5 Technical Skills to Boost Your Data Scientists Career

Data Scientist Career

5 Technical Skills to Boost Your Data Scientists Career

Machine-learning apps are an integral part of our daily lives. Machine-learning applications are an integral part of our lives, regardless of whether we know it or not. We come in contact with them every day online via advertisements, recommendations, fraud detection, search and image recognition, and other methods. Due to its increasing prevalence in our daily lives, there has been a boom in the demand for data scientists in recent years. Projected job growth is 31% through 2029. Yet, data scientists remain in short supply. In 2020 there was a shortage of 250,000.

You should know that a career as a data scientist requires more than programming and number crunching. Data scientists must also have strong communication, business acumen, and public speaking skills. As the machine learning practice leader at Databricks, I have seen firsthand the challenges of being a data scientist and how to stand out.

Are you eager to learn new tools and expand your professional knowledge? These are five skills that you should keep in mind to improve your data science career and professional profile.

Also read: 9 Required Skills to Become a Freelance Data Scientist in 2021

1. Blending technical and non-technical communication

Data scientists must communicate technical concepts to both technical and non-technical audiences in order to thrive. It doesn’t matter how hard you work to build the best model if it’s not easy to explain it to others so they can trust it.

One tip that I recommend to help concepts stick is to make analogies to everyday items. When I explain distributed computing using Apache Spark, I use candy to illustrate the process. If I had a lot of M&Ms I could count them all one by one to get the exact count. To make this task more efficient, you can invite your friends to help you count the M&Ms. Spark is what people think of when they see M&Ms in the grocery store. People often use rocket-ship analogies. However, unless you work for SpaceX or NASA, it is unlikely that you will come across rocket ships every day. This makes it more difficult to make your analogy stick.

Communicating effectively and explaining terminology in a way everyone understands will increase data transparency within the organization and help ensure that everyone knows what you are offering.

2. Always be learning

There is a need for data scientists, but many traditional education programs don’t teach the necessary skills. Coursera and university courses that I took were primarily focused on improving model performance and applying benchmarks, such as ImageNet accuracy. I discovered that these processes were only one piece of the puzzle when I first entered the industry. It is important to consider how data was collected and labeled, deployment constraints, infrastructure to support the model, monitoring, and pipelines for model retraining, among other things.

This phenomenon is described in the Google paper “Hidden technical debt in machine learning systems”. They report that only 5% of real-world ML systems are made up of “ML code”, while the rest of these systems use “glue code”.

How can you acquire all the necessary skills to become a data scientist? Always learn. My philosophy is that everyone can learn something from anyone you meet. I recommend meeting up with colleagues and fellow ML professionals and getting exposure to different aspects of the field. Even after graduating from grad school, I continued to take classes and join regular reading study groups. I recommend that you subscribe to The batch, a weekly digest of the latest in ML research, and innovative applications of ML in the industry (and, importantly, where ML and policy need to improve).

The data field is changing so fast. In computer science, the average half-life of your knowledge can be seven years. However, it is shorter in data science. Technological innovation will continue at a rapid rate, but don’t be overwhelmed or intimated. Keep learning and you will always be able to use new skills.

3. Starting simple and establishing a baseline

Data scientists are eager to learn the most recent and greatest tools, given the rapid advances in ML. Data scientists should start with a simple baseline and create metrics. The baseline should not be too naive. For example, predicting the average value of regression problems (e.g. predict the average house price) or the most common class for classification problems (e.g. always predict “no”) are examples.

I don’t know how many times I have heard someone claim that their machine learning model predicts XYZ problems 90% of the time. Only then did someone point out that if you consistently predict ‘no, you will be accurate 99% of all the time.” It is essential to establish a benchmark and clearly define product-relevant metrics for evaluating your ML systems in order to gain trust. The method that predicts no consistently might be the best for accuracy. However, it is not a reliable metric.

The F1 score may be a suitable metric to balance precision with recall and not just the number of correct predictions. Once you have established a baseline for your machine-learning system’s predictive performance, use that benchmark as a guideline.

4. Asking the right questions

Data scientists are often eager to create models. However, understanding the data, speaking to stakeholders and subject-matter specialists, and continuously asking questions through exploratory data analysis is critical for delivering the best solution for your business.

Instead of jumping to the solution to the technical problem, look at the business problem and take a step back. Instead of arguing about whether PyTorch is better than TensorFlow or not, ask: “How will this model be used?” How can we measure success” for this project? It pays off to think through these questions before you start the project.

Ask questions about the data. This includes how it was collected and how it should be used. For inspiration on the best questions to ask about data, I recommend the Datasheets for Datasets paper by Gebru and al.

Also read: 6 Easy Steps To Build Your Professional Data Science Team

5. Identifying your specialization

When interviewing candidates for my team, it is important that I find people who will add value to the existing team skillset. No matter how talented the clones of the existing team members are, I want people who are able to bring new ideas and talents to the table. I am trying to create a human team.

Candidates who have passions or expertise in a particular area are what really make them stand out. You can have a passion or expertise in a specific area of ML (e.g. NLP, computer vision) or within a particular industry (e.g. retail). But the key difference is to be a subject-matter expert and to stay current in this field. You will become the expert on a topic and be indispensable.

Data-science tools are improving, especially with low-code and non-code solutions. This will allow you to excel in business and technical skills, and provide the best value for your time.

When approaching a new project, make sure you have everything in order. You will be a rockstar if you can do all this.

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