There are a ton of analytics courses offering for job seekers which were well curated information and methodical training on different tools and technologies used for data analysis. Students and enthusiasts are often hard pressed to choose the right course at the right time. So, it is important to keep track of what is relevant in the market. The job listings on websites like indeed, simplyhired, and monster can be a good indicator of what is trending. The mentions that follow are the most popular in listings on all three of these job portals.
Structured Query Language
As the name suggests this is a language used for making queries from relational databases. There are a number of tools in the SQL family like MySQL, SQLlite, etc. These tools are required for more than fifty percent of data analytics jobs as well as data engineering jobs. It is one of the basic skills required for these jobs. There is no way around them.
Good old Excel is almost as popular among employers as SQL. This simple spreadsheet tool from Microsoft is not suited for analyzing large datasets. Surprisingly enough, this is one of the most used tools for small scale data analysis. The filters, pivot tables, and other functions work very efficiently.
Tableau is essentially a data visualization tool. And data visualization is probably one of the most important parts of the job. Because it is the visualization that determines whether or not your analysis will lead the company to some action. Companies stress on recruiting personnel with Tableau skills because of its easy to use, interactive and attractive nature.
One of the most popular programming languages Python has gradually become the darling of data analysts and data scientists. The Python libraries are particularly suited for machine learning. Hence, the rigid necessity of Python is more abundant in the advanced analytics jobs. Python skills can lead you to great success in the field of data science while also opening up opportunities in web development.
R is a computational language of supreme stature. When it comes to deploying statistical analysis, data scientists seem to prefer R more than any other tool. Its adaptability, reliability, and flexibility makes it a very powerful tool. The steep learning curve of R means that the number of capable R operators is not too high. Coupled with the demand this lack of personnel creates great opportunities for people with R skills.
A large number of entry-level and intermediate data analytics jobs require these skills (not to say that advanced analysts do not use these). Hence, they are so much in demand. But as you go upwards the spectrum of skills broaden while the number of positions shrink. The key, of course, is never to stop learning.