Data is everywhere around us and it will only continue to amass as we move toward a technologically driven world. While data is extremely valuable, we are inundated with it, which poses a problem to extrapolate valuable information from it. Finding data that is useful can be like looking for a needle in a haystack- frustrating and time-consuming. To make sense of the mountain of data available to businesses, it is important that the data is made comprehensible and actionable.
How do you achieve this feat? Simple. Through data science. Data science is an interdisciplinary field that employs mathematics, statistics, computing algorithms, advanced analytics, artificial intelligence (AI), and machine learning to extract useful information and insights (such as larger patterns and trends) from a large body of data or datasets.
Harnessing the power of data science has become important for businesses to improve and grow. As a result, the demand for data science professionals has seen a meteoric rise in the recent past.
According to LinkedIn, careers in data science are witnessing a growth of 35% annual growth rate in the US. The demand is only set to increase in the future, and a career in data science is becoming a highly lucrative one.
Before we delve deeper into the various aspects of data science as a career, let us first look at who is a data scientist. A data scientist is a computer professional possessing skills for collecting, analyzing, processing a large set of structured and unstructured data. In this time of computers, most organizations are collecting a huge amount of data in their daily operations.
In almost every interaction with technology, data is interchanged. A data scientist’s role is to analyze this data and interpret the results to implement them for the organizational benefits. If you want to gain expertise and get your dream data science job, check out our data science certifications.
In the modern world, about 2.5 quintillion bytes of data are processed every day. A data scientist can organize and analyze this huge amount of data to make it accessible to lead a profitable business. For example, an organization can use data science to remind its customer of habitual purchases. Suppose you order a shampoo every month, you may find a strategically placed deal around the same time of every month, prompting you to buy more.
Data Scientists not only play a key role in business analysis, but they are also responsible for building data products and software platforms. Truly speaking, data science is a combination of computer science, statistics, and mathematics.
Considering all this, it is a good idea to think of a career in this dynamically expanding industry. Let’s begin by looking at some of the most popular data science career opportunities.
Different Job Roles for Data Science Experts
Most companies are adopting data analysis for their growth. Data Scientists are in a growing demand not just in technology, but also in all other major sectors, including FMCG, logistics, and more. It is commendable to inscribe here that the five biggest companies viz. Google, Amazon, Apple, Microsoft, and Facebook have employed one-half of the total data scientists the world has.
Data science career opportunities are nonetheless, incredibly diverse. Opting to undertake a data science career will open up several career opportunities and job titles for you.
Some of the lucrative Data Science carriers for the aspirants are:
A Data Scientist explores various data patterns to measure the impact on an organization. A key role of a Data Scientist is the ability to explain the importance of data in a simpler method to be understood by others. They are supposed to have a statistical knowledge of different programming languages required for solving complex problems.
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Analyzing data to figure out a market trend is the role of a Data Analyst. He helps in providing a clear picture of the company’s standing in the market. Once the desired goal is set by a company, a Data Analyst provides datasets to achieve the required goal.
The role of a Data Analyst may change as per the requirement of a company. For instance, the marketing department may require their services for some time to understand consumer behavior and reactions to different marketing strategies.
Data Engineer works with the core of the organization and can be considered as the backbone of a company. They are the builder, designer, and manager of a large database. They are in charge of building data pipelines, enabling correct data flow, ensuring the data to reach the relevant departments.
A Data Engineer has to work in collaboration with other data experts to communicate results with his colleagues. In a nutshell, a data engineer has to share his insights with the company through data visualization, helping the organization grow.
Read: Data Engineer Salary in India
Business Intelligence Analyst
A business intelligence analyst helps in analyzing the collected data to maximize the company’s efficiency, hence generating more profits. Their role is more technical in nature then analytical, requiring more knowledge of popular machines. They have to serve as a bridge between business and IT, helping them improve.
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A Business Intelligence Analyst is required to possess the knowledge of a specific industry and industry trends.
Read more: Business Analysis Vs Business Intelligence: Differences Between BA & BI
The role of a marketing analyst is to assist companies in their marketing division. They analyze and suggest which product to produce in large quantities and which product to discontinue. Monitoring customer satisfaction reports help in improving existing products and services. They decide which products to sell with the targeted customers and at which price.
Read about: Data Scientist Salary in India
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One of the most essential careers in data science is to become a data architect. A data architect designs, develops, and maintains a business’ data management systems. They are responsible for meeting the company’s database requirements and building them in keeping with internal and external regulations.
Machine Learning Engineer
One popular role or career in data science, machine learning engineer, is primarily responsible for automating data analysis processes. They design and implement machine learning systems, research and optimise machine learning algorithms, and perform machine learning tests to monitor system performance and functioning. It has emerged as one of the most essential data science careers in recent times.
A database administrator is involved in maintaining and functioning of the organisation’s database system. They are responsible for the safe and secure management and storage of data and develop backups and recoveries solutions. To put it simply, they are responsible for the day-to-day functioning and management of a company’s database system.
Skills required to build a Career in Data Science
A data scientist should have good acumen for analyzing data with excellent programming skills. They use a variety of skills, depending on the specific organization demand.
Desirable skills for this career can be divided into two parts:
You must be excellent in Mathematics, Computer Programming, and Statistics to be a successful data scientist.
