Exploring The Data Science Learning Path

Who is a Data Scientist and What Do They Do?

The term “data scientist” is an industry-recognized designation for a professional with deep analytics experience, industry knowledge, and skills.

A data scientist is responsible for extracting insight from structured and unstructured data that have potential business impact. Data scientists are typically high-ranking team leads or have even higher positions in an analytics organization. With every industry and function now embracing analytics, having data scientists in an organization has become a necessity. Analytics now governs everything from HR and marketing to sales and supply chain.

Data Scientist Salary Prospects

According to Indeed, data scientists earn over $123,000 annually.

Qualifications of a Data Scientist

There are three education options that you can pursue if you are considering a career as a data scientist:

• Graduate degrees and certifications provide networking, internships, and recognized academic qualifications for a resume.
• MOOCs and self-guided learning courses, both cheap and free, targeted and short, allow you to complete projects on your own time.
• Boot camps are faster and more intense than traditional degrees.

Data Scientist Skills That Will Give You an Advantage

Possessing these technical skills will provide you with an edge over your peers:

• Statistics (e.g., hypothesis testing and summary statistics)
• Math (e.g., linear algebra, calculus, and probability)
• Machine learning tools and techniques (e.g., k-nearest neighbors, random forests, ensemble methods, etc.)
• Data mining
• Software engineering skills (e.g., distributed computing, algorithms and data structures)
• Data visualization (e.g., ggplot and d3.js) and reporting techniques
• Data cleaning and munging
• R or SAS languages
• Unstructured data techniques
• Python (most common), C/C++ Java, Perl
• SQL databases and database querying languages
• Big data platforms like Hadoop, Hive & Pig

The Business Skills That Will Give You an Advantage

• Analytic Problem-solving

  Candidates will need to approach challenges at a higher level, employing the right approach to make the maximum use of time and human resources.

• Effective Communication

  Candidates will have to detail techniques and discoveries to be technical and non-technical audiences in a language they can understand.

• Intellectual Curiosity

  Candidates will need to explore new territories and find creative and unusual ways to solve problems.

• Industry Knowledge

  Candidates will have to understand the way their chosen industry functions and how data is collected, analyzed, and utilized.

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.’

Chart Your Data Science Career With Simplilearn

Simplilearn offers courses in R, SAS, Hadoop, Python, and various other Big Data courses to climb the ladder to the top. We have introduced a Masters's program in Data Science that will give you the knowledge and the tools to accelerate your career. This program has been designed to meet the requirements of the new wave of demand for strong analytics professionals. It equips you with all the conceptual and technical skills required to succeed as a data scientist. The program provides access to high-quality eLearning content, simulation exams, a community moderated by experts, and other resources that ensure you follow the optimal path to your dream role of a data scientist.