Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data.
After your degree programme, you are not done yet. The truth is, most data scientists have a Master’s degree or Ph.D and they also undertake online training to learn a special skill like how to use Hadoop or Big Data querying. Therefore, you can enroll for a master’s degree program in the field of Data science, Mathematics, Astrophysics or any other related field. The skills you have learned during your degree programme will enable you to easily transition to data science.
Apart from classroom learning, you can practice what you learned in the classroom by building an app, starting a blog or exploring data analysis to enable you to learn more.
2. R Programming
In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred. R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve.
It is difficult to learn especially if you already mastered a programming language. Nonetheless, there are great resources on the internet to get you started in R such as Simplilearn’s Data Science Training with R Programming Language. It is a great resource for aspiring data scientists.
Technical Skills: Computer Science
3. Python Coding
Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by O’Reilly use Python as their major programming language.
Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.