5 Data Science Trends To Look For

Data science has driven major advancements in technology, transforming both our physical and digital worlds. Innovations in deep learning, natural language processing, and computer vision have led to digital assistants, wearable devices, streaming recommendations, and home automation.

Keeping up with the fast-paced evolution of data science can be challenging. The field has changed dramatically over the past decade, with new technologies, techniques, and tools emerging constantly.

Today, data science is unlocking more potential within organizations and becoming more accessible to teams of analysts and developers, not just specialists. This shift is revolutionizing how we hire, utilize, and assess data science talent.

To tap into these resources, organizations need to connect with the right data science companies. Pangea can help by connecting you with the best data science teams available. We vet and hand-pick recommendations based on your needs, ensuring you find the perfect fit to grow your organization.

Data Science Trends

Improving sustainability, building better spaces, enabling productivity, and creating business opportunities — today’s data science trends are transforming the way we see the world. Whether you’re looking at the world of data from an individual perspective, or that of an organization — data is transforming the way we see the world at an accelerating pace.

Here, we’ve collected some of the top data science trends impacting the industry, analyzed how they’re likely to affect organizations, and provide resources on how best to utilize these trends to your advantage. Here are the top 5 trends you need to know about in data science today:

1. Data Science is Becoming Increasingly Democratized with Better Tools and Resources

The availability of data science teams has historically been a limiting factor for organizations looking to revolutionize their applications and services. Hiring experienced, high-demand professionals in such a specialist field is a difficult task. It’s only recently, as tools and thinking have begun to shift, is it one that’s beginning to get easier.

Taking a look at precisely why data scientists are in such high demand today, we’ve prepared an information resource on the topic of data scientists and the methods to maximize productivity within your organization.

The tools and technologies now available to developers and analysts are allowing organizations to achieve more with less. Making frameworks and libraries available to developers, data science tooling is opening up opportunities for more engineers to lean on complex ML and AI technologies without the rich academic research background previously required.

This democratization of data science is, in turn, allowing data science teams to focus on and solve the most complex and difficult challenges within the field today.

N-ary tree structures are an example of data science techniques being made available to teams. Used to create some of the industry’s most common and effective question and answer-based decision systems, N-ary trees are key to modern services and a technology that many organization’s regularly depend on to provide customer service automations for some of the largest eCommerce services in the world.

This trend towards democratization is one that often follows in many areas of software engineering. By creating tools and automations to handle the trivial or mundane, developers and data scientists are able to make the most of their time and talents by focusing on the difficult parts of tasks that can’t easily be offloaded onto automations.

The opportunities available in this area are making data science specialists more productive and more effective within organizations. Our guide to hiring data scientists can help you bring on board the teams that can make the difference and focus on the biggest challenges you face today.

2. New Generations of Data Science Frameworks Taking Shape

The goals of modern data science typically align with injecting efficiency and speed into moving training models toward a production environment. Developers and data scientists today are accelerating towards these goals with new tools and frameworks being continually made available to offer enhanced learning capabilities and more computing power to teams.

The frameworks available now are rapidly becoming, not only more powerful and effective, but available for a wider number of platforms. Incorporating cloud and edge computing along with a host of new language features and data gathering capabilities, it’s modernized tooling that’s generating ever-increasing capabilities within the industry.

Within organizations, new technology is unlocking more and more of what’s possible today and creating efficiencies that have previously been impossible until now. New developments being made available include enabling GPUs to be used in machine learning pipelines, creating more accessible and efficient frameworks, and creating exciting new capabilities for developers to use.

Companies both inside and outside of tech are already seeing practical real-world benefits of implementing these tools as part of their processes.

Taking a look at the top data science frameworks available for use today, our article on the 10 Best Data Science Development Frameworks goes into detail on the most productive and accessible frameworks available to transform your teams and deliver real-world benefits into your organization.

3. Increasingly Predictive and Prescriptive Analytics

Over the last five or six years, huge investments have been made in data science by organizations looking to lean on the competitive advantage this rapidly evolving tech can bring. The result, of continued and ever-growing investment, has been the production and availability of a new generation of tools and resources capable of revolutionizing the field.

Today, these investments are beginning to pay significant returns. By moving beyond descriptive analytics and into predictive tools, organizations are being gifted new kinds of opportunities to grow their businesses as a result.

Since 2021, new techniques and technologies have emerged on an almost weekly basis. Central to this shift has been the emergence of cloud technologies and growing amounts of data being captured by companies.

While big data, defined by the cloud, has been around for several years now — it’s only in the last few years the technology has been so easily available and so productive for the teams that utilize it. Providing near-unlimited compute resources, eliminating data silos, and enabling broad data sharing — the cloud is one of several key technologies revolutionizing data science for organizations.

Taking a deep dive into these technologies, our article on the future of data science takes an expert look at where the field is today and what’s to come in the industry in the next few years.

4. Data-Driven Services

When it comes to consumer products and services, it’s almost impossible not to notice the rapid rise of machine learning. Many major retailers and service providers now employ AI chatbots and customer service tools as the first line of customer interaction to free up more valuable resources for complex queries.

Even software development itself is continually innovating with modern tools aimed at improving the way software is written by developers. Github copilot is just one example of a modern data science-driven tool aimed at assisting developers.

The effects of co-pilot and its use within software engineering teams have been surprising. Our article on the pros and cons of the technology can take you on a deep dive into its effect on both developers and teams able to improve their output through the use of this automated tool.

