Data science – the heart of machine learning and artificial intelligence ranks as the number one most promising job in America in 2019, Glassdoor.
It comes as no surprise about data scientists to top Glassdoor’s best job role in America. This has been happening consistently for the past three years with top employers seeking experts in the data science industry.
Currently, there are 2.8 billion active Facebook users generating a large number of data daily, says Statista. Next comes the question, how much data these users can generate in 2020 and beyond? Besides Facebook, Twitter users send almost half a million tweets every minute. Surprisingly, statistics from Facebook are only the tip of an iceberg, there are multiple other sources through which data is being generated every second.
- 92% of organizations are now investing in big data and AI, says NewVantage Partner.
- With the help of big data, Netflix can save USD1 billion per year on customer retention, a report by insideBIGDATA.
- 7 million jobs specifically in the field of data science and big data analytics by 2020 according to Forbes.
While the predictions are real, the problems associated with job opportunities are even bigger. The shortage of data science professionals persists. The demand for data scientists is estimated to grow by 28% in 2020, a statistics report by IBM.
Bolster your data science skills and become a data scientist today: –
Deemed as the hottest job of the century, a data science professional helps organizations interpret data, manage them to solve complex problems and predict actionable insights using expertise in different niches.
Data scientist qualifications
With technology growing rapidly, there is no specific career path or qualification to become a data scientist. However, you will find many expert professionals having a background in varied fields like mathematics, computer science, and engineering with exquisite programming skills.
Statistics is one major fundamental of mathematics that holds the key to data science. This helps data scientists analyze and interpret data, predict insights, and come up with hypotheses.
Other skills include: –
- R and Python programming – most data scientists know Python programming, however, having experience in both R and Python is an added advantage.
- Data wrangling – most data are often unstructured and messy which can be difficult to analyze. Thus, a data scientist must know how to deal with that kind of data.
- Machine learning – with the help of machine learning models data scientists can now easily make decisions and solve the most complex problems and come out with solutions.
- Data visualization – companies are now becoming data-driven. Such companies need to be specific with their decision-making for the first time. You should know how to visualize data using data visualization tools such as Tableau, ggplots, and matplotlib, etc.
- Predictive modeling – machine learning models such as logistic regression and linear regression are essential if you’re looking to create products based on the expected behavior of the user.
Besides these, problem-solving skills, soft skills, business acumen, and business acumen are other data scientist skills an aspirant should have.
As mentioned above, candidates need not have specific data science qualifications to enter the big data space.
Healthcare, pharmaceutical, internet industry, energy sector, telecommunication sector, and telecommunication industry are some of the top industries suitable for professionals looking to make their career big in the data science industry.
Tech giants and top employers are more keen to hire candidates with the required skillset and domain knowledge. Having said that, education providers such as certification bodies are making an impact in the job market today.
Data science is powerful and is a growing field with massive job opportunities. What are you waiting for?
21 thoughts on “What Does It Take to Grab a Data Scientist Job: Skills or Qualifications?”
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Requirements for work and level of professional skills depend on which company the specialist is in. For example, in large data analytics corporations, it is important to understand math and statistics. Marketplaces, Mobile data collection software companies and media companies need experts in the development of recommender systems, and in large retail – in the development of machine vision.
The journey from zero to first results in machine learning takes six months to a year. With programming experience, faster. The field of machine learning has already received a set of tools with which you can quickly create working models. The only problem is writing a wrapper around the model and writing that part of the code that is responsible for data preprocessing. Therefore, specialists need experience, and experience is gained only by work.
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Becoming a data scientist requires a combination of both skills and qualifications. A strong educational background in a quantitative field such as mathematics, statistics, or computer science is typically essential. Additionally, hands-on experience with programming languages such as Python or R, as well as database management and data visualization tools, is also important. To stand out as a candidate, having expertise in machine learning and working knowledge of big data platforms like Hadoop or Spark is valuable. However, strong technical skills alone aren’t enough. Soft skills like communication, collaboration, and problem-solving are also critical to success in a data science role. So, being an “essay writer uk” won’t guarantee you a data scientist job unless you have these crucial skills and qualifications.
Data scientists are logical specialists who concentrate significance from and deciphering information to tackle complex issues. The initial step to becoming a data scientist is commonly procuring a four-year degree program in data science or a connected field. You may likewise consider seeking a specialization or obtaining a graduate degree in data science before getting your most memorable section-level information researcher work. Dissertation Help
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