In the digital era, copious data from online actions, devices, and transactions inundates us. Hence, expertise in data science is vital. These experts decipher valuable insights, detect patterns, and enable data-driven choices.
So, is data science a good career? Long story short, the answer is yes. In this article, we take a deep dive into why this is true. We’ll also show you why pursuing an online data science course can be beneficial.
Data Science Job and Market Outlook
All the experts agree that the data science job market is growing quickly. Let’s see the figures:
The U.S. Bureau of Labor Statistics estimates data science jobs will grow 36 percent between 2021 and 2031. LinkedIn says data science has become the fastest-growing job need globally, increasing by more than 650 percent.
The Chinese big data and analytics market will likely escalate to USD 36.49 billion by 2026. According to IBM, data science ranks among the UAE’s top ten most sought-after professions. Likewise, the demand for data scientists is at an all-time high in India, with 11 million job openings in data science likely by 2026.
What is Data Science?
Data science is the study of data to gain important business insights. It is a multifaceted approach for analyzing massive volumes of data that integrates ideas and techniques from mathematics, statistics, artificial intelligence, and computer engineering.
Data scientists can apply this analysis to ask and respond to questions such as what occurred, why it occurred, what will occur, and what can be done with the outcomes.
What Does A Data Scientist Do?
Data science experts define team queries, develop data-driven solutions, and construct predictive models for speculation and forecasting. Here are some key tasks that data scientists typically perform daily.
Data Collection and Cleaning
Data scientists collect information from many sources. They confirm its accuracy by meticulously cleaning and refining it and then using it for analysis.
Exploring Patterns in Data
Data scientists discover underlying patterns within datasets using statistical summaries and visualization techniques, gaining critical insights into their structures.
Model Development and Refinement
Data scientists construct predictive models using learning algorithms, fine-tuning and evaluating them for the best results.
Deploying Models to Reality
Data scientists are essential in deploying models into real-world applications to ensure smooth integration and real-world functionality.
Interpreting and Communicating Insights
By converting complex analyses into clear insights, data scientists utilize visual aids and reports to communicate findings to various stakeholders.
Continuous Learning in the Evolving Field
Data scientists prioritize constant learning and keep up with the most recent techniques and tools, adapting to the dynamic field.
Domain Knowledge Enhancing Insights
Data scientists with industry expertise contextualize discoveries within certain fields, enriching the interpretation of data insights.
Enabling Informed Decision-Making
In essence, data scientists convert complex data into interpretable insights that help businesses make decisions, streamline operations, and uncover novel opportunities.
Reasons to Pursue A Career in Data Science
The vast and diverse field of data science offers a wide range of options for anyone wanting to pursue a technology career. Within this domain lies a promising vocational path packed with substantial rewards and abundant opportunities for advancement.
Data science is a versatile field
Nearly every type of industry benefits from having a data science specialist on their team, from finance and education to marketing and healthcare. This contributes to the rising demand for talent with data science expertise. According to research published by TeamLease Digital, there are 45,000 artificial intelligence job opportunities in India, with data scientists and machine learning engineers ranking among the most in-demand professions.
A data scientist who can investigate, report on and analyze data to make better decisions, enhance performance, and achieve goals faster. Therefore, if you are to ask – is data science a good career? Or wonder what can you do with a data science degree in a specific industry? The answer is you’ll discover a job opportunity that aligns with your interests and professional goals.
Scarcity of Data Science Professionals
The discipline of data science has a shortage of personnel. Although there are many opportunities in data science, very few people possess the qualifications to fill them. This implies that you will have a competitive advantage in finding work in data science if you have narrative, quantitative, and computational data skills and the required soft skills.
Let’s look at global stats highlighting the skills shortage in this field.
- A survey by US-based Alteryx revealed that 41 percent of UAE businesses find it easier to access analytics expertise. Yet, 32 percent of businesses have data scientists working exclusively on analytics projects.
- Due to a lack of skilled candidates in India, there are now close to 97,000 open analytics and data science opportunities.
- China has a great need for professionals with experience in data science since there are many more job openings than qualified candidates.
