Source: Canva
Data science has been in demand for quite some time now. Fortunately, the democratization of education has made it quite easy to put together a roadmap for learning essential technical skills.
Typically, the learning path includes building foundations that comprise linear algebra, mathematics, probability, statistics, etc. along with a good understanding of at least one programming language such as Python.
Technicians
Equipped with these foundations, students become comfortable with the fundamentals of machine learning, understand key algorithms (decision trees, random forests, ensembles, and time series), and finally understand complex deep learning algorithms.
During this journey, you will also need a good handle on concepts involving trade-offs between bias and variance, the power of generalization, algorithmic assumptions, and much more. This list is by no means complete (or ever will be), as the field of data science involves continuous learning, which occurs primarily through practical applications or by learning how industry experts do it.
In such cases, platforms like Kaggle provide a good playing field for understanding the complex nuances of building a high-performance model. Additionally, exposure to winning solutions on Kaggle not only increases your knowledge base, but also allows students to develop the mindset needed to develop their robust models.
Beyond technological skills
So far, so good. But have you noticed one thing?
The skills and path I outlined hold no secret; They are largely available in the public domain. Everyone is learning the same approach to develop skills to land their dream role in the data science domain.
This is when you need to check reality.
It's not just about the talent available in ai, but also the demand for those skills in the market. Advances in ai are occurring rapidly, especially since the dawn of the era of generative ai, which has led many organizations to reduce their workforce. Even Nvidia CEO Jensen Huang shared his views on the future workforce and skills by highlighting that “ai will take over coding, making learning optional. “ai is poised to make coding accessible to everyone, reshaping the way we learn to program.”
Source: Immigration and Employment Talk Show YouTube Channel
What you can do?
The changing industry landscape underscores one truth: changing times call for changing measures.
As the industry is seeing a shift in skill expectations, here's what you should focus on to build a stellar career in data science:
- Hone the often-overlooked decision-making skill essential for making trade-offs in building scalable machine learning systems.
- Develop the ability to make informed decisions even in the absence of complete information, demonstrating quick thinking and adaptability.
- Building machine learning models requires extensive stakeholder management, which involves potential friction. Master the art of stakeholder management to address potential conflicts and drive decisions with compelling justification.
Source: Canva
- Working with cross-functional teams also means that your audience can come from diverse backgrounds, so creating personalized communication is a huge advantage.
- Most ai projects fail at the proof-of-concept (PoC) stage and don't even reach production, while those in production struggle to show results. In short, organizations are waiting to see returns on their ai investments. So, become that go-to person to get things done and demonstrate results as you go.
- Ensure alignment of business problems with statistical ML solutions to drive ai project to success. If this step goes wrong, anything after that will be useless.
- Innovation is essential, not only for companies but for all of us. Think outside the box and design innovative solutions. It's a sure way to build your reputation as an expert data scientist.
The soft skills
Discovering things on the fly is an art that is rarely taught in classrooms. However, the fundamental question remains: how do you learn these skills?
There is no single path to mastery, but here are some starting points for developing that lens:
- Don't be afraid of failure, treat challenges as opportunities to learn new things. Think of each problem statement as a gateway to learning something new in ai. It's similar to studying at university, although you get paid to learn how to make innovations a reality, rather than paying fees. Data science implies “science,” which is experimental and involves multiple iterations to yield meaningful results (and sometimes no success, just the learnings). These learnings accumulate over time and help you build a bank of knowledge, 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? Because right 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 been reduced to simply invoking APIs,” choosing the right problems or, for that matter, inventing the right problem can pave the way for a deeply rewarding career path.
<img decoding="async" alt="Building ai products has been reduced to simply invoking APIs.” width=”100%” src=”https://technicalterrence.com/wp-content/uploads/2024/06/1718745228_612_Breaking-into-data-science-essential-skills-and-how-to-learn.png”/><img decoding="async" src="https://technicalterrence.com/wp-content/uploads/2024/06/1718745228_612_Breaking-into-data-science-essential-skills-and-how-to-learn.png" alt="Building ai products has been reduced to simply invoking APIs.” width=”100%”/>
Source: constructor.io
Master these skills to stand out during the interview process and create extraordinary machine learning products the world expects.
Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author and international speaker. Her mission is to democratize machine learning and break down the jargon so everyone can be a part of this transformation.
