Data has fast become one of the most significant resources that an organisation can acquire and use. It is the basis of business decisions, innovation, improved customer experience, and increased profits. As the amount of data available continues to increase, the need for employees trained to access, evaluate, and make sense of all of this data also increases. The role of a data scientist, or an individual trained in machine learning (ML), addresses this growing need.
The data scientist/ML buzz has been growing over a number of years, and a very important question remains:
Is there a future for a data scientist/ML professional over the next 5-10 years?
Short Answer: Absolutely! Here’s why!
This blog post will look at the current data scientist/ML trends, job opportunities, industry needs, what will fuel growth in the future, required skills, challenges, and what types of jobs will be available in the future. At the end of this post, you should know if this is a field of study that may be a good choice for you at this time and over the next ten years.
- What Are Data Science and Machine Learning?
Analyzing Data With Data Science
Data science is an interdisciplinary field of study that applies statistics, mathematics, computer science, and business acumen to mine structured and unstructured data for useful information. Data science encompasses the entire process of collecting, managing, processing, analyzing, and interpreting data so that organizations can make more informed decisions.
Artificial Intelligence (AI) and Machine Learning (ML)
Artificial intelligence (AI) is a more advanced form of computer technology, and machine learning (ML) is a subfield of AI, where computer systems are taught through data and not pre-programmed for individual tasks. AI interactive features allow the user to interact with the computer using natural language, voice-activated assistants, etc., whereas ML uses algorithms to identify patterns from large amounts of data and uses those patterns/patterns to create rules.
Recommendation engines, fraud detection, self-driving cars, and predictive health diagnostics all rely on AI/ML technologies to produce positive outcomes.
- Explosive Growth of Data Across Industries
The sheer volume of data generated globally is overwhelming, and it will only increase in size over time.
- Over 2.5 Quintillion Bytes of Data Created Each Year
- An estimated 463 Exabytes will be created each day by 2025.
- More than 75% of businesses use big data and analytics to enhance their business processes.
As data continues to expand significantly, so does the need to find ways to extract value out of the massive amount of information being processed every second; this places a premium on being a data scientist, and machine-learning (ML) practitioners will play an important role going forward.
- Rising Demand for Data Professionals
Trends in the Job Market
The demand, pay potential, and advancement opportunities in data science and the machine learning profession have continually placed them at the top globally. Some job market trends include the following:
Rapid Growth in Job Creation in Data Science: Data science professions are expected to create jobs at a rate far above the national average for all occupations.
A Range of Different Job Titles: There is a very wide variety of titles that can describe the various positions that are available within the business intelligence/data analysis/data science machine learning industry, including, but not limited to, Data Analyst, Data Scientist, Machine Learning Engineer, Business Intelligence Developer, Data Engineer, AI Specialist, etc.
Cross-Industry Nature of Data-Related Jobs: Many fields, such as finance, health care, retail, e-commerce, manufacturing, logistics, automotive, entertainment, etc., are dependent on data in today’s economy.
More than on a regular basis, there are thousands of data-related job postings available on LinkedIn, Glassdoor, and Indeed, with many employers struggling to fill the vacancies due to a lack of qualified candidates.
- Competitive Salaries and Long-Term Financial Growth
Salaries are a strong indication of a healthy career trajectory. Careers in Data Science and Machine Learning (ML) have the potential for upward salary growth, so many Data Science and ML roles are among the highest-paying jobs available.
- Data Analysts can earn a competitive base salary with significant potential for salary growth.
- Data Scientists and ML Engineers have an average salary of over $100,000 (depending on the market), and that increases significantly with experience.
- Data Science Leadership positions, such as the Head of Analytics or Director of Data Science, offer salaries that rival the highest salaries in the business.
Overall, salaries for entry-level jobs in the Data Science and Machine Learning field tend to be significantly higher than average salaries across industries.
- The Role of AI and Automation in Future Job Growth
Though some might think that AI and automation will take the place of humans, in fact, AI and automation have caused an increase in demand for human data professionals.
While automation will perform many of the repetitive tasks, it is still necessary for a human being to interpret and understand all of the complexities associated with analysing large datasets.
Data professionals will need to be trained, evaluated, and improved by data professionals in order to continually support AI and machine learning systems.
Data science professionals help establish business problems that can be solved through the use of AI; this is not something that can be accomplished through automation alone.
Within this context, it is worth noting that both AI and machine learning have given rise to many new career fields and job opportunities that did not exist ten years ago.
