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Developing a Data Science Career Framework

Data science is a multidisciplinary blend of data inference, algorithm development, and technology for solving the analytically complex problems. At the core is the data-trove of raw information, streaming in and stored in enterprise data warehouses. Many references and new information can be learned through this over-abundance of data.

Various tools and languages are available on the internet which is a perfect suit for this type of task. This article is written in attempt to help you get ready for a career in data science.

Educational Trends in Data Science

Data scientists are highly educated – 88% at the least have Master's degree-46% have PhDs. While there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to become a data scientist. The initial steps to becoming a data scientist include:

  • Earning 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.

Useful Technical Skills to develop a framework for a career in Data Science:

R Programming:

R is a very good data science for beginners. It offers a variety of libraries and formulas for processing information and performing statistical analysis. There is a myriad of resources available for an aspiring data scientist to learn.

Python Coding:

Python is the most popular coding language used for performing tasks in data science. Because of its versatility, Python can be utilized in many tasks such as data mining, web scraping or developing machine learning models. Python along with the thousands of libraries that it supports makes the work of a data scientist easier.

Hadoop Platform:

Although Hadoop is not always a stringent requirement for data science jobs, it is preferably the most loved when the volume of the data exceeds the memory of the system, or when the data needs to be sent to different servers. Hadoop can also be used for data exploration, data filtration, data sampling and summarization.

Machine Learning and AI

Machine Learning provides a system of ability to automatically learn and improve from experience without a manual human programming. It is imperative for a data scientist to be familiar with Machine Learning and AI technologies. This includes neural networks, reinforcement learning, adversarial learning, etc. Furthermore, most of the machine learning methods involves statistical approaches and this is one reason why a data scientist must have a strong grip on statistics (which includes probability theory). If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.