In this digital era, it would be silly to ignore data and the benefits it brings to an organization. Many businesses have shifted to data-driven approach in different ways. And those who haven’t shifted yet are likely to do so in the future.
For three consecutive years, data scientist is topping the number one job chart in the U.S. by Glassdoor. According to the U.S. Bureau of Labor Statistics reports, by 2026, the evolution of data science will create 11.5 million job opportunities in the country. The role of data scientist has become such a hot job option for tech geeks.
If the introduction has got you hooked in becoming a data scientist, but you want to weigh the pros and cons first and want to see how data scientists will be shaping the future then keep on reading this post.
Soaring Demand for Data Scientists
The job opportunities for data scientists have increased as compared to last year and many IT professionals are ready to jump into data science training programs and they are willing to invest their time and money.
Rapid Career Growth
Many latest surveys have predicted a 50% gap between the projected demand and supply of data scientists all around the globe. If you decide to make a career in data science then you have to deal with the collections of big amounts of data, your skills set needs to become stronger day by day and it will become more and more essential for your organization as they tend to handle more data in the near future.
Commoditization will only increase in the future
There’s no denying that data science is getting commoditized increasingly — the impact of technology has made it possible. Different programming frameworks come with built-in libraries of models that are already structured. This results in time saving, a data scientist will invest lesser time than usual and solve complex problems in a much shorter period of time than an entire team and this increases productivity. Organizations around the world have understood the importance of data science. They know that this is the ideal time for investing in data science whether their business niche is technical or not.
Endless Possibilities
If we go back to the time when the file system was functional and everything was stored in files and year later, they were discarded because they didn’t serve any purpose to the business. This was because we can not manually exploit a huge amount of data like we can today with the help of different analytics tools. Every industry gathers data, but many are only employing data scientists to interpret the hidden insights. From medicine to agriculture and food industry to finance, data scientists are employed in every sector to run the data-driven businesses.
Skills and Training Required As a Data Scientist
The common skills required for data scientist are mentioned below:
- Multi-variable calculus and linear algebra
- Data visualization
- Statistics and data mining
- Software engineering
- Machine Learning programming languages such as Python, Java
- Reinforcement learning
- Data wrangling
- Apache Spark
- Expertise in databases( for example, SQL Platforms)
- Data intuition
Other skills which also have a significant role to play:
- Strong communication skills
- Intellectual curiosity
- Problem-solving skills
- Business acumen
What Are The Responsibilities Of Data Scientists?
As this field is a bit new, so the responsibilities of a data scientist aren’t decided yet. However, as a data scientist, here are three key tasks they are expected to complete as part of their job role:
Staying Up to Date
Data scientists must be up to date in their field because the technology is evolving so quickly that if they don’t cope with it , they will fail. They have to stay on top with analytical techniques like machine learning , expert system, data mining and it’s subfield of text mining.
Gathering Data
It’s the duty of a data scientist to gather as much possible data from websites and social media platforms and extract it into useful information that is of some help to the business. Extracting irrelevant data will only result in waste of time and efforts.
Solving Problems With Statistics
This may seem hard and complex and there’s no denying it. But many data scientists study statistics in their academics career and this makes it relatively less complex for them. Statistical analysis is used to predict current and future market trends for a business.
Data Science Job Roles
There are numerous job roles for people who choose data science as a career. Data science is not only limited to the job role of a data scientist.
Some of the prominent Data Scientist job roles are:
- Data Analyst
Average salary: $68,752
Job Description: This is an “entry-level” position in the field of data science and the salary can vary depending upon the skill set. They have to deal with the company or industry data and analyze it according to their business needs.
- Data Architect
Average salary: $137,630
Job Description: IT professionals with a strong background in traditional programming and Business Intelligence. They will be dealing with data ambiguity.
- Data Visualizer
Average salary: $120,959
Job Description: As a data visualizer, you will have to translate data analytics into important insights for businesses. Should be able to translate the data in natural language and communicate with other team members as well.
- Machine Learning Engineer
Average salary: $144,085
Job Description: This role will involve taking a data scientist’s analysis expertise and transforming it into a software.
- Statistician
Average Salary: $82,477
Job Description: Using statistical methods to gather, interpret and analyze data and help the business solve real-world problems.
Data science professional are soaring high in demand in almost every sector, from government security to ride-booking apps. Many businesses and government departments are depending on data science to improve their business decisions and help their customer base. If you’re looking to make a career in it, go for different data science training programs, it will help you launch your first steps towards your goal.