As the demand for the hottest job becomes warmer in the New Year, the set of data science skills needed for them becomes more and more. Here we discuss skills that will be in great demand for data science that include visualization of data, Apache Spark, R, python and many more.
Here are, top Data Science Skills needed in 2016:
SQL is still one of the most important tools required, to be a successful data scientist, as the majority of data stored by companies is in these databases.
CrowdFlower recently did an analysis of 3,490 posts for scientific data work on LinkedIn and SQL is a skill that was specifically named on more than half of the lists they analyzed, which means SQL was the most proficient skill Often cited.
Companies making data-based decisions rely heavily on the ability of a data specialist to visualize and convey a story by analyzing the data because the researcher must communicate data-based information to technical and non-technical people Of the company.
Learning Hadoop coupled with Big Data Analytics will make you stand out from the crowd. One of the most urgent barriers to adoption for Big Data in the business is the lack of skills around Hadoop.
Spark is at the forefront of technologies that have evolved to meet the ever-increasing need to model and analyze large amounts of data.
Spark is a skill that is becoming more and more attention in the large data space, due to its speed and ease of use.
Python is another sector that is experiencing an increase in demand. This programming language helps engineers create concepts with less code than Java or C ++, so it is considered more efficient, less prone to bugs and with the potential to create clearer programs.
Not yet convinced about Python? Not only is Python easy to learn, but it also has a large active community that continues to grow. If you are stuck with some coding problems, there is a wide range of experts to help you in the community. According to one study, Python remains the No. 1 tool for data science.
This is the heart of what you can offer as a data scientist. Statistics will always be an essential component of a data scientist, so it is very important that they can decide on the most appropriate statistical techniques to approach different classes of problems and apply the relevant techniques.
It is often said that 80% of the work in data science is the manipulation of the data. Despite this, with R, data manipulation is easy because R has some of the best data management tools you will find.
R is an essential tool for financial and analytical management companies such as Google, Facebook and LinkedIn, so it is useful that you look at R more closely if you have not yet done so.
Anyone can be formula. Companies in 2016 want an innovation that sets them apart from their competitors, in terms of sales and the image they present to their consumers.
Creativity is the ability to apply the technical skills mentioned above, and use it to produce something of value, in a way other than a pre-concluded formula.