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

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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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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…

Master the core component of every data science tool in Python

A woman in a yellow sweater seated at a laptop.
A woman in a yellow sweater seated at a laptop.
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If you’re learning Python and want to become a data scientist, then NumPy (as well as pandas) will be a crucial part of your work. So it’s paramount you get used to it. Data sets come from different sources and a wide range of formats, including collections of documents, images, sound clips, numerical measurements, or pretty much anything else (frequently with missing values). Despite this apparent heterogeneity, it helps thinking of data as arrays of numbers.

That said, manipulation and efficient storage of numerical arrays is vital to data scientists. …

The quest to find the best data scientists by solving social-oriented problems

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The vast majority of information on Data Science is most likely associated with the private sector, like tech companies, and programming skills to address corporate goals. However, globally, we have been facing multiple social challenges, such as climate crisis, clean energy, wildlife conservation, sustainable growing cities and many others.

In fact, the United Nations has a section on its website dedicated to ‘Big Data for Sustainable Development’ [1]. Similar Data Science techniques can help gain real-time insights into people’s lives and wellbeing and target to aid interventions to vulnerable groups. Modern sources of data (e.g. satellite data), new technologies, and…

Expand your horizons when job hunting for Data Science roles

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Are you either a Data Scientist actively seeking a job, or are you switching careers to Data Science and wants to know what is out there? If so, this article will give you some insights on where to work as a Data Scientist beyond the traditional tech companies.

Obviously, most data professions would be excited to work at one of the companies that form the widely known acronym FAANG (Facebook, Apple, Amazon, Netflix, Google). FAANG companies are at the forefront of technological development. …

How to adapt your codes by using methods optimised for speed in Python

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Data science is intimately associated with performance, whether it is about metrics (predictive model accuracy, precision, recall, F1 score or log loss) or hardware specs (RAM, storage and GPU). However, sometimes metrics or hardware specs are variables data scientists do have control over. Maybe the company has already established its key performance indicators (KPIs) and bought all the necessary equipment to start running it.

That said, one could argue that data scientists can influence performance by improving their ability to code. That is true, but more importantly, data scientists should know which methods to use in each. Python is a…

An entertaining analogy to a complex topic

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When your plan to become a professional Data Scientists begins to materialise, you will often have to answer the infamous question: “So, what do you do?”. If you want to become a machine learning expert, you will have a hard time trying to explain what machine learning is to colleagues who do not have a tech background.

Professor Tom Mitchell, a renowned computer scientist at the Carnegie Mellon University, defines machine learning as:

“A computer program that learns from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T…

Simple datasets remove the unnecessary difficulty when learning complex topics such in data science

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Machine learning is a branch of Data Science specialised in making computers act without being explicitly programmed [1]. Recently, machine learning has given us self-driving cars, precise web search, practical speech recognition and a massively improved understanding of genetics. Machine learning is so relevant that you probably use it many times a day without noticing it. So, it is not a surprise that machine learning expertise is on the rise in multiple industries [2].

However, machine learning is not an easy topic to learn. It requires a decent level of math and statistics, let alone programming skills. Still, to reach…

Why I have decided to join Le Wagon and what they have to offer

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If you want to become a Data Scientist, then, at some point, you have asked yourself whether it is worth joining a bootcamp. How do I know that? I have asked myself that very question multiple times. Honestly, there are plenty of reasons why you should not join a bootcamp: it’s expensive, you can find almost everything online, there are many free e-books, you can enrol in a Coursera degree for free and so on. However, there are a few good reasons that convinced me to join a bootcamp. …

Cheatsheets are lifesavers for aspiring and early career Data Scientists.

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Python is one of the easiest programming languages you can learn to become a Data Scientist (the other language is R), and there are plenty of free online resources to get you started. The process of becoming learning Python to become a Data Scientist can be broadly split into three subjects: learning a programming language (usually Python), data analysis (data visualization, math and statistics) and machine learning (algorithms that improve automatically through experience).

However, the first step, learning how to programme can take too long, and some aspiring Data Scientists may feel discouraged to achieve their professional goals. Why? They…

Renato Boemer

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

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