- Expanding Use Cases Across Industries
The applications of Data Science and Machine Learning are being applied at every level and across an increasing variety of industries, from start-ups to Fortune 500 Companies. The following is a sampling of some of the more common applications:
Healthcare – Predictive Diagnostics, Personalized Treatment Plans, Patient Outcomes Forecasting
Finance – Fraud Detection, Algorithmic Trading, Risk Modelling
Retail & E-Commerce – Recommendation Engines, Dynamic Pricing, Custom Segmentation
Manufacturing – Predictive Maintenance, Supply Chain Optimisation, Quality Control Automation
Transportation – Route Optimisation, Autonomous Vehicles, Demand Forecasting
- Transferable and Future-Proof Skills
An important benefit to gain from pursuing data science and ML is the cross-disciplined nature of the skills acquired. Skills include the following: Programming (Python/R) and Data Visualization, Statistical Modelling, ML Algorithms, Data Storytelling, Cloud Analytics (Amazon Web Service/Azure/Google Cloud Platform)
Each of these skills can be applied in multiple industries, making them highly flexible and resilient in the job market.
- Importance of Data Ethics and Governance
The more powerful one becomes, the more responsibility one assumes. Data Science Demands (R&D) and ML are constant evolutionary processes; therefore, they create ever-growing opportunities to build out unethical dilemmas associated with:
- Privacy
- Algorithmic Bias
- Transparency and Fairness
- Responsible AI
As the knowledge of professionals increases toward an ethical framework and regulatory compliance, the value will also increase regarding GDPR, CCPA, etc.
- Opportunities for Entrepreneurship and Freelancing
There are many entrepreneurial ventures that entrepreneurs in the corporate world have developed, from the applications of data science and machine learning, to SaaS Analytics Products and Data Consulting Services, to niche Machine Learning Tools, and many others.
In addition, an independent data analyst may find work through a contract role or freelance position.
Both of these opportunities are increasing in demand on online platforms such as Upwork, Toptal, and Freelancer.
- Learning is Continuous, and That’s an Advantage
In fields where knowledge is rapidly becoming obsolete, both Data Science and Machine Learning are areas where practitioners will continue learning throughout their careers. Normally, the successful professional will view continuous learning as an opportunity rather than an obligation.
- New techniques and algorithms are introduced regularly.
- New tools and frameworks are introduced every year.
- Market dynamics require the Continuous Adaptation of Skill Sets.
Continuous Improvement is part of the culture in Data Science and Machine Learning, helping to ensure practitioners stay current and in demand.
- Future Trends to Watch
AutoML – Democratization of AI
The use of tools that allow people without expert knowledge of machine learning (ML) to build and implement ML applications will continue to grow; however, the data scientist/job will continue to provide value by assisting with the development of the overall strategy.
Explainable AI
For many industries that are heavily regulated, understanding how the various types of models make decisions will be very important.
Edge Computing
Edge-based real-time analytics (IoT, industrial) will provide new opportunities for many industries.
AI in Healthcare and Life Sciences
Forecasted investment in these areas will include significant funds for advanced analytical techniques and predictive modeling.
Quantum Computing
Though still in its infancy, the potential for Quantum to revolutionize data processing and optimization problems is imminent.
- How to Get Started in Data Science and ML
In this initial step, begin learning foundational information from statistics, basic math, and coding (Python, R).
Once you have this foundation, you can learn the core data science tools (Python/SAS/SQL) and ml libraries.
Once you have learned these tools, apply them to real-world data projects, gain practical experience, and build your portfolio.
Finally, choose an area of specialization (NLP, computer vision, or business analysis). Stay current on journal articles, blogs, papers, and news from the field.
Conclusion
As more and more organizations are making the transition from traditional ways of operating to a world where decisions are made based on the data collected, the need for Data Science and Machine Learning in the workplace will continue to grow.
As Data Science and Machine Learning are being implemented across many different industries, this is creating innovation in many areas, including healthcare, finance, automotive manufacturing, retail, and others.
As companies use these techniques and systems based on the predictive insight that can be gained from Data Science and Machine Learning techniques to automate their processes, companies will continue to need Data Professionals who have the skills to find, clean, analyze, and visualize their data to make data-driven decisions.
Data Science and ML can provide long-term stability, good pay, and global opportunities for people who are willing to put in the time to build up their analytical, technical, and problem-solving skills. Even though learning these skills may take some effort, the rewards far outweigh the effort.
In the next 5-10 years, those people who actively adapt to the new tools and technology in Data Science and Machine Learning can not only stay relevant but also continue to grow in their careers and have a solid career path into the future.