Practical Data Science Skills for Current Jobseekers

The latest World Economic Forum’s Future of Work Report notes that humans and machines will spend the same time on business tasks by the end of 2025. This means that big data, machine learning, and artificial intelligence technologies will take the center stage in enterprise operations. Among the professionals who will be instrumental in realizing this future are data scientists.

But what are the skills for data science, and how much will they be in demand? Well, the Bureau of Labor Statistics estimates that careers in data science will grow two-fold between now and 2029. Typically, data scientists leverage mathematics, statistics, software, and programming skills to gather, analyze, infer, and draw customer insights from data. That said, here is a complete guide on the 2022 data science skills checklist.

What Are the Skills for Data Science Professionals?

Employers often look into certain skills and capabilities when hiring professionals to uncover information and insights from big data. Data science skills sets are segmented into technical and soft capabilities, which when combined enable data scientists to bridge the gap with all business stakeholders and achieve desired outcomes. Here is a close look into data scientist required skills.

Technical Data Science Skills

Some of the most important data science skills that employers consider before hiring candidates include:

AI/Machine Learning
Organizations are moving away from traditional AI/machine learning algorithms, originally termed “black boxes”, thanks to their ambiguity in deriving predictions, based on respective inputs. Current job seekers in the data science field should be well-versed with explainable machine learning technologies, such as SHAP and LIME. These innovative techniques help data scientists to create explanatory models with better logical communication for predictive options. Other algorithms that you should understand in this file include Naïve Bayes classifiers, decision trees, and random forests.

Statistical Analysis
A typical day for a data scientist involves applying multiple statistical techniques and concepts to big data environments. With that in mind, it will help if you’re good at statistical analysis and its application to data science, such as probability, variance, distribution curves, as well as standard deviation. Having a technical background in these concepts will help you to gather, aggregate, analyze, and interpret data, to present findings to business stakeholders. In other words, you need statistical analysis skills to help your potential employer draw insights from raw data.

Understanding of SAS and Other Analytical Tools
Employers require data scientists to have a strong, technical background in Statistical Analysis System (SAS) for business intelligence, and other associated tools for exploring and drawing business insights from large data sets. The adoption of SAS analytics is also increasingly becoming popular in machine learning and data science. Besides using SAS to create easy and insightful analytics, professionals in data science should also be able to integrate the solution with other basic business tools, such as Excel and Outlook.

Software Engineering
Aspiring data scientists should have software engineering skills, especially if they are going to build and deliver multiple machine learning modules in the backend. All this typically happens in full-stack development, which requires in-depth proficiency in Python, R, Java, JavaScript, and Scala programming languages. Other software engineering skills for data scientists are DevOps and MLOps. DevOps and MLOps are both software development strategies that focus on collaborations between developers, operations, and data science. While DevOps focuses on application development, MLOps focuses on Machine Learning.

Big Data
Big data, the backbone of data science, is arguably one of the fastest growing sectors globally, now standing at $274.3 billion. This is one of the most in demand data science skills. Job candidates in data science-related fields should understand the basic concepts of big data, types of data in big data environments, as well their applications. These skills should extend to the 5 Vs of big data, including volume, value, variety, velocity, and veracity. Lastly, a data scientist should understand the 5 A’s of big data, such as agility, accuracy, adoption, automation, and accessibility.

Writing SQL Queries & Building Data Pipelines
Although data scientists don’t take fundamental classes on programming and coding, they learn these skills out of necessity, to perform their roles in big data environments, such as writing SQL Queries and building data pipelines. A recent survey by Kaggle, a subsidiary of Google reveals that nearly 80% of data scientists use Python programming language, while another 40% use SQL in their current job positions. All the same, prevalent programming skills for data science that most employers look at include Java, C, C++, and Julia.

Data Analysis
Another thing that shouldn’t miss in your data science skills checklist includes data analysis, which will help you extract data from secondary and primary sources, before reorganizing the same in easy-to-read formats. That means having an in-depth understanding of Structured Query Language (SQL) to process or compute voluminous data sets. You’ll also need hands-on skills in spreadsheet presentations, given that your employer might need summarized spreadsheet reports.

Data Visualization
Data scientists are involved in the whole process of data analysis, from the top to the bottom, including presentation, which requires strong data visualization skills. This proficiency extends to knowledge in prevalent tools, such as D3.js or Tableau that can help you create various types of data visualizations, such as:

• Scatter plots
• Pie charts
• Bubble charts
• Line and bar graphs
• Heat maps
• Histograms

Hands-on skills in these types of data visualization as well as the respective tools for creating them will help you communicate business-ready insights in a detailed, meticulous storytelling approach for in-depth comprehension.

Data Wrangling / Feature Engineering
One of the top technical data science skills in 2022 is feature engineering or data wrangling. Typically, having data wrangling skills means you can transform one data set format to another as you build models, explore feature upgrades, or perform deep dives. At the same time, you should be able to extract features from raw data using your feature engineering skills.

Natural Language Processing
Technical proficiency in natural language processing, popularly abbreviated as NLP is a must-have skill if you want to be employed as a data scientist in any industry. As an extension of artificial intelligence (AI), NLP is increasingly becoming popular in the race to business automation. Organizations are already employing their use cases in building chatbots and virtual assistants, as well as monitoring consumer engagement on social media platforms. As a data scientist, you’ll also use NLP to run sentimental analyses of target audiences or extract specific texts from big data.

