You are currently viewing Top Data Scientist Skills to Learn in 2024

Top Data Scientist Skills to Learn in 2024

  • Post author:
  • Post category:Global
  • Post comments:0 Comments

The data science platform market size is projected to reach more than US $178 billion globally by 2025 (source: Business Insider). This means the demand will be extremely high for data science professionals by 2025. The Bureau of Labor Statistics estimates that employment in data science will grow by 35% from 2022 to 2032.

Around 17,700 vacancies will be available for data scientists yearly on average, over the forecasted period. As we approach 2024, it is crucial to think about what data science skills to learn to become well-prepared for a growing career.

Technical Data Scientist Skills

  1. Programming

Expertise in one of the programming languages like Hadoop, R, Python, etc., and the ability to effectively adapt to changing technology is essential for data scientists. SQL, Python, C/C++, and Java are common programming languages that assist data scientists in organizing sets of unstructured data. Any uncertainty while using programming tools can become a deal-breaker for an organization depending on your work for their business growth.

  1. Mathematics & Statistics

A strong base in math and statistics is important to easily write code or use functions and reach precise conclusions from the dataset. Without math and statistics knowledge, it is almost impossible to create hypotheses based on the ways the system will act with certain changes. Data scientists will have difficulty in assuming statistical significance regarding data variations.

  1. Quantitative Analysis

Data scientists must have primitive knowledge of a challenging environment and its behavior, munging messy and difficult data, and developing prototypes and models to test assumptions. They must know how to develop predictive and regression models, ML (machine learning) algorithms, data-reduction methods, time-series forecasting, and other key concepts associated with quantitative analysis.

  1. Linear Algebra & Multivariate Analysis

Some interviewers may ask you calculus questions. Apart from the interview, knowledge of linear algebra and multivariable calculus helps when building out-of-the-box models in-house. This is primarily beneficial when products explained by data can result in transformational goals for the company. Linear algebra and multivariate calculus include advanced mathematical ideas implemented in ML and data analysis.

  1. Data Visualization Skills

Working knowledge of Plotly, Tableau, Sisense, and/or Qlikview is key to a data scientist’s career success. It shows their ability to give insights to technical and non-technical audiences. A data scientist must have the knowledge of principles of visualizing and confidently presenting convincing data to stakeholders for business growth.

Non-Technical Data Scientist Skills

  1. Iterative Design

Data scientists ask questions from data analysts and then gain insights from the results to decide the next step and deliver the best result. An aspiring candidate must have the ability to work through this iterative design process.

  1. Hacker’s Spirit

It refers to the ability to work with new or unknown coding languages or formats and develop their own tools when they cannot get a solution.

  1. Data Intuition

This is one of the key data scientist skills that helps data scientists to identify patterns within structured and unstructured data sets.

  1. Data-Driven Decision Making

Develop the skill to conclude, judge, or make decisions with accurate data.

  1. Intellectual Curiosity

Data scientists have to work with unstructured data and hardly know how to get valuable insights for business growth. Intellectual curiosity helps them consider areas no one else has considered before.

  1. Stay Organized

To work systematically, completing prioritizing tasks and resolving challenges on time, it is important to be organized.

  1. Statistical Thinking

Alongside a solid background in data science, data scientists must be perfect at statistical thinking. They must know how to resolve a query using statistics.

  1. Interpersonal Skills

Just like data visualization, data scientists need to develop interpersonal skills to communicate data insights and collaborate with teams effectively. Leadership, public speaking, active listening, sharing feedback, empathy, and good communication skills are crucial interpersonal skills.

Choose a Certification Course to Grow Skills

  1. Certified Senior Data Scientist (CSDS™) by USDSI®

USDSI® (United States Data Science Institute), a proud member of the American National Standards Institute and Institute of Credentialing Excellence, offers the CSDSTM course to become a future-ready senior data scientist.

This self-paced program requires a time commitment of 8-10 hours per week. Candidates will learn data science significance, data science with R, elastic stack for data scientists, data science project management, and more topics.

  1. Professional Data Engineer Certification by Google

This certification has no prerequisite; however, more than three years of industry experience is recommended for designing and managing solutions with Google Cloud. The certification exam evaluates the ability to design a data processing system, prepare and use data for analysis, store data, process data, and maintain, and automate data workloads.

  1. Data Science Professional Certificate by IBM

Beginners who are new to the data science sector can develop skills in data science within 5 months at their own pace through this data science course. It requires no advanced experience and helps get hands-on experience and build in-demand skills. Candidates learn the way to apply data science methodology and libraries, languages, and tools used by data scientists.

  1. AWS Certified Machine Learning

This certification is offered by Amazon Web Services (AWS) for data science and development experts. Earning this certification shows skills in building, training, tuning, and deploying ML models on AWS. Candidates require at least 2 years of experience and skills to show intuition behind basic ML algorithms.

Conclusion

The demand for data scientists will go up further in the coming years in almost every industry. The data science job market will have a huge gap between the demand and availability of skilled data scientists. So, it’s the right time to use the opportunity to your advantage by developing key skills and securing a rewarding career for you and the industry.

Author bio:

Hello, I am a professional SEO Expert & Write for us technology blog and submit a guest post on different platforms- we provide a good opportunity for content writers to submit guest posts on our website. We frequently highlight and tend to showcase guests.

Leave a Reply