Data science careers are in high demand
If you decide to pursue a career in data science, plenty of opportunities are available. Although the significance of data science has only just come to light, businesses worldwide are rushing to capitalize on this brand-new area of knowledge.
Business analytics are becoming increasingly crucial to a company’s performance in a world that is becoming digital. Capacity to gather, organize, analyze, and draw conclusions from unstructured data. Businesses know that data science offers insights that may enhance operations, help them communicate more effectively with their consumers, improve information sharing, and boost yearly income.
Data science jobs are in strong demand, with 301,000 jobs being filled annually in Australia. According to a recent report by the Brazilian Data Science Association, Brazil’s data science industry is predicted to expand at 17 percent annually during the next five years, generating new jobs and fostering economic growth.
It’s a high-paying career
Data science is a well-paid career because of its demand for leveraging data for valuable insights. Businesses rely on skilled data scientists to make informed choices that boost efficiency and provide a competitive edge.
The specialized skill set, shortages of qualified professionals, and the significant impact on business outcomes contribute to the high earning potential in this field. According to Glassdoor, the average pay for data scientists is USD 113,436. With the growth of data, the demand for data scientists in companies has been rising rapidly.
Data Science Salaries
Within data science, compensation serves as a barometer for the integral role these experts play in gleaning insights from data.
1. Salaries by Experience
According to a survey by O’Reilly’s 2016 Data Science Salary Survey, experience is one of the most critical factors in a data scientist’s compensation.
2020 Burtch-Works data science pay analysis reveals the following
- Junior data scientist – USD 95,000 annually
- Mid-level data scientist – USD 130,000 to USD 195,000
- Seasoned data scientists – USD 165,000 and USD 250,000 per year, depending on their level of expertise
2. Salaries by Country
Data science salaries vary significantly by country due to demand, cost of living, and industry maturity. As of 2023, the average yearly salaries for different countries are as follows.
- The average salary in the U.S. is USD 124,692
- The average salary in India is INR 9.97 lakhs
- The average salary in the United Arab Emirates is AED 516,000
- The average salary in China is CNY 420,000
- The average salary in Australia is AUD 125,000
- The average salary in Brazil is BRL 318,480
3. Salaries by Industry
Data science salaries vary across industries. For instance, the average annual salary for a data scientist in India, in different sectors are as follows
- Healthcare: 13.9 lakh
- Technology: 8.6 lakh
- Financial Services: 12 lakh
- Manufacturing: 12.4 lakh
- BPO: 7.0 lakh
- Analytics and KPO: 9.6 lakh
- Education: 6.6 lakh
4. Salaries by Employers
Here are a few examples of some of the top-paying employers in different countries for data scientists.
- LinkedIn: Data scientists’ salary at LinkedIn in the US is USD 169,234
- ANZ: Data scientists’ salary of ANZ Australia is USD 065K (AUD 87K), with top earners reaching up to USD 286K (AUD 384K).
- Google: Data scientists’ salary at Google India ranges between USD 70.27K (INR 5232K)to USD 1351K (INR 101172K).
- Meta: Data scientists’ salary in Meta China is USD 0.78K (CNY 5.036K), with the highest salaries extending to USD 273K (CNY 1759K).
- IBM: Data scientists at IBM, Brazil, have an average salary of USD 0.6K (BRL 3.26K), with top data scientists earning up to USD 14K (BRL 75.86K)
The Future Outlook of Data Science Career
The future outlook for a data science career appears promising, and it continues to be a highly sought-after field. Following are some trends and factors that support the optimistic outlook:
Demand won’t slow down
As companies from diverse industries realize the benefits of data-driven decision-making, there is a predicted increase in demand for data scientists. The ability to extract insights from data to boost efficiency, identify trends, and develop predictive models remains essential.
As businesses continue to adopt data-driven approaches, they need skilled professionals to analyze and interpret data, leading to many job openings for data scientists. Also, experts in the field must updated with the latest tools, techniques, and methodologies. Data scientists can stay relevant through continuous learning, refining their skills to match the dynamic job market’s demands.
Data science is no longer just used by tech firms. Data science is increasingly used in healthcare, banking, retail, and manufacturing sectors to streamline operations, improve consumer experiences, and develop new products and services.
