

Important applications 

Listing below are a few of the notable applications of data science in IT – 

  1. Automation of coding and testing with data science
  2. Artificial intelligence for clearing redundant software features
  3. Deep Learning algorithms for software security assessment
  4. Artificial intelligence in software project planning 

Top IT firms like Wipro, Cloudera, IBM and Splunk has successfully introduced artificial intelligence and data science in their businesses to reap great results. While Wipro uses data science for model management framework, Cloudera uses it for managing analytics pipelines. IBM applies data science for multicloud ModelOps and Splunk applies it for IT operations management. IT firms like VMware, Pax8, Stack Overflow and Samsara have also made successful implementations of data science.  

Data science use cases in IT and corporate sector helps it in several ways. Say, by identifying customer preferences, increased security for sensitive information, identifying risks and bottlenecks along with analyzing overall market data pattern. With extensive data science applications, job opportunities are also on the rise. Like popular data science job roles – data architect, data engineer, software data analyst and database administrator. 

Now that you are aware of the possibilities of data science in IT, let us get into answering an important question of how to make a career transition into data science. Well, if you hail from an IT or software development background you have added advantage because of technical flair that can make the career switch easier.

Understanding differences between roles

data science use cases in IT

It is important to understand the differences between software development job roles and data scientists first and foremost. 

  • Coming to the skills, a software developer should essentially be proficient in Python, Java, C programming. 
  • He/she should be well versed in Agile and SCRUM methodologies too. 
  • Data base architecture design, cloud technology and fundamentals of computer science are other important things that a software developer should know.

Now, when we come to a data science professional, the following skills are required:

  • He/she should be having sufficient knowledge in statistics and programming along with knowledge of handling big data. 
  • Machine learning algorithms, advanced artificial intelligence, deep learning and reinforcement learning models should also be in the checklist. 
  • Sound analytical knowledge clubbed with data managing, visualization and forecasting skills are also mandatory.

So, when a software developer focus on designing and testing new software programs, on the other hand a data science professional work to draw data-driven decisions and predictions.

You might also want to read: Future of Data Science: Will It Make a Positive View?

Career advancements with data science 

By now, you will be aware of the basic job responsibility differences between software development and data science roles. Also, the importance of upgrading into data science. So, with an existing software background, how can you go about skilling up in data science? Listing below are few tips and tricks.

Firstly, and most importantly, you can leverage your software background for learning data science. You can start off with the following:

  1. Brush up your coding and testing skills
  2. Revise your knowledge in data structures and algorithms
  3. Polish the skillset in database architecture and design
  4. Better your communication and visualization skills

While these are the things you can begin working on, you should make a checklist and tick off the following pointers too. 

Steps for easier Data Science career transition

 1. First thing would be to identify your potentials

data science use cases in IT

This is a basic step and this guides you better to know your strengths and weaknesses better. Upon identifying the areas where you are technically challenged, you can start focusing and working to improve them.

 2.  Understanding data science job responsibilities

data science use cases in IT

Even though there are significant similarities between both IT and data science job roles, these exists only in a surface level. Therefore, thorough research would be required to understand data science job responsibilities and requirements before taking the big step.

 3.  Identifying the skill gap and bridging it

data science use cases in IT

After extensive research on what is required and what you lack, you can start centering your efforts to bridge the knowledge gap. 

Once you are aware of the skill requirements to don a data science job role, you can work towards getting there. Out of many, listing below are a few required skills that you should gain insights about:

  1. Advance learning of mathematics and statistics
  2. Study ML algorithms and techniques
  3. Learn more about building data pipelines
  4. Get equipped with Big Data, Hadoop

How to get the right training?

You can get core data science training covering all the above-mentioned topics more at Skillslash, along with real-work experience. Besides, get direct company certification for projects from top AI startups.

Get a personalized learning experience with distinctively designed data science courses for professionals and novices. Skillslash data science courses also cover domain training and elective tracks for functional and industry specializations. You can self-design your courses, bring your own projects and get assistance to complete them as well.

Data science is an important area of study and gaining skills in it will undoubtedly elevate your profile. Take the case of future as well, data science is expected to revolutionize IT by bringing in reinforcement learning for business decisions and responsible AI models collaborating with humans. Most importantly, there will be significant improvement in data management with automated ML models and advancements in hardware to manage workloads.

With such an extensive area of application, data science can surely be your go to because there are brilliant job opportunities that is going to line up! So, advance your career curve with the right data science training now!

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