Big data for decision making
Unleash the power of big data for decision making
While the world is still resting more on data, it is becoming increasingly valuable, provided companies can exploit it. This is where the data scientist comes in. Companies need specialized statistics and data modeling professionals to unleash the power of comprehensive raw data from many different sources: transaction logging, digital media and documents, databases, web and social networks, Internet of Things (IOT) sensors, and more.
Business intelligence, or “Decision Making Information” that companies extract from the data they collect, can be used to inform decisions in all kinds of domains, whether it’s new product development, from marketing campaigns or supply chain design. Companies also rely on this information to improve cybersecurity, but also employee retention, recruitment and productivity, as well as customer service and engagement, among others.
Since they can use decision-making computers in many different ways, they are looking for data scientists who know the economic world. Communication and other soft skills are also important. If these skills are so important, because data scientists often have to quickly and briefly present to non-technical employees all the risks, trends and opportunities that the company needs to monitor or exploit.
Data scientists often have to describe their analysis in writing or present their findings directly to company teams. In addition, the quality of trading is becoming increasingly important for this position.
Technical skills required
Data scientists must have a range of analytical and mathematical skills, not only basic mathematical knowledge, but also in areas such as multiple variable calculus and linear algebra. Data Science is essentially a combination of statistics, mathematics and computer science. Therefore, many employers are specifically looking for candidates with a background in statistics.
Machine learning skills are also in demand because they help data scientists identify the nature of the data. A specific experience of programming languages, e.g., Python (a generally easy-to-use flexible language) or Java (one of the oldest languages, applicable to almost all technology domains), is often part of the data job description. Scientist. Many companies also seek professionals who can work with languages such as R, which is used for statistical analysis, data visualization and predictive modeling, as well as with tools such as Tableau for interactive data visualization.
Key role of degree level for more experienced data scientists
Many companies prefer to recruit data scientists who have a PhD in an associated area, for example, mathematics or computer science. A Ph.D. can offer a step ahead of candidates in the recruiting process, and is even a requirement for certain positions. And while a PhD or other graduate degree is not necessary to obtain a startup data scientist position, degree level is likely to be more important when looking to advance your career plan.
Setting the stage for a career as a data scientist
If you are a student or graduate student and considering a data scientist career, the mandatory criteria for the position will largely depend on the employer, the technological tools used by the Company to manage its data, as well as time and resources. You can invest in data scientist training early in the career.
Acquire programming skills
The board may seem obvious, but it’s in your best interest to do so before applying for data scientist positions. Proficiency in basic languages, such as Python and SQL, will likely be required, but also take a look at the descriptions of the data scientist positions you target. What other types of languages do you focus on for positions early in your career? You will have a better idea of the direction to give your training.
Familiarize yourself with the data scientist community
Look for opportunities to meet online with data science professionals or those aspiring to become data scientists. To get started, start a search on LinkedIn groups. You can also read blogs on data science and follow influential data scientists.
Once you’ve made contact with a few well-established data scientists, request an informal interview to learn more about their careers. Also, don’t neglect mentors and professional contacts in their network.
They may have advice to get you started on the job, including putting you in touch with professionals of their expertise.
Start your own data science projects. Creating your own data science projects demonstrates your thirst for knowledge, which can give you a competitive edge in the recruiting process. This tells employers that you are motivated to acquire new skills, but also to use them creatively and innovatively just for fun. A quick online search will help you find a profusion of project ideas for beginners.
All of the points discussed here can prepare you to create a beginner data scientist CV that will hold the attention of a recruiting manager. You can also contact specialized recruiters to get their help. They can introduce you to companies and employers in your area that are likely to recruit early-career metadata experts, but they also provide valuable tips for writing your data scientist CV.
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