Data Science has many career options, a rapidly growing industry. Data scientists constantly study and grow in their field, using numbers and data to make real-world decisions and communicate business needs.
We expect the data science career path to expand and become more popular in the coming years. As an example, the majority of us have a Smartphone. Eight out of ten adult owners own a desktop or laptop computer. Half of all adults own tablets. And one in five readers has an eReader. In addition, 78% of healthcare customers wear wearable devices to track their vital signs and lifestyle.
Data is generated daily by the increasing number of mobile users and internet penetration. Also, eCommerce apps are available at your fingertips.
What is the Role of a Data Scientist?
Data science professionals will collect, process, model, and analyze data to gain a deeper understanding. Businesses use data sciences to increase profitability, improve decisions and grow. Most companies use data science, so let’s examine the top data science facts to understand its importance and usefulness.
Data Science Facts
1. Data Scientists and Data Analysts Are NOT Similar People
This is a myth that people who have a superficial understanding of data science often believe. The work of data analysts and data scientists is completely different. Data analysts are responsible for analyzing data and finding trends, while data scientists are responsible for finding causes and forecasting future trends. Data science is a relatively new field. It is, therefore, inevitable that certain misconceptions will arise. It is important to note that both work together. They complement each other and work towards a common objective.
2. Data Science Is Not Excel Sheets
This belief may seem shocking, but contrary to the one above, many believe that Excel sheets are the centre of life for a data scientist. It is not true. As mentioned, data science is a vast area primarily focusing on the intended and correct outcome. To achieve that result, data science professionals will fight to the death. They use various data analytics techniques such as SQL queries, statistical analyses, predictive analysis, etc.
Excel sheets used to be a big part of their analysis and conclusion. With the availability of Python and R programming tools, data scientists spend more time coding than Excel sheets.
3. Data Science Is Not Just For Large Scale Organizations
Many businesses think large organizations with high-class infrastructure can only use data science. This belief is a result of a false notion about data sciences. Data science does not involve heavy equipment, machines or large working resources. Data science may comprise statistics, big data, programming, analysis, and presentation, as well as smart people who understand how to get the most out of data to add value to an organization. This has nothing to do with whether the organization is large or small.
A data scientist must arrive at a beneficial conclusion for the company. No one cares what tools or techniques were used to get that result. You only need an internet-connected computer and a few tools to help guide you through the data science cycle. Also, need to download various free, open-source tools online to get started.
4. Data Science Does Not Require a PhD or Tech-Savvy to Learn
It is a common misconception that data science requires a PhD or a super-brain. This is a complete lie. Anyone with an average level of intelligence can learn data science.
The following fields are required to be up-skilled to learn data science:
· Statistical modelling
· Predictive modelling
· Machine Learning
· Programming
· Algorithm
· You can also read about the advantages of using
Here is the theory of learning data science. It would be fascinating to hear some words from data scientists themselves. Data science is more complicated than it might seem. The only requirement is to have empathy for the possibilities. The rest will fall into place as you learn.
5. Data Science Sector Have Various Roles – Not Just Data Scientists
Data scientists are often the only role people think of when they hear data science. They ignore the many other roles in the field. All of these are included in data science.
· Data engineers manage data infrastructure during the entire data science lifecycle. Basic skills include programming tools like Python and database tools like NoSQL.
· Data analysts find answers to questions by analyzing data using the appropriate tools. Basic skills include programming, data visualization, statistics, math, and data analysis.
· Data scientists are experts in big data. They analyze the data and present their findings through reports and presentations. Basic skills include statistics, mathematics and programming.
You can also make a career in data science by taking on other roles.
Conclusion
This blog discusses the importance of data for the present and the future. As a result, the demand for data scientists, analysts, engineers, etc., will increase. The explosion of data in nearly every field is making data science inevitable. It is a great career option. Anyone who is good at problem-solving and has data empathy can benefit from a career in data science.
It is a field that has enormous potential, both for business and for job seekers. It is important to avoid any false information regarding the area.
