From individual skills to business development, data professionals have many opportunities in the next few years.

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Photo by Paweł Czerwiński on Unsplash

In response to an atypical year, companies rely on data and analytics leaders to accelerate innovation and create new routes to generate revenue. However, recent research involving business leaders in the U.S., U.K., and Germany shows a growing concern around employees’ lack of analytical skills [1].

Although there are many challenges ahead, the same research reported that more than half (52%) of companies and organisations are looking to foster a data-driven culture. This is an excellent opportunity for data professionals seeking to climb the corporate ladder and those in a career change to Data Science. …

Don’t dwell on questions that already have answers. Here are three things I wish I knew before starting my career transition to Data Science.

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Photo by Ross Findon on Unsplash

You have reached a point in your career that it does not make sense to continue doing the same thing. Maybe you are bored, don’t earn as much as you deserve or, like me, simply never liked your job. Amidst a career turmoil, you came across data science and noticed there is a massive opportunity by switching careers. Also, you have found several coding tutorials on YouTube by Data Scientists.

However, despite many experts online, maybe a few of them have been in a career transition to data science. Probably even fewer did such a change from a completely unrelated field in their late 30s. This suggests that what you have been watching/reading may not apply to your reality. That said, you should watch those videos with a pinch of salt. After all, you do not want to waste your valuable time. …


Keep an eye on the latest development in the field of Artificial Intelligence.

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Source: Photo by Amanda Dalbjörn on Unsplash

As a Data Scientist and an Artificial Intelligence aficionado, I am always looking for whatever is new and innovative in our field. For this reason, it is crucial to keep up with the latest news in different blogs. Also, I find inspiring to read and follow the work of some of the top influencers in the field of Artificial Intelligence.

If you are like me, or just want to see who is doing what out there, then here is a list of the top influencers in AI, besides Elon Musk, to keep an eye on for the next few years. I have included their Twitter account and additional links in case you want to learn more about them. …

There is a growing obsession with hard skills, but soft skills can make you stand out from the crowd

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Photo by Mitul Grover on Unsplash

The number of Data Scientists continues to grow every year, and the markets seem to accommodate every one of us. Luckily, there is no sign of slowing down. According to the US Bureau of Labour Statistics, the need to manipulate data will roughly rise to 11.5 million job openings by 2026 [1]. Also, Data Scientists salaries have reached an average of $120,000 in the US [2].

However, the competition for certain tech-industry positions is exceptionally high, and it looks like it’s going to get tighter. There are many bootcamp releasing ‘batches’ of Data Scientists and recent graduates from multiple universities seeking for jobs, let alone those in a career transition. …

Match filenames using patterns

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Photo by CHUTTERSNAP on Unsplash

The most common operating systems for computers nowadays are Windows, Linux, and OS X, and they all come equipped with a terminal — also called shell. Linux and OS X are Unix-like operating systems. Because this system is frequently used by data scientists, I will focus on Bash — which is a type of Unix shell [1]. So, let’s get started.

If the only way you know how to copy, create, and find files when using the Command-Line is by passing arguments to cp—or avoiding it altogether — then keep reading. Wildcards are used to create patterns that match groups of filenames. These patterns are called glob patterns, which work like regular expressions (aka regex), but with different rules. …

Switching career to Data Science is challenging, but certainly possible.

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Photo by Sven Mieke on Unsplash

You have been flirting with Data Science for a while and decided it is the right career for you. That is great, most people struggle with the decision-making process and never make it that far. However, the obvious question is: “how do I make a career change to Data Science?” You will also realise that each of us has a different background; may that be academic, financial, or personal. Therefore, there is no silver bullet when it comes to your career change.

There is a lot of information you can pick up along the way both with current Data Scientists and like-minded people. But remember that changing career to Data Science is definitely not the same as having evolved into a Data Scientist role. So, learning and sharing experiences with those on a similar journey may be one of the best things you can do throughout your career change process. For this reason, I have decided to write this article to share my experience so far. …

Silicon Valley isn’t your only option. Data Scientists are in demand worldwide and speaking both English and French might become a valuable skill.

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Photo by Stephen Leonardi on Unsplash

The job landscape for Data Scientists is promising. According to the US Bureau of Labour Statistics, by 2026, there will be roughly 11.5 million job openings [1]. These numbers suggest that companies outside Silicon Valley recognise the importance of data professionals to their business. As a result, both experienced professionals and those in a career change to Data Science can expand their horizons. Although, Silicon Valley is still the number one area for data professionals — and with the highest average salaries — it’s not the only option.

Based on the growing demand in different industries, the list of cities below are on a positive trend for Data Scientists. Some of the factors influencing the list below include the number of hiring companies, government investment, collaboration between academia and industry as well as salary. Interestingly, leading cities are no longer exclusively English-speaking. Investments in Data Professionals and Artificial Intelligence are also directed to French-speaking areas. The French-speaking trend might gain thrust now that the UK has left the European Union. …

Making Data Science inclusive for colour blind professionals and taking your data visualization skills to the next level.

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Photo by Robert Katzki on Unsplash

As a Data Scientist in business and corporate environments, the goal is to make your work easy to understand and visually appealing, especially to non-data professionals. Graphs and colours, therefore, play a crucial role when communicating insights to business executives. So, make sure everyone in the room can understand your work.

Colour blindness is a genetic condition that makes it difficult to distinguish between specific colours, usually red and green. Colour blindness affects approximately 1 in 12 men (8%) [1]. This means that in the UK, there are about 5.6 million colour blind individuals. Globally, colour blindness affects approximately 300 million people, roughly the entire population of the United States. …

Getting Started

Data cleaning can be time-consuming, but understanding the different types of missing values, and how to deal with them, will significantly increase your productivity.

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Photo by Max Duzij on Unsplash

According to IBM Data Analytics, Data Scientists can spend up to 80% of their valuable time simply finding, organising and cleaning data [1]. So, it’s natural that you want to increase productivity on data cleaning to get back to what matter most — generating insights. You will need to quickly handle a common type of messy data: missing values, also known as Not a Number (NaN).

In practice, some datasets arrive with missing data. For example, people usually don’t like sharing their income in online forms and surveys, so they will either leave it blank or input something completely unexpected. …

Are you seeking a niche that will grow in the next decades? Look no further, neuroscience and technology are driving a profitable industry called Neurotech.

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Photo by Jesse Martini on Unsplash

Neuroscience is today what health and nutrition were 50 years ago. Back then, many of us began to adopt technical terms restricted only to the medical community — for example, saturated fat, glucose levels, and HDL (aka the good cholesterol).

As a consequence, we became demanding consumers when it comes to personal health. The heath and food industries have listened to us, and now, we have access to a range of organic food delivered to our doors, apps tracking menstrual cycles, and DNA-based diets. …


Renato Boemer

Entrepreneur with a degree from the University of Cambridge. Combining Data Science, Artificial Intelligence and Neuroscience.

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