20 Jul 2022
Top Skills You Need In Real World Data Science | Ekeeda
Ekeeda Moderator
Works at Ekeeda
Data science, ML, and AI have been the hottest career fields in the last couple of years now. From science to data science, it has been a paradigm in the way people look upon the intricacies of business and how to accelerate the company's performance and overall growth. Today, millions of candidates across the world want to work as data scientists and put in a lot of hard work, and efforts to upgrade their existing skills through big data university courses or maybe through online courses that are self-paced and industry-oriented. However, there are a lot of hurdles in the real world related to working out and deriving solutions to a business problem. Other than technical, even non-tech skills like good communication, team player, proactive to take up challenges and solve are also important to work as a data scientist. The blog highlights what I’ve learned when data science was implied in the real world.
In this blog, I will share my personal experience that I have come across while implying data science to real-world problems.
Identify Business Issues
There are a lot of challenges in the real-world problems that aspirants don’t face in the university environment. In universities, they are used to structured problems and a popular dataset & eventually get the exact solution. However, the problem in the real world and various industries will often be unstructured and complex. Any assumptions on the problem will backfire in the real world. It is better to understand the business problem completely before diving into the analysis. Understanding the business problems involves doing more research on the problem and its domain, planning, asking the clients the right questions, and discussing with team members.
Be A Team Player
Data science revolves around logical thinking, generating more ideas and creativity to solve problems. Hence, teamwork plays an important role in data science. It is also necessary to think multi-dimensionally rather than one-dimensional. Team members could be people from diverse backgrounds with different kinds of skill sets which are necessary for the process and business output. Every individual has to use their expertise and the work is distributed as per their skills and caliber. I was given a task based on my data science skills to solve problems in different ways and learn new things.
A Good Listener
Another great skill that I came across is to be a good listener rather than just blabber. Data science is about grasping things and working in collaboration. Basically, the person has to understand the views of others team members. Many times, other team members come up with good ideas and the ideas might be unique and suitable for the particular problem. So, it is necessary to listen to and understand them in order to successfully implement them in the project. It’s nothing to hide that Data science is not an individual business rather it’s a team effort and everyone has a chance to present their ideas and make the process get swift and bring the best outputs.
Documentation Process
Artificial intelligence and data science subjects are evolving at a breakneck speed in the world and due to this, there will always be something new and crucial to learning. It’s very hard to remember things and thus documentation is a must. Documentation helped me crystallize things in my own thought process. We used to document our learnings, findings, analysis, model process, experiments, and the coding structure. It’s not just the success, I even wrote about failed experiments and the reasons behind the failure in a detailed manner. It later helped me to sharpen my ideas and taught me in the long run. It even helped me improve my communication skills & learn data science concepts in detail.
Be On Your Toes
Working in a swift and fast-paced environment helped me clearly plan, prioritize, and direction during the start of every sprint job. Having an agile mindset is the need of the hour and it helped me in responding to changes & handling uncertainties with ease and comfort. If you come across uncertainty, try out options, collect feedback and improve iteratively. It also gave me an opportunity to collaborate with different teams. Presenting a minimum viable product (MVP) in the form of a machine learning model at the end of each sprint to the stakeholders helped me to shape my projects in a better form.
Learn To Tell A Story
Narrating a story is an important part of data science skills. We will crunch the data and create a model, and find the insight. What does this model say in business terms? In other words, how this business model will generate money for the company or solve the problem? Stakeholders and management are not interested in algorithms, statistics, or any technical terms. So, the main element is to narrate the explain the model in simpler terms to a non-technical audience in an engaging way. One way is to explain the mode via a short and interesting story. You should use good examples, comparison, and visualization so that you can convey a positive message towards the end of the story. Whether to take the crucial steps or not. Storytelling is an art and it takes time and a lot of practice to master things.
Creativity To Deliver Results
Most individuals use traditional PPT to display their work to the stakeholders and clients. However, it's an age-old boring practice, instead, why can’t we create a web app or dashboard to explain our model output? Give them the experience to deliver the great output and promising ways to enhance the business growth & book profits. Creating a web app or dashboard will show commitment to the project and also get connected with stakeholders and clients.
Use Version Control
It’s an important thing that everyone includes in the workflow. It will help to manage your codes centrally rather than saving them on a PC/Laptop or external drive. This way, you can refer to the code or documents whenever you are working on a new project at new locations.
Practice Coding Skills
I significantly improved my coding skills during the last 8-12 months. One thing I’ve learned is to write functional or Object-oriented code to create maximum code reusability. It will help to use the code in future projects also and reduce time in the existing ones. I used to document the code function whenever I referred to Google or StackOverflow and it helped me to learn new coding skills and things about coding. Always follow the best practices and remember to keep your code reader-friendly.
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Don’t Isolate And Sit
Data science is the overall blending of computer science, statistics, machine learning, and domain expertise. Thus, it requires varied skills from handling different to cleaning data and interpreting the final model and deploying it. So don’t assume that you can master data science in one day. Learn to implement Practical Data Science knowledge as much as possible in real-world projects if you wish to be proficient in the field. If you get into a difficult situation don’t just sit in a corner and baffle. Instead, take help from senior managers and data experts science you will gain more knowledge and eventually make you confident about your approach.
Learning And More Learning
AI Data Science Machine Learning is the trilogy that brings in technological advances and it’s the new revolution in the IT industry. You should decide to take them strategically by investing one or two hours every day to learn new Ai, ML, or data science concepts and solve new problems that will include learning algorithms, coding, reading a blog, doing personal projects, etc. Other than this, I would recommend reading a bit of non-technical books that help you understand the storytelling techniques & develop unique communication skills.
Keep Your Passion Ignited
Initially, I thought in this analytical world you can master everything. But then slowly and steadily I realized that my assumption was wrong. I understood that continuous learning is required for all and on top of that you need the hunger & passion to learn more. Be it AI Data Science Machine Learning or NLP, it is always the passion that solves complicated problems.
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Thanks, And Happy Reading!
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