Demand for Data Skills has Skyrocketed
By: Benjamin Obi Tayo Ph.D.
Tags: Programming, Data Driven, Data, Machine Learning, Technology Skills

Demand for Data Skills has Skyrocketed

Top data skills you need in today’s data-driven world and how to acquire them

Photo by NESA by Makers on Unsplash

I. Introduction

In the past decade, the demand for individuals with data skills has skyrocketed. A recent study using data collected from LinkedIn shows that most of the top tech jobs in the United States and worldwide are related to data, as shown in the figures below:

Image by Benjamin O. Tayo

Image by Benjamin O. Tayo

The figures above show that most of the top tech jobs today’s world are related to data. As more and more companies are becoming data-driven, it is no surprise that there is a high demand for workers with data-related skills (data mining, data storage, data retrieval, data transformation and cleaning, and data analysis). We also notice that infrastructures such as Linux, Azure, and AWS that are used for large scale data science projects also feature among the top tech skills. This is because more and more companies are using cloud computing for data science and machine learning projects.

The growth in the demand for skilled workers with advanced data skills can be attributed to 3 factors:

The world is producing data at an unprecedented rate; as a result, data has become a new commodity with extremely high value. There is, therefore, a need for highly skilled individuals to mine, transform, and analyze the data. More and more companies are becoming data-driven. These companies are now creating teams of skilled workers that can work together to leverage the power of data for improving daily business operations or increasing sales and profits. Global competition in the leveraging of tech skills for optimizing business operations and for decision making is increasing. As a result, many companies are putting more resources into recruiting, hiring, and nurturing the right talent to remain in the global competition.

If you are considering joining the data workforce, you may be wondering about what data skills you need and how to acquire them.

II. Data Job Titles

Some of the top data job titles that are in high demand include the following:

a) Data Scientist

b) Data Analyst

c) Business Intelligence Analyst

d) Database Developer

e) Database Administrator

f) Data Engineer

g) Data Analytics Manager

h) Big Data Software Developer

i) Cloud Developer

j) Cloud Software Engineer

III. How to acquire data skills?

1. Massive Open Online Courses (MOOCs)

The rising demand for data science practitioners has given rise to a proliferation of massive open online courses (MOOC). The most popular providers of MOOC include the following:

a) edx: https://www.edx.org/

b) Coursera: https://www.coursera.org/

c) DataCamp: https://www.datacamp.com/

d) Udemy: https://www.udemy.com/

e) Udacity: https://www.udacity.com/

f) Lynda: https://www.lynda.com/

If you are going to be taking one of these courses, keep in mind that some MOOCs are 100% free, while some do require you to pay a subscription fee (it could range anywhere from $50 to $200 per course or more, varies from platforms to platforms). Keep in mind that gaining expertise in any discipline requires an enormous amount of time and energy. So do not be in a rush. Make sure that if you decide to enroll in a course, you should be ready to complete the entire course, including all assignments and homework. Some of the quizzes and homework assignments will be quite challenging. However, keep in mind that if you don’t challenge yourself, you wouldn’t be able to grow in your knowledge and skills.

Having completed so many data science MOOCs myself, find below are 3 of my favorite data science specializations.

(i) Professional Certificate in Data Science (HarvardX, through edX)

Includes the following courses, all taught using R (you can audit courses for free or purchase a verified certificate):

Data Science: R Basics; Data Science: Visualization; Data Science: Probability; Data Science: Inference and Modeling; Data Science: Productivity Tools; Data Science: Wrangling; Data Science: Linear Regression; Data Science: Machine Learning; Data Science: Capstone

(ii) Analytics: Essential Tools and Methods (Georgia TechX, through edX)

Includes the following courses, all taught using R, Python, and SQL (you can audit for free or purchase a verified certificate):

Introduction to Analytics Modeling; Introduction to Computing for Data Analysis; Data Analytics for Business.

(iii) Applied Data Science with Python Specialization (the University of Michigan, through Coursera)

Includes the following courses, all taught using python (you can audit most courses for free, some require the purchase of a verified certificate):

Introduction to Data Science in Python; Applied Plotting, Charting & Data Representation in Python; Applied Machine Learning in Python; Applied Text Mining in Python; Applied Social Network Analysis in Python.

2. Learning from a Textbook

Learning from a textbook provides a more refined and in-depth knowledge beyond what you get from online courses. This book provides a great introduction to data science and machine learning, with code included: “Python Machine Learning”, by Sebastian Raschka. https://github.com/rasbt/python-machine-learning-book-3rd-edition

The author explains fundamental concepts in machine learning in a way that is very easy to follow. Also, the code is included, so you can actually use the code provided to practice and build your own models. I have personally found this book to be very useful in my journey as a data scientist. I would recommend this book to any data science aspirant. All that you need is basic linear algebra and programming skills to be able to understand the book.

There are lots of other excellent data science textbooks out there such as “Python for Data Analysis” by Wes McKinney, “Applied Predictive Modeling” by Kuhn & Johnson, “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank & Mark A. Hall, and so on.

3. Medium

Medium is now considered one of the fastest-growing platforms for learning about data science. If you are interested in using this platform for data science self-study, the first step would be to create a medium account. You can create a free account or a member account. With a free account, there are limitations on the number of member articles that you can access per month. A member account requires a monthly subscription fee of $5 or $50/year. Find out more about becoming a medium member from here: https://medium.com/membership. With a member account, you will have unlimited access to medium articles and publications.

The 2 top data science publications on the medium are Towards Data Science and Towards AI. Every day, new articles are published on medium covering topics such as data science, machine learning, data visualization, programming, artificial intelligence, etc. Using the search tool on the medium website, you can have access to so many articles and tutorials covering a wide variety of topics in data science from basic to advanced concepts.

4. KDnuggets Website

KDnuggets is a leading site on AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning. On the website, you can find important educational tools and resources in data science as well as tools for professional development:

5. GitHub

GitHub contains several tutorials and projects on data science and machine learning. Besides being an excellent resource for data science education, GitHub is also an excellent platform for portfolio building. For more information on creating a data science portfolio on GitHub, please see the following article: A Data Science Portfolio is More Valuable than a Resume.

6. LinkedIn

As data science is a field that is ever-evolving due to technological innovations and the development of new algorithms, one way to stay current is to join a network of data science professionals. LinkedIn is an excellent platform for networking. There are several data science groups and organizations on LinkedIn that one can join such as Towards AI, DataScienceHub, Towards data science, KDnuggets, etc. You can also follow top leaders in the field on this platform.

7. YouTube

YouTube contains several educational videos and tutorials that can teach you the essential math and programming skills required in data science, as well as several data science tutorials for beginners. A simple search would generate several video tutorials and lectures.

8. Khan Academy

Khan academy is also a great website for learning basic math, statistics, calculus, and linear algebra skills required in data science.

IV. Summary

In summary, we’ve discussed some of the top data skills that are currently in high demand. As more and more companies are becoming data-driven, the demand for workers with advanced data-related skills will continue to increase. The skills to focus on in today's demand will depend on what tech sector one is interested in. For data analysts/data scientists’ job roles, it is essential to master skills such as SQL, Python, Machine Learning, and AWS.