Data Science Skills You Should Learn in 2023

This year there are various exciting developments regarding tech and data. For instance, the application of Artificial Intelligence (AI) is continuously increasing, reaching $136.6 billion as of 2022, according to Grand View Research. Moreover, AI will contribute to 26.1% of China’s GDP by 2030. Thus, it’s no surprise that AI integration with business is a hot topic amongst 77% of companies today.

Since AI applications are ever-growing, you have probably reached this article to explore data science skills that will likely remain in demand this year. So, without further ado, let’s dive right in.

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The Core, the Foundations, and the Technical Skills

These are skills you’re likely going to want to learn as a data scientist this year:

  • Core Skills
  • Linear Algebra
  • Statistics
  • Microsoft Excel 
  • Soft Skills
  • Decision Making
  • Data Science 101
  • Foundations
  • Data Visualization
  • Business Intelligence
  • Exploratory Data Analysis (EDA)
  • Technical
  • Machine Learning
  • Neural Networks
  • Hadoop
  • Cloud Computing
  • Packages and Software
  • Deep Learning
  • Database Knowledge and Management
  • Data Wrangling
  • Mathematics
  • Statistical Analysis
  • Big Data

If you’re interested in learning some of these skills, Learner’s Guild is a great place to start. Learners Guild is the alternative to college for aspiring software developers. Most of the learning is based on team projects, so these aspiring software developers understand the application of the software they create. What’s more, is that they even have a campus in Oakland, so if you’re a resident there, you can easily get a course!

Now, for readers not native to the world of data science, there are some non-technical data science skills they should consider honing. This includes understanding the impact of data governance and security, its dependence on the regulations, and the legalities of where this applies. Additionally, skills such as communication, being a team player or a leader, having business acumen, critical thinking, intellectual curiosity, and data intuition play a significant role in individual career growth within this field.

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Programming Skills

When it comes to programming, some skills need to be locked on. You’ll likely be asked about the following programs if you’re applying for a tech role, more specifically, as a developer, tech support, or data analyst. First, you can look into a deeper understanding and application of Python or Flask, a python-based web framework for web applications. On the other hand, Python has been trending for the last decade, as it’s the most sought-after programming language, specifically for machine learning. You’ll also need to look into data warehousing, such as SQL – to learn to organize data and Apache Spark to run data queries.

Reading up and joining social communities relevant to these programs is best. However, for non-technical readers, all hope is not lost, as businesses still need account managers, business strategists, and communication specialists. Some of the non-technical skills include:

  • Building Team Work Synergy
  • Data-Driven Decision Making
  • Creativity
  • Data Intuition
  • Strong Communication Skills
  • Intellectual Curiosity & Passion

You could also look into streamlining workflow processes, client management, and even taking up the ‘ABCDE Organizational Challenge Skillset.’

The ABCDE Skillset

These skill sets can help you find a direction to find a specialization and help build strength. The abbreviations of this skillset may vary, but the underlying idea is Analytics, Business Acumen, Coding, Domain Knowledge, and Explanatory skills. Most of these are self-explanatory. However, it’s crucial to understand that developing these skill sets can help individual job growth.

Now that we’ve covered the skills required to become a data scientist, let’s look at some jobs that are highly in demand regarding.

  1. Data Scientist

As per Coursera (last updated early this year), a data scientist’s median salary is around $103,104 per annum. Data scientists often work on data-based predictions and develop forecasting models to help in business intelligence. In addition, they work in organizations as third-party contractors supporting business intelligence-related decisions.

  1. Data Analyst

From the same source, data analysts make a median salary of $67,150 per annum. Their job centers around collecting, analyzing, evaluating, reviewing, and organizing data. These data analysts often have to perform statistical calculations in search of data trends. 

  1. Data Engineer/Architect

A data engineer and a data architect earn between $94,067 per annum to $119,156 per annum. A data engineer is responsible for building systems that automate tasks and help data analysts interpret data faster. On the other hand, a data architect creates system plans to manage and organize data more innovatively and efficiently.

  1. Machine Learning Engineer and Business Intelligence Engineer

With a median salary between $108,402 and $94,607 per annum, these job titles have been trending higher since mid-2022. Where machine learning engineers specialize in designing architecture centered around AI, business intelligence engineers design/install and maintain data systems that can analyze large data clusters. This profession helps identify and fill any gaps in performance, thus boosting results.

To Land a Job in Data Science – In a Nutshell

If you’re thinking of landing a job in data science, consider taking a step back to understand the core, the foundations, and the technical skills in this field. Next, you’ll need to identify if you’ve got what it takes to be a technical specialist or if you’d rather have a non-technical role within this field. Furthermore, you’ll need to orient yourself with a few programming languages, if not dive deeper into understanding the business application of such programs. 

Apart from programming, you’ll also need to understand statistics, data manipulation, data visualization, machine learning, and improving communication. Furthermore, for continuous growth, it’s essential to follow these tips:

  • you’ll need to look out for certificate courses
  • build on a reading habit
  • be an active member of the data community
  • participate in open projects. 

These skill sets are critical for individuals that wish to climb this career ladder. By taking a data science course, you can gain practical experience in using these skills, which are extremely beneficial to further enhance your knowledge and understanding of data science and land a lucrative job in the field. The process may be tough but not impossible. Hopefully, this article will guide you on the path to obtaining beneficial skills and knowledge that will help you land a promising data science job in 2023.

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