Other technical skills needed are:
- Programming – Proficiency in computer programming and coding languages
- Analytical tools knowledge – SAS, HADOOP, Spark, and R, are the popular analytical tools, used by the data scientists
- Unstructured Data Workability – Ability to manage unstructured data received from different channels
Most of the personal skills possessed by an individual are categorized under the non-technical section. They are:
- Strong business sense
- Communication skills
- Data intuition
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How do I become a Data Scientist?
You should have a bachelor’s degree in data science or a related field to start your career as a Data Scientist. A certified course in data science is preferable to enter the market as a data scientist. A background in Mathematics and Statistics also increases the chances of being successful in this field.
Even students of entirely different fields of education can pursue their career as a data scientist. If you have never written code, you can still become a data analyst with the virtue of your hard labor and learning commitment.
upGrad offers comprehensive courses in Data Science. The material is easy-to-understand and offered by experienced professionals in the field. We also offer special assistance by guidance counselors to help you decide the kind of career you should be seeking.
After completing our 12-18 months long course, you will become industry ready and are sure to get a well-paying job!
Learn more: How to Become a Data Scientist?
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What Kind of Salary can you Expect in a Data Science Career?
Data Science is high in demand and, therefore, pays well.
The average base salary for a data analyst in India is around 4.96 lakhs per year that includes around 50K in bonuses and 4K on a profit-sharing basis. This figure can go up to 9.98 lakhs per year, depending on your experience, additional skills, location, and employer.
If you are just starting out as a data analyst or have less than one year of experience in the field, you can expect to earn somewhere around 3.50 lakhs per year. If you have around 1-4 years of experience, you can expect to make 4.83 lakhs per year. For analysts with 5-9 years of experience, the compensation is somewhere around 7.20 lakhs per year. Experienced candidates with 10-19 years of experience are paid around 12.75 lakhs per year.
So how much impact does your experience really have on your salary? If you are an experienced data analyst, you will earn a salary that is around 157% higher than inexperienced candidates. Mid-level experience holders can also earn 45% more than others with lesser experience.
|3.50 lakhs per year||4.83 lakhs per year||7.20 lakhs per year||12.75 lakhs per year|
Any additional skills or those that you are not taught during your certification can contribute to a higher salary. If you are skilled in data analysis, you can earn something around 4.32 lakhs per year. If you can work with SQL, you can get a salary of around 4.92 lakhs per year.
Proficiency in statistical analysis is also an added bonus. You can earn around 4.80 lakhs per year with this skill. With knowledge of Microsoft Excel, your salary will be around 3.89 lakhs per year. The most popular skill out of all these is data analysis, followed by Microsoft Excel and SQL. Statistical analysis is the least popular. However, all these skills pay you less than the market rate.
In summary, there are several factors impacting the salary package when opting for data science as a career. Listed below are some of the important contributing factors:
- Work experience: Your years of work experience in the industry can play a significant role in determining the salary you command in a data science career. The higher the number of years you have worked in the industry, the higher your prospect of negotiating a higher pay package.
- Role/ Job title: You can choose to select one of many careers in the field of data science as discussed in the preceding section. The job title or role will factor in to determine your salary package as some roles can be higher paying than others.
- Employer: Who you work for can significantly impact your salary. Working with big and/or reputable employers in the field will result in earning competitive salaries.
- Location: The place of employment determines the cost of living and demand for data science specialists in the area. This can directly affect your remuneration package.
- Skills: Your skillsets and level of expertise in them can significantly affect your pay package. Upskilling and learning skills that are valued and in demand in the field can help you grow in your career and negotiate a higher salary.
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Data is the currency of our age. This makes data science careers one of the fastest growing and most popular career prospects. Data Science is amongst the number one jobs in the U.S. As per the labor statistics report of the U.S, demand for skilled data scientists will rise at the rate of 27.9 percent by the year 2026. If you have analytical skills and are passionate about computers, then an advanced degree in data science is a perfect goal for your carrier. You can join any of upGrad’s Premium Data Science Courses to accelerate the chances of getting an excellent job as a Data Scientist.
If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
All the best for your career!
What are some steps to follow for becoming a data scientist?
Data Scientist is a highly in-demand job role in the market because of the increased use of data in every sector. With the increased demand, there are plenty of students considering building their careers in the field of data science.
Here is a step-by-step process to make things easier for you:
1. Take online courses and get good at machine learning, math, and statistics
2. Learn to code different languages like Python and R
3. Achieve a bachelor's degree in data science or other related fields
4. Understand database handling for effectively retrieving information from it
5. Explore the concepts of big data and its approaches
6. Take part in competitions and network with data scientists to know more about the field
7. Take up an internship for getting industrial exposure and experience in the field
By following these steps, you will be able to become a successful data scientist and also develop a bright future for yourself. Run after acquiring the skills before you start running after a job in the field of data science.
Which is the best language for data science?
There are two types of programming languages being used by the programmers – low-level and high-level. Beginners in the field make use of low-level languages like machine language and assembly language. A majority of data scientists use high-level programming languages for better efficiency. Some of the widely used programming languages for data science are:
Programmers use any particular programming language based on their requirements as well as their level of understanding.
Do data scientists utilize the C++ programming language?
Programmers are seen talking about the merits and demerits of C and C++ programming languages in concern with data science. Where Python and R are considered to be the most popular languages for data science, there are certain data science professionals seeing C and C++ as efficient and effective choices for data science.
By using the traditional C and C++ languages, developers have found them to be really useful for data science development due to their numerous opportunities. Several developers have found C++ to be the best programming language that they prefer using for data science.