If you want to be professionally successful, a good idea is to invest in data science. With the amount of information generated every day, these professionals are increasingly needed by companies, because it helps to make more informed and informed decisions.
Data scientists are professionals who are responsible for making the company useful for a large number of structured and unstructured data that is always available through the internet.
This tempting career requires a candidate with a variety of skills and requirements, but the question is: how do you enter this profession? In this post, we will outline the key steps you must take to build a successful career in data analysis.
So, what if you know where to start? See tips from now!
Market Trends for Data Scientists
The job market lacks data scientists – even careers that are qualified by the World Economic Forum and released by infoMoney as one of the most relevant in 2020.
According to IBM, the demand for people with data scientists will increase by 28 percent by 2020. This function, which previously existed only in innovative companies that wanted to stand out in the marketplace, became a necessity for businesses to remain competitive. This fact explains the gradual increase in the demand for people with these skills and what contributes to the growth of professions and training courses.
Professional profile with training data scientists
In addition to looking for insights into large volumes of data and translating them into business languages, traders must be able to use the most sophisticated data mining and visualization tools available. In addition, manage and handle this large amount of information through:
Exploration analysis;
Data visualization
Machine Learning;
Deep learning;
Combining predictive modelling, among the other skills we will highlight below.
Skills that must be developed
Professionals who will act as data scientists need to have basic skills. Among others are:
Mathematics
Data scientists work with numbers: problem solving, statistics, probabilities, Sigma Notation, Bayesian Inference, among other subjects related to mathematics.
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During large database manipulations and analyzes – you need to run your own tools and solutions for this activity and programming languages, such as Python and R, are present in almost all databases and also include SQL, MatLab, MongoDB and Spark.
English
This is not a mandatory requirement to qualify during the training of a data scientist, but having knowledge in languages can be a great competitive advantage.
Creativity
The purpose of being a data scientist is to uncover insight and produce intelligence. Creativity is also relevant because it allows professionals to anticipate business needs, especially those that have not been done by any segment.
Logical thinking
Logical thinking is another thing that is relevant because it helps to analyze and streamline the learning process in Data Science. Finally, it is necessary to have abilities with numbers, because they are used in different aspects, including in machine learning algorithms, in-depth learning and statistics, which are important parts of Data Science.
Storytelling
Finally, it is worth creating data storytelling skills – because it helps to transform collected items into elements for visual narratives and facilitates decision making – and data mining, a process that identifies relevant information to gain knowledge about the business, product or competition.
