Current status and prospects of the data science job market

Image source: Created by Pressfoto @ Freepik

The Data science job market has been booming in the last decade. Data scientists were one of the most in-demand technical experts in the IT industry. Research shows that the average number of job postings on famous job portals increased by about 30% year over year during 2013–2019, and overall, the data science job market showed 344% growth during the same period [1]. In fact, during the last decade, everybody agreed with how Harvard Business Review [2] saw the data science career: the sexiest job of the 21st century!

However, the growth of the data science job market slowed down…

A brief look into the role of data engineer

Image credit: Brett Sayles @ Pexels.com

Data engineering is one of the booming fields in the big data landscape. The data engineering job market is steadily growing in the previous years. In 2020, there were 40% more interviews for data engineer jobs than the interviews conducted in 2019. Companies are facing more difficulty in filling the data engineering roles and recruiting experienced data engineers.

Data engineering is one of the most critical roles in the big data industry: the intersection of big data, cloud, DevOps, and software engineering. A data engineer is an expert software engineer with in-depth skills in building and maintaining large-scale big data…

Why do you need more preparation to become a data scientist nowadays

Image Source: Wes Hicks @ Unsplash

There have been about nine years since Harvard Business Review announced that data science is the sexiest career in the 21st century. Since then, many aspiring data scientists started to upskill themselves and approach the job market, and some of them successfully transitioned into data science jobs. In 2021, the data science job market is still growing. Despite a slowdown in the job market during 2020 due to the Covid-19 pandemic, data science is still a lucrative career for young professionals. …

5 ways to benefit from an early start in your data science career

Image credit: Brian Erikson @ Unsplash.com

One of the questions I receive the most from talents interested in a data science career is that “When is the best time to start working in data science?”. The answer to this question can be straightforward and generic: “The sooner, the better, especially while you are still a student!”.

Yes, that’s true. It is always better to start building up your data science career while still a student (even during your Ph.D. studies if you are doing one) or very early in your career. When I started my data science journey, I was just at the end of my…

Natural Language Processing

Top natural language processing tools to learn

Source: Mvdheuvel @ Unsplash.com

Natural language processing (NLP) is one of the areas in artificial intelligence that deals with the interaction between humans and machines through natural language [1]. According to the “State of AI Report 2020", NLP is one of the most trendy AI technologies areas unlocking many new use cases in the past few years by the emergence of sophisticated language models such as transformers [2]. NLP is all about how machines can understand human languages and replicate the human language abilities to automate the time-consuming human tasks or extract knowledge from the text data to generated actionable insights for business users.

Technical skills necessary to grow as a data scientist

Image source: Pixabay @ Pexels.com

The emergence of data science in many industries has attracted millions of fresh talents to grow their computer programming and machine learning skills and land a data science job in the past few years. As data science projects are mostly done within the framework of enterprise software projects, software engineering skills are mandatory for data scientists to perform. In this article, we will discuss core software engineering skills that are required for aspiring data scientists:

Object-Oriented Programming

Computer programming is probably the most critical part of a data science job. Programming skills are one of the crucial abilities required for…

Career strategies for AI & data talents

Source: Freepik

Data science is an interdisciplinary field with so many related areas, such as machine learning or big data. As the industry moves forward, the similarities and distinction between data science-related fields are also defining more concretely. Machine learning engineering is a very close field to data science, and in some companies, there is almost no distinction between these two career paths. What is precisely machine learning engineering?

Machine learning engineering is a sophisticated software engineering area that focuses on developing smart software that can automate human-like tasks with the power of artificial intelligence and machine learning technologies. Compared to data…

Emerging trends of the data science job market

Source: Pexels.com/@picography

There was a time when having a solid background in math, probability & statistics, linear algebra, and some machine learning plus having great data analysis and presentation skills were enough to make it in data science. The traditional role of a business intelligence specialist has matured into data science. In recent years, however, data scientists’ job description has been shifted from pure data analysis contains more skills from big data, machine learning, and cloud technologies rather than conventional analytics. Also, having substantial expertise in front-end and back-end software development and DevOps is becoming necessary for a data science toolbox.


Soft skills to develop for data science consulting career

Source: Freepik

One famous notion in the consulting industry is that “data science is not about science!”. When we hear this notion for the first time, it sounds a bit odd because, after all, data science has vital routes in highly theoretical fields like machine learning and statistics. But maybe what this notion wants to point out is that there is much more to data science in the consulting industry than just working with statistical methods or machine learning.

Of course, data science is a highly interdisciplinary field built as a combination of machine learning, statistical data analysis, business analytics, and software…

What it takes to be successful as a data science consultant

Photo Credit. Fauxels @ Pexels.com

There was a time that I thought you could be a top data science consultant by having excellent core tech skills like machine learning, data analysis, and software engineering. When I started working in my data science consulting job, I was excited to work as a consultant for a known global brand. I was very motivated to put the solutions that I learned through my academic and startup career (with studying papers, reading code repositories, and taking online courses) into practice for very large corporations.

It took me years to become an experienced data science consultant who can work independently…

Pouyan R. Fard

I write about data science, tech, AI, and startups. My data science consulting agency: fard-ai.com

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store