One of the results of this rapid growth in data-driven tech, has been an explosion of data-gathering capabilities from an increasing array of places. Resulting in the need for more analysis and even greater numbers of tools—teams are gaining increasingly greater insights into more and more aspects of an organization and its customers.

Data about how users stream content, drive, exercise, shop, walk, eat, and read is all coming online with increasingly smart and affordable devices being made available. For companies, the result of this data explosion is an opportunity to target applications, services, and devices to precisely meet user demand.

Whether you have a ready-made team in mind to jump on these opportunities as they’re discovered, or hire freelance professionals to work on projects as needed—being able to convert these opportunities into apps and services is key to making data science work for you in the future.

Answering the question, should You Hire Data Science Freelancers, or an Outsourced Data Scientist team? our article looks at the benefits and drawbacks of each approach when it comes to importing data science talent into your organization.

5. Converging Technologies

Mobile networks with superfast 5G capabilities are coming online with increasing capacity throughout the year, for example. At the same time, IoT devices are becoming increasingly present and AI technologies increasingly prevalent throughout software solutions.

With several, seemingly separate, technologies coming together this year the possibilities for more capable devices, services, and even spaces are becoming increasingly greater. Better connections to the cloud, more abundant computing power in cloud and edge technologies, and exceptional predictive tools are coming together to create truly industry-changing capabilities.

The convergence of these tools will be a building block toward the automations and connections necessary to create smart cities. Organization’s already up to speed with data science technologies should be looking to inject themselves into these opportunities as a driving force behind lifestyle and technology improvements.

Hiring Data scientists is going to prove key to implementing these automations within teams. Our guide to applying the best practices to hiring data science teams and our job description template to get you there can be the kickstart you need to enter the field in the weeks and months to come.

Data Science today

The days of data science being a closed and niche community are very limited. Already, the field is being opened up and readily embraced by developers, analysts, and project managers to deliver apps and services that define organizations.

The prevalence of the data science field throughout technology solutions requires some caution from organizations too, however. Managing big data teams requires experience, considerations, and knowledge that many may not have come across before in day-to-day software development. Even bringing in an external team to manage big data projects, takes a small amount of learning and adjustment to get to grips with expectations.

The Pangea guide to the do’s and don’ts of managing an outsourced big data development team can take you step-by-step through successful big data collaboration within your industry.

Knowing the fundamentals of data science is going to be increasingly important to enable organizations to meet users where they are, rather than where the company needs them to be. Sales, marketing, and management teams are going to need an increased awareness surrounding these technologies to succeed across all industries.

The future of data science is going to be defined by revolutionary new technologies such as AI, ML, IoT and big data. Taking a look at the impacts of these technologies on today’s marketplaces, our glimpse into the future of data science analyzes the effects each will have and what your company can do to prepare for them.

These five factors are going to be key to making data science a critical component of organizations for some time to come. Taking a closer look at why data science is shaping up to be one of the most important future skillsets, our article can highlight what you need to do to revolutionize your workflows.

Luckily, entry into this data science revolution has never been more straightforward. Your organization needs only access to the teams of experts capable of building the models, tools, and resources that will meet your user’s needs. In this field, Pangea has you covered! We provide access to the top-tier data science teams available today, pre-vetting, analyzing, and hand-picking from the top-tier teams that meet your requirements—simply tell us what you need and let us handle the rest.

FAQs

Q1. What is data science?

Data science is the field of performing analysis on large amounts of data to uncover patterns, find correlations, and deliver insights that can inform future decisions. The insight that data science requires leans on multiple disciplines and several areas of expertise.

Statistical analysis is a key resource for data scientists, allowing teams to draw meaningful correlations through seemingly disparate sets of data that wouldn’t otherwise be obvious. One key reason data science is such a major and exciting field today is that the abundance of data and analysis now available is creating opportunities for companies to gain more insight and deliver services to users that capitalize on demand.

Data science goes beyond tech to encompass health, fitness, as well as day-to-day decision making. Insights that went previously unnoticed are consistently being uncovered by data science techniques and the ever-increasing amounts of data being collected by our devices, wearables, and online habits.

Q2. Is data science a good career?

Data science is an excellent career path with extremely good opportunities for advancement in the future. Today, demand for data scientists is exceptionally high and with a continued growth in the field and an explosion in the amount of data we collect and use—demand for data scientists in the future is only expected to grow.

The average data scientist salary today in the United States is over $100,000. The U.S. Bureau of Labor Statistics also predicts that demand for the role is going to rise by more than 25% by 2026. For organizations, the power of big data to create opportunities, generate business, and capitalize on demand makes the role an exceptionally valuable one to bring on board.

For those looking for an interesting, lucrative, and dependable career that will last into the future—data science is an excellent route to pursue.

Q3. Is Python enough for data science?

Python is an excellent programming language to get started in learning data science—yet, it’s only part of the puzzle when it comes to a complete career path. Python, as a highly productive, concise, and forgiving language is one of the top choices when it comes to data science in industry.

In academia, R tends to be more popular to work with large data sets. Both languages have a rich array of packages that support data science workflows.

As a beginner-friendly language, Python is an excellent place to start and can take you a long way into a career in the data science field. In the future, however, it’s more than likely that having some additional language skills such as Java, C#, or C++ is going to pay dividends when it comes to writing tools and presenting data associated with most major projects.