Data Science in 2024: An Overview of Changes and Challenges in the Booming Field

Abstract

The article provides an overview of the current state of the data science industry in 2024, highlighting the significant changes and challenges that have emerged since the boom years of 2020. It discusses the shift from a generalized data scientist role towards more specialized positions, the impact of artificial intelligence and automation, and the skills and tools required for data scientists to thrive in the changing landscape. The article also emphasizes the importance of programming languages, particularly Python and SQL, and the increasing demand for data and machine learning engineers.

Introduction

The data science industry has radically transformed recently, with the COVID-19 pandemic catalysing significant changes. In 2020, the demand for data science skills increased by 50% across various sectors as businesses sought to leverage data-driven insights to navigate the challenges posed by the pandemic. However, as the pandemic subsided in 2022 and 2023, the data science sector experienced a dramatic shift, with a recruiting boom turning into a layoff spree.

What is Data Science?

Data science is an interdisciplinary area that extracts knowledge and insights from structured and unstructured data using scientific procedures, systems, algorithms, and methodologies. It merges fields like computer science, information science, and statistics to analyse and comprehend complicated data. The main objectives of data science are finding patterns, gaining insights, and using data analysis to make well-informed decisions. Not only is data science becoming more and more critical, but it is also wholly changing industries by transforming data into a valuable tool for insights and decision-making.

Data science is becoming a key component of corporate strategy planning, helping firms acquire a competitive edge, comprehend market trends, and make better decisions. Its applications span several industries, including healthcare, banking, retail, entertainment, and transportation. Data science is applied in healthcare to optimize patient care and anticipate diseases. It is utilized in finance for automated trading, fraud detection, and risk evaluation. Supply chain optimization, inventory management, and personalized suggestions improve the customer experience in retail. It is essential for increasing safety and efficiency in transportation, from developing autonomous vehicles to helping logistics organizations optimize their routes. Streaming services like Netflix use Data science in the entertainment industry for their content recommendation algorithms.

Strategic business decisions are driven by data science, which raises profitability, lowers expenses, and increases efficiency. Data science is practical in urban planning, environmental conservation, and public health programs. Empowering companies to recognize untapped markets and create innovative goods and services promotes innovation.

A combination of abilities, such as statistical analysis and mathematics, programming in Python, R, and SQL, machine learning, data visualization, problem-solving, and communication, are usually required to become a competent data scientist. Examples of popular data science tools and technologies are machine learning frameworks like TensorFlow and sci-kit-learn, data visualization programs like Tableau and PowerBI, and programming languages like Python and R.

The increasing amount of data produced by business, industry, technology, engineering, and science gave rise to data science. Due to this expanding need, data scientists are now found in practically every industry, including retail, banking, and healthcare. Data science is one of the most promising and in-demand career pathways for competent individuals and is still growing. Proficient data scientists can recognize pertinent inquiries, gather information from diverse data sources, arrange the data, convert outcomes into remedies, and convey their discoveries in a manner that influences business choices favourably. Since practically every industry needs these abilities, qualified data scientists are becoming increasingly valuable for businesses.

What Has Changed?

What aspects of the data science ecosystem have changed, and what obstacles will the field face in 2024 regarding hiring data scientists?

What will the world of data science look like in 2024? We must go back several years to answer this question and tell your fortune. We'll look at how we transitioned from the boom years of 2020 to the more specialized and complex fields that will emerge in 2024.

Back to 2020

In 2020, the world was gripped by the COVID-19 pandemic, posing enormous problems for industries. However, this boosted the tech industry significantly, as many things moved online rather than in person. More specifically, demand for data science increased by 50% across businesses and marketplaces. Healthcare, technology, media, and financial services were especially eager for data science skills and embarked on a recruiting binge.

2022 and 2023 Job Losses

The high demand for data scientists did not continue very long. As the pandemic subsided in 2022 and 2023, the data science sector saw a dramatic shift: a recruiting boom turned into a layoff spree.

Big tech corporations reduced their job postings by 90%. It was a competitive market for both entry-level and seasoned data scientists. Over two years, we saw over 500,000 job losses in the computer industry, with engineering and data science roles accounting for more than 30%.

Specialization & AI Era

These two years have seen more than just layoffs. There has also been a noticeable shift in the increase in specialization. The generic data scientist function led to more specialized positions such as machine learning and data engineers. There was less emphasis on data scientists who could handle end-to-end tasks.

Let us not forget the significance of artificial intelligence. OpenAI tools like ChatGPT have made AI more accessible while improving data science efficiency and automation.

Data Science Ecosystem in 2024

Though overall job prospects have declined, the market has now stabilized.

Source: https://www.interviewquery.com/p/september-data-science-job-market

There is a strong demand for skilled workers in specialized roles.

