The term data science includes within it a number of disciplines, including artificial intelligence, data analysis, machine learning and statistical analysis.
Some people may be interested in finding employment within the field of data science but wonder about the industry’s future and its reliance on advancing technology.
There is also the question of the type of technology that students of data science need to be able to understand very quickly.
What is data science?
Data science is essentially a part of the wider computer science umbrella. It makes use of analytical tools and AI to turn raw data into valuable information.
Students of data science will find a number of employment opportunities open to them within a wide array of different industries, such as AI, business intelligence, statistics, data analytics and machine learning.
Data science skills are very much in demand in several lucrative industries as they are used by businesses to make improvements to marketing strategies and productivity and to increase revenue.
Data scientists work in industries such as information technology, biomechanical technology, manufacturing and education.
There are several data science skills that need to be mastered by professionals in the industry so that they can truly harness the potential of the discipline, including machine learning, Python, data analysis tools, data wrangling, database management, AI, SQL and statistical analysis.
Is data science too reliant on advancing technology and will it become obsolete?
Data science is certain to change and evolve as the years roll on, and indeed has already done so.
Whether data science will be completely replaced by one particular technology is impossible to say, but in the modern world, data scientists are among the business world’s most sought-after professionals thanks to the growing understanding of the value of data.
There are numerous technologies that are applicable to data science.
AI
AI has been seen as one of the biggest threats to the future of data science, with this technology being extensively used by the industry and enabling predictive modeling, statistical algorithm testing and data analytics.
This tech helps with the automation of tasks, potentially reducing the need for real data scientists one day, but it is unfair to say that data science will be replaced by AI.
AI and data science together have completely changed the decision-making process via predictive analytics models, but AI is not likely to actually replace real data scientists anytime soon because human intelligence is vital for the correction of machine errors and the modification of algorithms.
AI technologies are better seen as a complementary branch of the same industry with AI tech such as machine learning, deep learning and neural networking likely to only be of assistance to data scientists when performing their duties for many years to come.
The fact that data science is so ingrained in so many business operations is one of the reasons why it will not become obsolete regardless of where technology advances or does not advance.
What skills do data science students need to know?
There are many industry-standard skills that any would-be data scientists who have studied the likes of an online Master’s in Data Science will need to quickly familiarize themselves with.
Experience with such tools will be expected from almost any job in the industry, and at minimum being familiar with the concepts will make them easy to use prior to any actual experience with them.
Python
Learning Python should be of vital importance to data science students.
The majority of data science jobs will expect applicants to be fluent in Python as it is the current industry standard for scientific computing and has an almost unmatched ecosystem.
Fortunately, the widespread adoption of Python makes it quite easy for students to pick up.
Analytics
Tech is not often directly listed on the analytical side because almost any package will be useful for making concrete visualizations depending on your needs.
It is a good idea to develop a diverse set of libraries for the purpose of data visualization.
Statistics are also very important to the analytical process. Being familiar with SciPy for Python is likely to be very advantageous when looking for employment.
Employers want real quantitative results and statistical tests are the easiest method of delivering those.
Statistical skills will also help students to progress into developing machine learning skills.
Data
A complex understanding of data is necessary regardless of the programming language you are using, though, in the case of Python, packages such as Pandas and NumPy are vital when working with data.
It will be even more crucial to learn data management in your favored programming language if you intend to work with large models and complicated sets of observations.
It is also important to grasp how data is stored and processed, how to query that data, and then how to use your code.
Data aggregation algorithms are also of vital importance, whether from generated data, most commonly requested data, or logged data.
The ability to work with APIs and retrieve data in non-traditional ways is essential for all data scientists.
Machine learning
There are many situations in machine learning where it will be optimal to be able to make use of typical, more black box models than to build neural networks, though data scientists should have an understanding of both.
The majority of jobs will require Python developers to have some minimal experience with Sklearn.
Other standard industry tools that data science students should be familiar with include Julia and TensorFlow.
Data science provides companies with qualitative analysis, statistical predictive modeling, data synthesis and quantitative analysis, producing valuable insights that cannot be gained anywhere but from data scientists.
Data science solutions are relied upon by the finance, healthcare, computer science, ecommerce and education industries, and data scientists make sure that the discipline’s algorithms are free from errors and assist with having hard data translated into visual data that is more easily digestible.