Data Scientists are hyperspecialized figures: a minimum of a bachelor's degree, more often a master's / doctoral degree. The course of study does not end with academic research, but continues with specialized courses outside the university environment.
Popular undergraduate programs among those now playing the role of data scientist are engineering, computer science, economics, mathematics, and statistics. There is no more suitable undergraduate program here, if only because only one out of three scientists (statistically) has gone through a multidisciplinary path. This is due to the fact that in order to become a given specialist, it makes sense to have a variety of skills: from technology to knowledge of the market and business, the ability to use machine learning methods, programming languages.
Analysts analyzed vacancies posted on Linkedin in 2018. As a prerequisite, it becomes necessary to use at least one programming language (in 74% of sentences) - R / Python. In 62% of cases, it becomes possible to develop and implement machine learning algorithms. By comparing this data with skills prevalent in companies (and machine learning and deep learning skills are present in one in five companies), skills are expected to increase in years to come. The ability to communicate and present results to business users is reported in 36% of the analyzed proposals. Extensive knowledge of mathematical-statistical models and algorithms, the programming techniques needed to implement them, and the ability to concisely and clearly describe the evidence - that's what the ideal "data scientist" drawing is.
What is the average Data Scientist salary? Research conducted by research institutes has shown that such specialists receive an average of $ 100,000 -110,000 annually. However, this situation is an exception (possibly due to a higher average age) - salaries are lower in other countries. Internationally, the average salary earned by a data scientist is $ 68,000. It is still considered high given the low average age of the respondents + and many of them come from areas where the cost of living is lower than Western countries.
To become a data scientist: an action plan
You need to be sure you want to become a data expert. Once you have this confidence, start collecting skills even before you go to university or go to study elsewhere. Learn programming languages - Python, Java. Keep updating your knowledge in applied mathematics and statistics. You will find them useful not only in your university studies, but also for your future work.
Get an appropriate education or supplement the existing one
This is a prerequisite for getting a job as a data scientist, so it is very important. Get your degree in data science if you can find the courses they offer. If not, get a degree in statistics, mathematics, or information technology. Learn programming languages as you study for your bachelor's degree. Look for internship opportunities to help you start building a strong network of local contacts.
Find jobs in this field or at least an internship
Even with your bachelor's degree, you can get a job: it will be an entry-level job, where you may not be paid as much as you planned, but that's okay. If you have certifications in related fields, your chances of getting an entry-level job as a data analyst or scientist are better. Look for certifications in business intelligence applications, relational database management systems, and data visualization software.
Get your degree
You will grow more tangibly in the field if you have a master's degree. Employers are looking for data scientists with a degree and industry experience. In addition to earning a master's degree, you should also know how to use enterprise-grade data management programs: Hadoop, MapReduce, and Spark. Most master's programs include the use of these programs in their resume.
The downside to a data science career is that it's constantly evolving. The software you can't do without today will be outdated tomorrow, which means you must remain relevant in this ever-changing field. You can do this by following present-day continuing education courses. It is possible to remain relevant to the industry by connecting online and participating in educational and professional development opportunities through boot camp and conferences.
Tips on how to get a job in data science
Remember: the step to becoming a data scientist is to get a job. Although, no doubt, it's a bit hard to find, especially if you're a recent graduate.
Tips to make your job search easier:
- Build an effective portfolio and get the right experience for that (internships and workshops while studying are great!). After completing projects, highlight the best ones by focusing on the bigger ones.
- Get a mentor. A mentor plays an important role in the aspiring data professional's career: it's a way to build a working network - a mentor with years of industry experience can tell you what companies are looking for.
- Participate in conferences and meetings. Look for data science lectures and participate on your own. Conferences to keep in mind include the Strata Data Conference, KDD (Knowledge Discovery in Data Mining), and the International Conference on Machine Learning (ICML). Consider Meetup.com, SF mining, and Data Science DC meetings .
- Use message boards: Kaggle, Datajobs, and Data Science Central.
- And don't be afraid to change your field if you were previously engaged in something else, but then realized that a Data Scientist career would suit you better!