Breaking into Data Science: Essential Skills and How to Learn Them

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Data science has been in demand for quite some time now. Fortunately, the democratization of education has made it fairly easy to build a roadmap to learn essential technical skills.

Typically, the learning path includes building foundations comprising linear algebra, mathematics, probability, statistics, etc. along with a good grasp of at least one programming language like Python.

The Technicals

Equipped with these fundamentals, the learners become comfortable with machine learning fundamentals, understanding key algorithms – decision trees, random forests, ensembles, and time series, and eventually grasp complex deep learning algorithms.

During this journey, you will also need a good handle on concepts involving bias-variance trade-offs, the power of generalization, assumptions of algorithms, and much more. This list by no means is complete (or, will ever be), as the data science field involves continuous learning – that mostly happens through practical hands-on applications, or from learning how industry experts are doing it.

In such cases, platforms like Kaggle provide a good playground for understanding the complex nuances of building a high-performing model. Additionally, exposure to winning solutions on Kaggle not only increases their knowledge base but also enables learners to build the mindset of developing their robust models.

Beyond Tech Skills

So far, so good. But, have you noticed one thing?

The skills and the path I outlined hold no secret; they are largely available in the public domain. Everyone is learning the same approach to building skills to land their dream role in the data science domain.

This is when the reality check is necessary.

It is not just about the available AI talent but also the demand for such skills in the market. AI advancements are happening rapidly, especially since the onset of the Generative-AI era, which has prompted many organizations to reduce their workforce. Even Nvidia's CEO, Jensen Huang shared his views on future workforce and skills by highlighting that “AI will take over coding, making learning optional. AI is set to make coding accessible for everyone, reshaping how we learn to program”

Source: Immigration & Jobs Talk Show YT channel

What You Can Do?

The shifting industry landscape underscores one truth – changing times call for changing measures.

Given that the industry is witnessing a change in skill expectations, here is what you should focus on to build a stellar data science career:

• Hone the often-overlooked skill of decision-making, essential for making the trade-offs in building scalable machine learning systems.
• Build the ability to make informed decisions even in the absence of complete information, demonstrating quick thinking and adaptability.
• Building ML models requires extensive stakeholder management, implying potential friction. Master the art of stakeholder management to navigate potential conflicts and drive decisions with a compelling rationale.

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• Working with cross-functional teams also means that your audience might come from varied backgrounds, so building tailored communication is a big bonus.
• Most AI projects fail at the proof of concept (PoC) stage and do not even make it to production, while the ones in production struggle to show results. In short, organizations are waiting to see the returns on their AI investments. So, become that go-to person for getting things done and demonstrating the results while making progress.
• Ensure the alignment of business problems with statistical ML solutions to lead the given AI project to success. If this step goes wrong, anything downstream will not be useful.
• Innovation is a must – not just for enterprises but for all of us. Think outside the box and design innovative solutions. It is a sure-shot way to build your reputation as an expert data scientist.

The Soft Skills

Figuring out things on the fly is an art, seldom taught in classrooms. Yet, the pivotal question remains – how does one learn such skills?

There is no singular path to mastery, but here are a few starting points to develop that lens:

• Do not fear failure, instead treat challenges as opportunities to learn new things. Think of every problem statement as a gateway to learning something new in AI. It is similar to studying in university, albeit the one where you are paid for learning to make innovations come to life, instead of paying fees. Data science involves “science”, which is experimentative and involves multiple iterations to give meaningful results (and sometimes no success at all, just the learnings). These learnings accumulate over time and help you build a knowledge bank, which becomes your differentiator as you gain experience.
• Overcoming fear also means asking questions. For example, always “Start with Why?” Why are we building this? Why would our customers/stakeholders care? Why now?
• Once the "Why" behind the problem statement is clear, the "what" and "how" will follow naturally, simplifying the process of creating exceptional AI products.
• In short, in this new world where “building AI products has come down to just invoking APIs”, choosing the right problems or for that matter, inventing the right problem can pave the way for a profoundly rewarding career trajectory.

Source: builder.io

Master these skills to stand out during the interview process and build remarkable ML products that the world awaits.
 
 

Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.