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Last January, the prestigious job search site Glassdoor voted the job of Data Scientist as the number one position in its top 25 best jobs in the world. Through this article, discover the skills needed to practice this profession at the heart of Big Data.

In charge of managing, analyzing and exploiting massive data within a company, the Data Scientist is the evolution of the Data Analyst in the Big Data era. According to the study conducted by Glassdoor, the average annual salary of a Data Scientist is $116,840.

Given the extreme specialization required for this profession, hiring opportunities are numerous and far outnumber the number of qualified profiles. At the end of January, Glassdoor counted 1736 job offers.

There is no doubt that the Data Scientist job is exciting. However, it is also a highly responsible position that requires natural predispositions and a high level of education. Here are the essential skills to hope for a career in this field.

How to become a Data Scientist? Required training and skills

Understanding the basics of Data Science

A Data Scientist must master the fundamentals of data science. Many beginners make the mistake of applying Machine Learning methods without understanding the basics.

This is a mistake. The expert must be able to differentiate between Machine Learning and Deep Learning, and distinguish Data Science from business analysis and data engineering. He must also know the most commonly used tools. Finally, he/she must be able to distinguish between regression and classification problems, as well as supervised and unsupervised learning.

Training in data analysis

Currently, 88% of Data Scientists have at least a Master’s degree, and 46% of them have a PhD. This educational background seems necessary to develop the level of knowledge required to practice this profession.

Programming languages

Data Scientist jobs require the mastery of at least one programming language. The most commonly used is Python, but it can be replaced by R, Java, Julia, Pearl or C/C++.

As a general rule, Python is preferred because it is a general-purpose language with many libraries dedicated to data science. On the other hand, R is a language dedicated to statistical analysis and data visualization. Julia brings together the best of both worlds and is faster.

The increase in computing power of computers is the source of the rise of Machine Learning, and programming languages allow us to communicate with these machines. Even if you don’t have to be the best programmer in the world, a data scientist must know how to use them.

Know how to analyze and manipulate data

It may seem obvious, but a Data Scientist must be very comfortable with data manipulation and analysis. Data wrangling is the process of manipulating data, cleaning it up and transforming it into a format suitable for analysis. This step is necessary to simplify data analysis and improve its results.

The purpose of data analysis is to learn from the data. We use Excel, SQL, or Pandas on Python for this purpose. This is the core of a Data Analyst’s job, but the Data Scientist’s job goes further by using Machine Learning.

Data Visualization

Data Visualization consists of presenting the results of data analysis in the form of graphs, charts or other diagrams. This allows the audience to interpret the results much more easily.

There are many tools available to perform this task. Data Science programming languages such as Python offer different libraries for creating advanced graphs. We can also mention specialized software such as Tableau.

Machine Learning

Machine Learning is the skill that really differentiates the Data Scientist from the Data Analyst. It is used to create predictive models, based on past data to predict future trends.

Various Machine Learning algorithms such as linear regression and logistic models are used to solve various problems. A data scientist needs to know the code of each of these many algorithms, but more importantly how they work.

This way, he can choose the right model for the problem at hand. He can also configure the hyperparameters and reduce the error rate of his model.

Deep Learning

Deep Learning and artificial neural networks are a subcategory of artificial intelligence, on which many recent innovations such as autonomous vehicles or DeepFakes videos are based.

The rise of this branch of AI is linked to recent advances in storage and computing capabilities. A modern Data Scientist must have some knowledge in this field.

In order to master Deep Learning, it is necessary to master a programming language such as Python and to have some knowledge of algebra and mathematics. Libraries like TensorFlow, Keras and PyTorch are also essential tools.

Understanding linear algebra and functions of several variables

Linear algebra and functions of several variables form the basis of many statistical computing and machine learning techniques. Even if implemented with R or sklearn, some companies with data-driven products may decide to develop their own implementations to improve their algorithms or predictive performance.

Using Hadoop

While not required by some companies, proficiency with the Hadoop platform is most often required. Likewise, experience with Hive and Pig processing tools is an added consideration for hiring. Cloud tools like Amazon S3 are also important.

Programming in SQL

Hadoop and NoSQL databases have largely taken hold in the Big Data field. However, most recruiters require candidates to be proficient in SQL programming in order to formulate and execute queries. In fact, SQL is trending to become the predominant language in Big Data again in 2016.

Managing unstructured data

To become a Data Scientist, it is essential to know how to manage unstructured data from social networks, or video or audio streams. This data is the main challenge of Big Data.

It is also important to know how to handle data with imperfections, such as missing values or inconsistent format strings. This skill is particularly important in companies that are not used to data analysis.

Software engineering

In a small company not used to data science, a data scientist must have software engineering skills. These skills will allow him/her to take charge of the development of a data-driven product or data logging.

Software engineering skills are essential for a Data Scientist to create Machine Learning models. The professional must know the basics of Software Engineering such as the life cycle of a development project.

Knowing how to write clean and efficient code is very helpful, and also allows for better collaboration with developers and the rest of the company’s teams. A solid foundation is a valuable asset.

Model deployment

Model deployment is often overlooked, but it is a crucial step in Machine Learning. It aims to enable end users to use the model, without the technical skills of a data scientist.

In general, this task of deploying and putting the models into production is taken care of by the Machine Learning Engineer, which can be seen as an evolution or specialization of Machine Learning. The Data Scientist able to deploy Machine Learning models brings immense value to his or her company.

Intellectual curiosity

Intellectual curiosity is essential to detect the most interesting and exploitable data within a huge volume of data. To be successful as a Data Scientist, it is necessary to be creative and ask your own questions rather than simply answering the ones that come up.

The data scientist must ask what causes an event and how it occurs. He or she must ask about the possible consequences of each change. Perpetual questioning is the most important “soft skill” of the Data Scientist.

It is this curiosity that will allow him to reach the final goal of the Machine Learning project, and to justify the results of his work. It will also allow him to keep abreast of developments in the field of Data Science and to continue to learn from day to day.

The Narrative

Raw data tables don’t speak to anyone. To convey and share the results of their data analysis, a data scientist must be able to tell a story in the form of data visualization.

Charts and graphs are interactive presentations that the human brain can understand in a natural and intuitive way. Storytelling is one of the main qualities of a Data Scientist.

Structured thinking

The best Data Scientists are able to break down a problem into multiple parts in order to solve it more efficiently. This is called structured thinking.

This is a very important quality to approach problems from different angles. Some people have this way of thinking innately, but it can also be developed…

The mind of an entrepreneur

In order to successfully exploit a company’s Big Data, it is necessary to understand the problems to be solved and the new possibilities that data can offer. This is why the Data Scientist must understand the business world in general and the industry to which he/she is affiliated in particular.

The sense of communication

Integrated within the company, the Data Scientist must imperatively be able to communicate his technical discoveries to other employees, in the marketing or sales departments for example. His role is to help the decision-makers to make the right decisions, by providing them with the necessary information.

He or she must also understand the problems of other teams and help them address these challenges through data analysis. To do this, it is also important to master data visualization tools such as ggplot or d3.js.

In conclusion, the skills required for a Data Scientist are numerous and specific. Before deciding to undertake a training or a career in this field, it is necessary to determine whether or not you have the profile of a data scientist.