Data Science Career Opportunities, Challenges, and Lessons

Data Science Career Challenges and Lesson

You learn data science to be a data scientist, But to lead data science you need to innovate

Building your career in data science, read this article to learn about the data science career opportunities, challenges and lessons. Leaders in data science work in both academia and industry, these are the people whose research and innovation have created huge career opportunities across all industries in tech, media, retail, finance, travel, social media, healthcare and more. One of the most famous position is called Data Scientist.

This article is for the people who are building their career as data scientist and want to work for companies rather than academia. There are two types of work in data science, One is research and innovation focused, where your work moves the data science field forward, others will study your work and research through books and courses. Second one is development and business focused, where innovation also matter but mostly you are using existing research and tools for achieving business targets. There is a significant difference in how data science is being used in both types of jobs. Here I am sharing some of the observations you should think about when you are early in your data science career working for a business.

Idealistic Theories in Academia vs Practical Reality in Industry

People from academia, who are not associated with industry, offer courses on data science more from an academic point of view. But for anyone who joins industry as a data scientist it becomes very important to learn how to apply that knowledge in real world problem solving. In fact we need both the research and ability to use that research in industry. Never assume you know it all by completing a few courses, instead seek to solve a real problem or work with a company.

Optimize RMSE(Your Knowledge, Your Performance)

Data Collection vs Quality Data Collection

Data is being created every moment of every day, from clicks, likes, and shares, to rides, transactions, and streaming content. The question that every data scientist should ask “How can I collect quality data?” It’s not big data that makes the big difference, it’s the way you approach big data. It’s a skill you need to develop. You need to develop a data collection strategy and you need to take initiative, which can help reduce infrastructure cost and you will also have quality data for your model.

Data Science begins before you have any data.

Fancy Algorithms vs What is needed

The product you are developing gets more complex over time, sometimes your ideal data science approach is not feasible. And we are required to simplify our approach so we are sometimes quite disappointed because we know some super fancy algorithm but it just can’t be applied. So we have to rethink our solution in the light of simplicity.

Glory is beating a neural network with a linear model.

Data Science Knowledge vs Development Knowledge

There are some skills that data scientist should learn if he or she doesn’t want be a useless resource in any company that builds data products. One of these skills is programming like Python, R, Scripting. There is huge crowd of aspiring data scientists waiting for your job. So either you need to stand out with your research work or learn some of the skill sets of a developer. If you can learn data science, what’s stopping you from learning development skills. It’s fun to learn programming.

Knowledge of Data Science vs Knowledge of Scale

Without the knowledge of scale, computational complexity and infrastructure needs it will be hard to lead a project. You learn this skill with experience but it’s easy to ignore it because it’s not taught to you. If you make conscious decision early, you will be way ahead of time.

When you think data science, think BIG.

Great Work vs Great Communication

Communicate your data for impact. Impact is when decision makers clearly understand what you wanted to communicate and take action. Remember your analytics is not universally interesting. Understand who is your audience and what they can understand to make them act.

Knowledge is knowing data science, Wisdom is knowing what to communicate.

Algorithm vs Performance

Regardless of how smart or how educated we are in data science, if our model doesn’t generate significant positive value for business, we fail. We do data science on a business purpose, we have to achieve targets.

It’s not about data science algorithms, It’s about performance.

These are some of my observations and there are more than one ways of doing things. Feel free to share your thoughts on career in data science and ask any question you might have related to data science.

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