Deep Learning
A majority of data scientists work with AI and Machine Learning technologies. However, there is a rising trend in employment, where these professionals are hired to execute machine learning applications using advanced methods, such as deep learning. Data science skills to learn in Deep Learning include neural network application in creating advanced analytical models. Typically, you’ll need hands-on skills in various algorithms, such as

• Convolutional Neural Networks (CNNs)
• Long Short Term Memory Networks (LSTMs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptrons (MLPs)
• Self Organizing Maps (SOMs)
• Deep Belief Networks (DBNs)
• Restricted Boltzmann Machines (RBMs)
• Auto-encoders

Soft Skills Needed for Data Science

As noted earlier, there are various data science soft skills that can enhance your chances of getting employed, including:

Data Intuition
Data intuition is probably the most critical among all soft data scientist skills, especially when it comes to big data environments. A good professional in this field should have innate intuition that enables them to look beyond apparent reports of data analyses to draw valuable business insights from them. However, acquiring this soft skill should worry you that much if you are a junior data scientist as it comes with experience and exposure to different big data projects.

Great Communication
It goes without saying that an ideal candidate for any job position should have strong written and oral communication skills, data science positions not being an exception. Your skills should extend beyond gathering and extraction of data to great communication skills which will enable employers to gain from your services. Moreover, mastering good communication skills means a successful career as you can always communicate your thoughts and ideas without shying off, something that can propel you to senior data science positions.

Proficient at Working with Unstructured Data
Working with unstructured data can be pretty much challenging, as opposed to structured data. That’s why employers look for skills beyond technical proficiency in unstructured data tools. For instance, a data scientist who exhibits patience can wait for relatively long periods to detect new and changed data. At the same time, patience comes in handy because you have to manage and analyze unstructured data manually, as well as derive valuable insights from it.

Developing Metrics
A good data scientist should be able to measure results against the effort put into work. That means having soft skills in metric development, which essentially translates to creating a metric, writing it in code, and automating a pipeline for periodic monitoring. Most importantly, you should be creative in coming up with metrics that other members of the organization can easily follow and relate to. On top of that, a data scientist should know how to calculate and present metric findings to business stakeholders.

How to Improve Your Data Science Skills & Learning Resources

Data science can be a lucrative and fulfilling career in the long term if you know how to make yourself indispensable in your new organization. Among the tips that most professionals employ is improving their skills. With that in mind, here is how to improve data science skills through online courses and self-learning websites.

Online Data Science Courses

One of the best places to learn or improve your data science skills is online, where several courses are available for both beginners and ongoing. For instance, you can improve your skills by registering at:

• Harvard University Data Science Certificate: Covers various data science fields and awards credits in at least 4 certificate courses, including data sampling, data analysis, data prediction, data management, and communication.
• Code with Google – Applied Computing Series: Offers an advanced Machine Learning Crash Course through video tutorials, hands-on exercises, interactive sessions, and case studies.
• California Institute of Technology Learning From Data Course: Caltech’s professor Yaser Abu-Mostafa can give you video course instructions on data science algorithms, as well as features a Q&A.
• Master of Information and Data Science (MIDS) at UC Berkeley School of Information: Offers live class courses for ongoing data science professionals to hone their skills when it comes to solving problems using complex data.
• Udacity: Offers online lessons that teach in-demand tech skills to help practising data science professionals be ready for future jobs.
• Coursera: Offers industry-recognised Azure Data Scientist Associate Certificate, which is ideal for both experienced and entry-level data analytics professionals.
• Cloud Academy: Teaches practical data science skills with a special Python course. Users can access the digital training tools remotely.
• Udemy Courses: The platform matches data science students with global tutors for tailored courses. Alternatively, learners can purchase tutorial videos that are published every month.

Online Data Science Tutorials

Innovative data scientists should always learn on their own to improve and stay on top of emerging big data trends. Prevalent data science tutorials that can facilitate this include:

• Codementor: Offers beginner and professional tutorials on data analysis, an introduction to machine learning, and how to choose the right software package for data analytics.
• Topcoder: This source gives you access to industry experts for career insights, as well as video tutorials that teach the basics of data science concepts. It can be a good place for networking too.
• KDnuggets: Offers beginner tutorials on the whole concept of data science, from gathering, sorting, analyzing, and interpreting, to visual presentation.
• Flowingdata: Dr. Nathan Yau, Ph.D., prepares course tutorials that help data science professionals to improve their presentation, analysis, and understanding of data with regard to current trends and real-life scenarios.

Other Resources for Improving Your Data Science Skills

• The Open Source Data Science Masters: You can sign up on this resource to access fats science tutorials, books, or even online study groups to improve your current skills.
• Learn Data Science by nborwankar: Offers data science tutorials through IPython Notebooks that cover linear regression, data analyses, and random forests, among other data science fields.
• Data Science Weekly: Data science professionals can sign up for weekly newsletters from this resource to get updated on the latest job listings, trends, and industry news.
• FiveThirtyEight: Data scientists who want to specialize in politics, economics, health, or sports niches can check this resource for relevant podcasts and insights from industry experts.
• Simply Statistics: This resource offers a heap of articles authored by Jeff Leek, Rafa Irizarry, and Roger Peng, to help data scientists improve their skills.
• International Conference on Machine Learning: The conference supports budding professionals in machine learning to increase the tech’s awareness and support its application in various fields of data science.
• Reddit: This platform offers a well-connected online community of data scientists and related experts who share valuable insights to help each other solve complex problems using data.

Summing Up

Data is arguably the new digital gold, taking over key business operations at a faster rate than you can imagine. A profession that touches on business data, such as data science has a promising future, as far as modern enterprise applications are concerned. Use this guide to know the data scientist required skills in your respective field to get a certification or improve your proficiency. You can also check our blog section for insightful articles on data science and engineering professions.