Your ability to code is more vital than ever. This is especially relevant for employment in machine learning engineering, where you must use code to implement data science methodologies.

The programming languages used by data scientists have become more consolidated. Python is frequently the dominant language here.

Source: https://blog.jetbrains.com/pycharm/2023/10/future-of-data-science/

And SQL will be around forever. On the other hand, several languages, like R, SAS, and SAP, are losing favour.

So, if you're starting as a data scientist and must choose which language to learn, Python and SQL will always be available and dominating.

Interestingly, some jobs, such as data analysts and business analysts, benefit from the advent of low-code and no-code tools popularized by the rise of AI, notably plugins that ChatGPT may utilize to automate a large portion of data science labour.

Today, the data science market is more divided than it was previously. We can clearly distinguish between positions such as business analysts, AI/ML engineers, and data engineers. So, it's trifurcated?

Challenges for Data Scientists in 2024

Data scientists face the difficulty of demonstrating their value in terms of ROI.

Source: https://www.linkedin.com/pulse/value-roi-fiona-anderson/

The early excitement has dissipated, and businesses are looking for results. Data scientists will need to demonstrate their worth here.

They'll need to specialize in their expertise, such as AI & ML, data engineering, or data analytics.

Source: https://www.datacamp.com/blog/machine-learning-engineer-salaries-in-2023

Source: https://medium.com/@viduhewage02/roles-in-database-management-and-development-3072d3d221b0

Source: https://www.upwork.com/en-gb/services/product/development-it-an-expert-in-power-bi-tableau-and-microsoft-excel-for-data-analytics-1692392828735832064

They'll need to adapt to the new powerful tools being developed, like Google's Gemini, Galactica, and ChatGPT.

Education & Training

The changes in the data science industry have also impacted the educational and training landscape. As the field becomes more specialized, aspiring data scientists must carefully consider their focus areas and acquire the necessary skills to thrive in the evolving job market.

Best Private Colleges in Delhi NCR - SGT University

One institution that has recognized the changing trends in data science is SGT University, located in the Delhi NCR region. SGT University has established itself as a leading provider of quality education, with a strong emphasis on programs that cater to the growing demand for data-driven professionals.

Data Science Courses at SGT University

SGT University offers two prominent data science programs:

Bachelor of Science (Hons.) (Statistics & Data Science)

The three-year Bachelor of Science (Hons.) (Statistics & Data Science) program, also known as B.Sc. Hons in Statistics & Data Science is designed to teach students various statistical and data science skills and knowledge. The students will receive training in data collection, analysis, and interpretation, and the most recent statistical and data mining tools.

The course seeks to improve students' problem-solving and judgment skills, preparing them to face real-world data difficulties in various sectors.

• Annual Intake: 20 Students


• Duration: 6/8 Semesters


• Total Fee (Per Annum): INR 1,00,000


• Eligibility: 10+2 with 60% marks in aggregate with Mathematics as one of the subjects

Master of Science (Data Science)

The Master of Science (MSc in Data Science) is a four-semester degree that teaches students advanced skills and expertise in data analysis, management, and interpretation. Students are rigorously trained in programming languages, statistical modelling, machine learning, and data visualization. They acquire hands-on experience with real-world industry statistics, learning to spot patterns and trends, interpret data, and successfully communicate findings. Graduates of the curriculum are well-prepared to work as data analysts, scientists, and business intelligence specialists.

• Annual Intake: 10 Students


• Duration: 4 Semesters


• Total Fee (Per Annum): INR 1,00,000


• Eligibility: Bachelor with Statistics/ Mathematics/ Computer Science as one of the subjects (at least in one semester) OR equivalent degree from a recognized university with 55% marks.

These programs are designed to equip students with the necessary skills and knowledge to excel in the data science field, including proficiency in programming languages, statistical analysis, machine learning, and data visualization.

Conclusion

The data science industry has undergone a transformative period of challenges and opportunities. As the field becomes more specialized, aspiring data scientists must be prepared to adapt to the changing landscape, acquire in-demand skills, and demonstrate their value to employers. Since the beginning, data science has been a constantly changing field. It has changed in the last several years and will continue in 2024. Data scientists must adapt, evolve with it, and rise to new challenges and opportunities. The main difficulties are specialization and keeping up with the latest AI tools developments. As has always been, you must demonstrate your value to the potential employer.

Institutions like SGT University are at the forefront of providing quality education and training to meet the evolving needs of the data science industry, ensuring that the next generation of data professionals is well-equipped to navigate the exciting future ahead. By offering programs like the Bachelor of Science (Hons.) (Statistics & Data Science) and the Master of Science (Data Science), SGT University is preparing students with the necessary skills and knowledge to excel in the data science field, including proficiency in programming languages, statistical analysis, machine learning, and data visualization.