The future of a data science career will be shaped by the diversification of data science applications outside of tech companies, spanning healthcare, finance, retail, and manufacturing.
Growth of AI and ML
Data science is closely linked to artificial intelligence (AI) and machine learning (ML). The relationship between data science and AI and ML emphasizes the significance of data scientists in model building and optimization, promoting career progression alongside developing technology.
Their expertise guarantees efficient AI/ML model creation, enabling data scientists to keep pace with the rapidly changing world of technological advancement and cutting-edge applications.
The ongoing data boom
The amount of data produced worldwide is increasing dramatically. Data scientists have many career options due to the boom in Big Data, which necessitates the need for specialized experts to handle and analyze large databases.
A wide variety of career options, spanning industries and ground-breaking initiatives, are available due to the increase in demand for qualified data scientists who are excellent at managing and drawing insights from massive databases. Data scientists are essential in influencing future plans and innovations since data is the basis for decision-making.
Increase in automation
Although data science requires complex tasks, increasingly more tools and platforms are available to automate some steps in the procedure. The rise of automation and specialized tools in data science streamlines processes, boosting efficiency.
This allows data scientists to concentrate on strategic, innovative endeavors and creates a promising career environment. In an expanding data-driven environment, this trend reinforces the position of data scientists as managers of advanced analytics, assuring their relevance and impact.
Interdisciplinary nature of data science
Data scientists must often work with experts from other fields to understand domain-specific problems. Working together and communicating effectively across disciplines is more crucial than ever.
The necessity for interdisciplinary collaboration places data scientists at the nexus of problem-solving. Their capacity to link diverse expertise enhances their career opportunities by fostering creativity through all-encompassing insights and effective cross-disciplinary communication.
Growing emphasis on ethics and privacy
As the collection and analysis of data expand, ethical issues and data privacy are becoming more important. Data scientists are expected to be aware of and deal with these challenges.
Data scientists are becoming more important and have better long-term career prospects due to the growing emphasis on ethical data procedures and privacy awareness. Data scientists build trust by deftly negotiating ethical challenges, assuring their ongoing relevance in an evolving data-centric environment.
The need for continuous learning
Data science is dynamic, with new methods, procedures, and tools being developed often. Continuous learning is essential for data scientists to be current and useful.
It encourages adaptability, promotes expertise in innovative technologies, and elevates data scientists as change agents. The capacity to solve problems, grow in one’s career, and effectively manage challenging circumstances are all improved by continued learning. It also highlights the commitment of data scientists to uphold high standards in a field that is going through rapid transformation.
Also Read: The Top Data Science Interview Questions
How to Become A Data Scientist?
As a fresher, becoming a data scientist requires a systematic strategy integrating education, training, real-world experience, and job application. Here is a step-by-step manual to assist you as you proceed along the professional path:
Learn the fundamentals
Start with understanding the fundamental data science concepts, such as programming, probability, linear algebra, and statistics.
Learn about essential programming languages like Python and R and data analysis and manipulation tools, including pandas, NumPy, and scikit-learn.
Data science skills to master
As a data scientist, you must be proficient in:
- Data Preprocessing: Refine raw data through cleaning, transformation, and organization.
- Machine Learning: Grasp algorithms, applications, and model assessment.
- Data visualization: Use Matplotlib, Seaborn, and Tableau to communicate findings.
- Big Data Tech: Learn Hadoop, Spark, and distributed computing.
- Domain Knowledge: Apply data science adeptly in industry contexts for optimal impact.
Get trained for more structured learning
Enroll in a comprehensive data science bootcamp to learn more about the structure and gain practical experience. Explore platforms for options like a specialization from a respected institution, encompassing diverse data science topics and applied assignments.
Gain hands-on experience
Apply your knowledge by working on real-world projects and datasets. Take part in Kaggle tournaments, open-source, or individual projects to showcase your skills. Practical experience is crucial to showcase your capabilities to potential employers.
Build a solid portfolio
Create a portfolio that highlights your work, code, and insights. Include information on the projects, the data pretreatment procedures, the visualization, and the model implementation. A solid portfolio proves your abilities and shows how to tackle data science difficulties.
