Course Detail

Data Scientist Learning Path

A person looking to be a well-rounded senior data scientist can follow the recommended learning path shown below. 

SAS

SAS is a computer programming language that is used for statistical analysis. It stands as the undisputed market leader in the commercial analytics space.

SAS updates are developed in a controlled environment and are thus always well tested compared to open source. The language is easy to learn and provides a simple option for professionals who already have an established knowledge of SQL.

Many businesses distrust freeware and don’t like the idea of not having a software provider verify the efficacy of their application usage. Then there is the matter of market opinion – SAS is leading the advanced analytics segment with a 31.6 percent market share, according to IDC.

R

With the R certification and training, professionals will be competent in R programming language concepts such as data visualizations, exploration, and statistical concepts like linear and logistic regression, cluster analysis, and forecasting.

R is open-source, has a vibrant community, has libraries for extensive analytics and visualization, has a steep learning curve, and integrates with big data and Hadoop. And compared to other languages, R still stands as the one that produces a higher salary of $115,531. It is one of the most in-demand skills.

Data scientists and statisticians around the world use this programming language to solve some of their most challenging problems in fields that range from computational biology to quantitative marketing.

Since complex data is represented through charts and graphs, the language has become an essential part of the data analysis process.

Hadoop

An open-source framework, Hadoop is used for distributed processing and distributed storage of large data sets.

Hadoop is written in Java; all of the modules are devised with the central assumption that hardware failures are ordinary and common and should be handled automatically by the software.

Hadoop has opened new doors for data scientists to store and process data. Instead of depending on proprietary hardware and other systems to process and store data, Hadoop allows parallel distributed processing of massive amounts of data across industry-standard servers that will process and store data. With Hadoop, no data is too big.

For more information on these programming languages, or any other programming languages that are important to a data scientist, feel free to download the eBook, ‘Top Programming Languages for a Data Scientist.’