There is so much hype around the data scientist role these days that when a company needs a specialist to get some insights from data, the first idea is to look for a data scientist. But is it really the best option? Let's see how the roles of data scientists and data analysts differ and why you may want to hire an analyst before any other role. Data Scientist or Data Analyst?
Data analyst is a relatively new position available at several companies. It’s also a high-salary specialization without a complex learning curve. Thus, many professionals are looking to make a career switch to this burgeoning field. In this article, I’ll explain what skills you need to become a junior data analyst. We’ll also review some tips for making this career change and see what an entry-level data analyst salary looks like.
Looking for some advice to build a data science portfolio that will put you ahead of other aspiring data scientists? Don’t miss these useful tips. Why Have a Portfolio at All? Even though the demand for data scientists is high, the competition for entry-level positions in this field is tough. It should come as no surprise that companies prefer to hire people with at least some real-world experience in data science.
When you already have some experience with Python, building your own portfolio of data science projects is the best way to showcase your skills to potential employers. But where do you begin with developing your very first Python project? First, Why Develop a Data Science Project? There are a number of career development benefits to creating your own data science project in a language such as Python: Studying.
Looking for a data science job? Then you’ve probably noticed that most positions require applicants to have some level of Python programming skills. But how are they going to test this? What are they going to ask? Let’s prepare you for some interview questions! Why Do Data Scientists Need Python? Data science goes beyond simple data analysis and requires that you be able to work with more advanced tools. Thus, if you work with big data and need to perform complex computations or create aesthetically pleasing and interactive plots, Python is one of the most efficient solutions out there.
R is an extremely powerful and lucrative language for data science, and as of 2018, it’s one of the most popular programming language choices for data science professionals. R is an open-source programming language that’s widely used by data miners, statisticians, and data scientists to perform statistical computing as well as data analysis. Given R’s increasing popularity, R professionals are faced with plenty of career options and future possibilities.
Information technology is one of the hottest industries in the world and offers thousands of job in each major city around the globe. If you’re a student or professional who wants to get IT skills and find your first job in the industry, Vertabelo Academy can help you get started. The range of IT jobs available is stunning—from software engineers to system administrators and data scientists, IT rules the job market.
One of the oldest jokes in the business world goes like this: The CFO asked the CEO, “What happens if we invest in developing people and they leave us?” The CEO answered, “What happens if we don’t and they stay?” If you’re like the CEO and want to help your employees grow, this article will explain how you can do so with Vertabelo Academy. Vertabelo Academy has been around since 2015.