Considering a career in data science? Start with R

0
106
Data Science

A career in data science has become quite coveted in the last few years. The discipline has moved out of academia to rule the commercial world and the opportunities are varied and numerous. There are tons of reasons behind data science becoming so important for the business world. Artificial intelligence and machine learning have penetrated mainstream business and data is the central figure in these futuristic technologies.

Data science is responsible for streamlining the process of data collection, validation, analysis, and pipelining them into various systems. Data science professionals are the stalwarts of data driven decision making and therefore they are in massive demand.

Learning R is a good starting point

If you are considering a career in data science, it is the right time to make the first move. It is a multidisciplinary field which can be approached with different tools. R is one of the most popular languages for data science. So, you can start your preparations with a job-oriented data science R course.

The practice of data science can be divided into a few parts.

  • Importing the data: This means gathering data from different sources and pipelining them into the system. This data can be structured or unstructured.
  • Tidying or cleansing the data: This can mean two things which are related to each other. Firstly cleansing may refer to segregating the wheat from the chaff that is sorting out good data. It can also mean arranging the data in a structured manner according to the needs of the analyst.
  • Transformation: This part is a bit more complicated. Data is collected in different forms and formats and needs to be transformed into a single format. There is another aspect of data transformation. The data might not be directly answering the questions asked so it has to be changed so as to achieve a certain target.
  • Data modelling: This means creating advanced computational systems that can use the data to locate patterns and to make assumptions.
  • Data visualization: The whole study goes in vain unless it is represented in a comprehensible manner. Visualization refers to the use of tables, pictures, charts, graphs for presenting the data in a manner that the insights are easily found.

R is a language equipped to handle all these tasks. Data scientists have shown a lot of trust towards R and consequently it holds a large share of the market along with SAS and Python. Data science R course definitely starts you up in the right direction.

You have to keep growing your repertoire

As a data science professional you will be expected to be in the know of various tools and technologies. Once you get started with R you can then spread yourself across different tools. Most teams of data scientists work with a combination of R and Python. Knowing both well can help you build strong base in the industry.

LEAVE A REPLY

Please enter your comment!
Please enter your name here