Apply to entry-level data science jobs
Look for entry-level jobs in data science, like junior data scientist, data analyst, or research assistant. Look for businesses that respect new talent and provide mentoring initiatives. Make sure to emphasize your abilities, projects, and pertinent education in your cover letter and CV.
Start your career as a junior data scientist
Once you land an entry-level job, focus on continuous learning and skill improvement. Participate actively in projects, seek feedback from more seasoned team members, and collaborate with them. As you gain experience, you can take on more complex tasks and responsibilities.
Data Science Career Trajectory
The typical career trajectory in the data science industry follows a hierarchical progression that reflects increasing responsibilities, experience, and expertise. Those who choose a profession in data science can anticipate the following stages of their journey:
Junior Data Scientist
Many prospective data scientists begin their careers as junior data scientists. Under the direction of more experienced team members, junior data scientists focus on fundamental tasks, including data cleansing, basic analysis, and model implementation. As learners get practical experience, they will likely work closely with experienced data scientists and receive mentoring.
Senior Data Scientist
Junior data scientists can rise to senior positions as they gain experience. Senior data scientists lead in making strategic decisions based on data insights, create and construct advanced models, and take on increasingly challenging tasks. Additionally, they could support innovation and research in their area of expertise.
Principal Data Scientist
The next step involves becoming a principal data scientist. Professionals with this experience level are valued for their significant contributions to the industry. They often manage high-impact initiatives, build sophisticated models, and mentor young and senior team members.
Data Science Manager
As data scientists advance, some could decide to go into managing positions. Data science managers oversee groups of data scientists, manage resources, ensure that projects align with corporate objectives, and assist with professional growth. At this point, having strong leadership and communication abilities is essential.
Senior Director/VP of Data Science
Achieving senior leadership positions is at the top of the data science career path. The organization’s overall data strategy is shaped by senior directors or vice presidents of data science. They work closely with other departments, make crucial business decisions based on data insights, and promote innovation and growth through data-driven projects.
Chief Data Officer
As data science becomes increasingly integrated into corporate objectives, data scientists may take on even higher-level jobs. The Chief Data Officer is one such role that leads data-driven initiatives at the executive level.
Read More: How to Become a Data Scientist?
Other Related Careers As A Data Scientist
Data analysts are specialists in data analytics and analysis. They collect, analyze, and analyze data to find patterns. They also identify the data’s relationships, dependencies, anomalies, and trends.
Data analysts are experts in transforming and manipulating enormous databases for advanced analytics. In the process of creating reports, predicting, and visualizing, analysts are also highly helpful. They support businesses in making data-driven business decisions like business intelligence professionals.
Data engineers have a very specific role in processing generated or sorted data. They are often experts in data modeling and warehousing. The role of a data engineer involves processing sourced data in batches and ensuring the integrity of data pipelines.
Additionally, they play a crucial role in developing an organization’s optimal data scientist and analyst ecosystem. As a data engineer, you will ensure that the whole data network operates efficiently and effectively, facilitating data transfer within this ecosystem.
Data architects ensure that databases function smoothly and perform better over time. A data architect develops new database systems and data solutions in addition to working with already existing systems.
Additionally, they provide database administrators and analysts access to the system. The primary goal of this role is to build an accessible data environment for the company that will support data scientists and other employees. This is unquestionably among India’s top data science positions in terms of pay and potential for professional advancement.
Business Intelligence Developer
Business intelligence (BI) analysts, developers, and users create and use BI technologies to assist businesses in making wise operational choices. As application architects, BI developers excel in manipulating existing systems and locating or connecting the data a BI tool will utilize.
BI analysts customize end-user dashboards and reports to concentrate on key details, while BI developers alter BI tools in accordance with the needs of the business.
So, if you ask — is data science a good career? Undeniably yes! Data science is the most demanded job of this decade and will be even for next. With increasing awareness of the field, competition in securing jobs between professionals is also at its peak. Choosing the right career path is just the beginning.
Start the next phase of your data science career on the right foot by getting trained and earning the relevant certificates. A top-notch data science program is the best way to advance your journey and set the stage for your career advancement.