What is Data Science?
Dr. Martin Schedlbauer, a data science professor at Northeastern University, says that data science is used by “computing professionals who have the skills for collecting, shaping, storing, managing, and analyzing data [as an] important resource for organizations to allow for data-driven decision making.” Almost every interaction with technology includes data — your Amazon purchases, Facebook feed, Netflix recommendations, and even the facial recognition required to sign in to your phone.
Commonly referred to as the “oil of the 21st century,” our digital data carries the most important in the field. It has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient.
Data science has been one of the trendiest topics in the last couple of years. But what does it take to become a data scientist in 2020?
So, Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing, and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
In-Demand Data Science Careers as follows :
What are the different roles available in a Data Science Career?
- Business Intelligence (BI) Developer.
- Data Architect.
- Applications Architect.
- Infrastructure Architect.
- Data Scientist.
- Data Analyst.
- Big Data Specialist.
- Statistician.
and God knows…
Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. Data science careers are in high demand and this trend will not be slowing down any time soon, if ever.
How to build a strong base for your Data Science career?
This is the most interesting question of the day. Isn’t it?
Currently, the Data Science career is the most rewarding career and many professionals are looking for a most promising way to dive into it. To start analyzing the big measure of data floating around, your aggressive edge in understanding few essentials is required.
Look for Specific direction: Data Scientist is the common term and there are many sub positions available under this roof. Find out which subfield interests you. Whether you are an avid enthusiast in building a Machine Learning algorithm or interested in analyzing the neural networks of Deep Learning, find the specific niche, and start equipping yourself accordingly.
Learn the Data Science tools: Data Science is not restricted to working on statistical methods and deriving a formula. Data Scientist is a decision-maker of a company and it is crucial to master the Data Science tools such as SAS, Python, SPSS, R, and SQL along with your Statistics and Applied Mathematics knowledge. In addition, the working experience on Hadoop and Spark, programming languages, understanding of machine learning and deep learning would beneficial.
Work upon your Communication skills: The heavy program codes implied on puzzling big chunks of numbers can inspire you but not the Business owners. If you are unable to convey the analyzed information in an easy to understand format to the business owners then you might lose the victory in your data battle. So, ace the ability to present the results in an understandable and easy way for your business owners.
Showcase your Data Analytics skills: Never hesitate to explore the data when you get the opportunity to do so. Many Data Science professionals find it difficult to identify where to start once they are done with all the studying. Start identifying the sources of the hidden data that are waiting to be used and apply your skills.