How To Advance In Data Science: What Are the 5 Most Important Skills?

The job market for data scientists is more competitive than ever. Specific skills set you apart from your competitors.

Personalized recommendations and advertising, search queries, or fraud and image detection – today, machine learning (ML) is inextricably linked to our daily lives, even if it is not always obvious. According to a 2020 report by ISG provider Lens Analytics, the demand for data scientists in Germany has exploded in recent years due to the increasing prevalence of ML in our daily lives.

The demands on data scientists are high. More is expected than a bit of arithmetic and programming. Soft skills, social skills, and a vision are essential for this job. So what are the top five skills someone needs to succeed in data science?

Understandable Communication

To be successful as a data scientist, the ability to communicate is at least as important as the technical know-how. You have to be able to explain technical details to both expert and non-expert audiences and build trust in the program by building bridges between these audiences.

It is helpful, for example, to build bridges to everyday life, i.e., to things that everyone can do something with, regardless of their technical knowledge. Abstract or overly technical comparisons are not very memorable for most people and therefore miss the mark.

Everyone in the company should understand the added value that a data scientist brings. It is not primarily about how, for example, Apache Spark works in detail. Instead, it is about creating awareness of data science and its application. You have to be able to develop a business case so that even non-professionals can understand the value of data.

Keep An Eye On The Trends

The number of vacancies has increased and will continue to grow. Instead, the problem with the situation in the labor market is that the training programs are not sufficiently geared to practice in the industry, and therefore, not all the skills required for the job are learned.

Key competencies include:

• Knowledge of data collection and labeling.
• Handling operating conditions and model infrastructure.
• Monitoring.
• Model retraining pipelines.

Hidden Technical Debt in ML Systems, a report by Google, describes this phenomenon. It states that about 5 percent of real-world ML systems are made up of ML code, while the rest is glue code to support those ML systems.

Therefore, everyone who works in data science should always follow the current trends and developments in research, industry, and politics and remain willing to learn. In other areas, what has been known has been helpful for a long time and can be applied again and again. In computer science and data science, most technologies become obsolete after about seven years. And this rapid development will continue to accelerate in the coming years. Therefore, it is much more important here to be able to adapt to trends and developments. Flexibility in mind and the skills used are critical.

Start By Concentrating On The Essentials

Rapid advances in machine learning are driving data scientists to increasingly sophisticated tools with endless functional possibilities. However, it would help if you first had a stable basis with the appropriate metrics for starters. Superficial structures such as predicting the mean in regression problems or predicting the class in classification problems are sufficient for this. But you have to look carefully: If you have a 90 percent prediction accuracy for a given situation, but you’re right 99 percent of the time because you’re always predicting the same thing, it’s not very exciting. Accuracy is not the measure of all things.

The critical question is how to build trust in ML systems. To do this, you have to develop a transparent benchmark that simplifies the individual, product-relevant evaluation. Because accuracy does not represent optimization in some instances, other criteria may need to be used. For example, the F1 score is a suitable measure that considers both accuracy and recognition and not just the absolute number of correct predictions. When these baselines are in place, you are hedged to the downside and have reliable machine learning predictive power.

Proceed In A Structured Manner

Data scientists like to get bogged down in dynamic modeling and sometimes lose sight of the most important: the constant questioning and understanding of the data in constructive exchange with various stakeholders and experts.

Also, don’t get too hasty in concentrating on the technical issue. Instead of debating library choice, consider applicability first. Before going into the technical details, one should first clarify how specific models can be used and how they increase a company’s success. You always need to have a holistic view of the data and the desired outcome you are working toward.

Finally, it is essential to know where the data came from, how it was collected, and how it can and cannot be used.

Choosing The Right Area Of ​​Responsibility

A good team can complement each other. Everyone has their strengths and weaknesses. But of course, you also need new talents who bring in innovations and ideas.

At some point, everyone has to specialize. The field of data science encompasses so many aspects that it is impossible to keep track of all developments. Choosing a specialty makes you a highly qualified contact for specific topics such as ML, NLP, or computer vision. A particular passion for the subject is essential, as is specific expertise in a highly topical and comprehensive field.

Data scientists can distinguish themselves by developing specific data science tools, especially low-code and no-code solutions, to stand out from the crowd and offer more. In this way, they improve their efficiency and productivity in business and technical fields.

Data scientists are more in demand than ever anyway. But if you can combine the above qualities in yourself, your expertise will be in excellent order. It’s subject matter expertise that asks the right business and data questions, builds a solid baseline and associated metrics, and uses one’s specialization to communicate results to stakeholders effectively.

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