*This article originally appeared on the Udacity Blog on September 7, 2021.

Data science is constantly evolving and is expected to continue growing rapidly over the next several years. It has even led to the creation of fields within artificial intelligence like computer vision, deep learning, and natural language processing. We’ve raked through the most popular sources for technology and created a short list of the most recurring trends in data science this year.

Small data.

The format and volume of small data is what makes it so accessible for people, not just machines. Allen Bonde defines small data as what “connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged- often visually- to be accessible, understandable, and actionable for everyday tasks.” This trend will probably predict how we personalize technology and make data work for people, instead of making people work around data.

TinyML.

TinyML is machine learning that makes deep learning networks small enough for small hardware. It should be no surprise that TinyML has been trending because of the need for everyone to have a smartphone and maybe even a tablet. Keeping up with the different platforms and applications will tell us how machine learning will adapt to keep a whole world of information at the touch of our fingertips.

Synthetic data.

This fake “data” that is created by a computer program is used for training machine learning models. Using synthetic data can save you the time and money it takes to collect the large amounts of real data needed in order to train a model quickly.

Deepfakes.

According to Exploding Topics, the search for “deepfake” has increased by 18% alone in the past year alone, with a volume of 33,100 Google searches per month. With the ever-growing trend of young people loving SnapChat and TikTok filters, getting in on the ground floor of deepfakes now will allow you the ability to make the potential power of deepfakes work for you instead of against you.

Generative AI.

This type of AI can be unsupervised or semi-supervised in machine learning. The algorithm lets the computer use code, content, and data to make brand-new versions. This type of machine learning allows computers to have an understanding of how the real world works and predict outcomes. 

AutoML.

Automated machine learning is exactly what it sounds like. The increasing demand in AutoML allows people who don’t have expert knowledge in machine learning to use premade programs with ease.

Augmented analytics.

This technology will help with preparing data, providing insights, and explaining how the data can be analyzed and explored. The automation will help to speed up and streamline the process of analyzing data to assist experts and help clarify to the casual user.

Explore how you can make your data work harder.

Machine learning and artificial intelligence are developing faster and in more ways than we could imagine. We’re seeing lots of trends in automation and machine learning technology that can help itself even more than humans can. Keeping up with these data and analytics trends will help you prepare for what is soon to come in 2023. 

You can also gain real-world data science experience and keep up with the latest from the world of data science with the Data Scientist Nanodegree program.

*This article originally appeared on the Udacity Blog on June 10, 2021

Bioinformatics sounds like a futurist-type of occupation that could only be found in the not-too-distant future, the discipline is here and growing fast.  Bioinformatics is the combination of computer science, data analytics, and biology.

Basically, it is the process of collecting, storing, and processing massive amounts of data using powerful computing programs, but the data that is collected and analyzed is biological data.

Bioinformatics has been used for cutting-edge, scientific studies like DNA sequencing, analyzing biological networks in systems biology, and simulating biomolecular interactions.

While often confused with computational biology — the use of bioengineering tech and computers to study biology — bioinformatics has a heavier emphasis on the study of computer science techniques in order to aid the study of biology.

Both require extensive knowledge of computer science, data analysis, and biology, but bioinformatics is about building the tools for studying biology while computational biology is about studying biology using those tools.

Bioinformatics Skills

There are a hefty list of skills required to work as a bioinformatician and many who work in the field hold at least a Masters of Science, if not a Doctorate degree. Since bioinformatics is a cross-disciplinary career, bioinformaticians must have skills related to programming, mathematics, and biology.

According to BiteSizeBio, the top skills required to be a bioinformatician include:

• Statistical analysis

• Programming 

  • Scripting languages such as Python, R, or Matlab

• Machine learning

• Database management 

• Data mining

• Genomic and genetic knowledge

• Bioinformatics tooling

• Biology specialization 

  • Molecular biology, genetics, cancer biology, or modern biology

• Bioinformatics tooling

  • Sequence alignment tools, Genome Analysis Toolkit (GATK), gene data sets, etc…

Bioinformatics Salary

The salary for bioinformaticians range a lot based on two big factors: highest degree earned and location. According to Glassdoor, the average pay for a bioinformatician is around $96,000, but a bioinformatician living in the Bay Area with an MS or Ph.D can earn well over $150,000 a year.

Read The Bioinformatics Salary Report for a more in-depth look into the earning potential of a bioinformatician.

Day in the Life of a Bioinformatician

Bioinformaticians work closely with technicians, biologists, and computational biologists on a daily basis. Most days, they are studying biological data sets — like genomic, proteomic, and post-genomic databases — and helping technicians use and improve bioinformatics tools.

Through complex analysis and mathematical knowledge, bioinformaticians develop algorithms and analytical tools for scientific projects (this is why biological knowledge is critical).

Bioinformaticians also need to be up-to-date on current trends in existing bioinformatics software and often recommend tools to biologists as well as help them configure, customize, learn, and debug the software.

Technology and Healthcare

At the recent AI for Healthcare Virtual Conference hosted by Udacity, one of the top takeaways was the usefulness of wearable tech. Wearable tech can gather real-time medical data on the wearer overtime to help doctors notice trends and spot signs for deadly diseases much faster than they could from running tests in the lab once the patient notices symptoms and sees the doctor.

Additionally, artificial intelligence (AI) was predicted to be one of the top disruptors of the healthcare industry in the coming years. From diagnosing diseases to predicting pandemics, AI technology can be applied to the healthcare industry to save lives and help keep everyone healthier.

Analyzing the data that comes from just one piece of wearable tech is computationally heavy. Analyzing big data that comes from millions in the population in order to predict pandemics is also computationally heavy. More research and new tools must be developed to make this technology accessible to everyone.

Your Future in Bioinformatics

If working on world-changing technology in bioinformatics sounds exciting, you’re in luck. Job growth in the bioinformatics field is projected to grow up to 9% in the coming years.

You can begin learning today by enrolling in the AI for Healthcare Nanodegree program from Udacity.

*This article originally appeared on the Udacity Blog on March 9, 2021

Of all the ways that technology has transformed our lives, innovations in healthcare are some of the most impactful. 

Telemedicine, artificial intelligence (AI)-enabled medical devices and blockchain electronic health records are just a few concrete examples of digital transformation in healthcare, which is completely reshaping how we interact with health professionals, how our data is shared among providers and how decisions are made about treatment plans and health outcomes.

Here are some of the innovations in healthcare we can thank technology for and what’s in store in the near future.

#1. Healthcare On Demand

Countless apps offer on demand healthcare services, enabling patients to get care without actually needing to call or visit a doctor’s office. Scheduling visits, payment, reports and more can be handled through the app. Plus, some apps offer house visits or virtual appointments, while others can be used to search and book appointments for things like flu shots and blood work.

With healthcare on demand, it’s important to note that patients, medical practitioners and healthcare administrators all stand to benefit.

For patients, it’s ease of accessibility and the convenience of being able to use an app anytime, anywhere. You’re saving time not having to call for appointments, and in the case of housecalls, you can get the medical care you need right from home. Ease of accessibility is particularly challenging for those in remote locations, and healthcare apps make it that much easier for them to get care. 

For doctors and administrators, paperwork is highly time consuming, so having apps that process much of the paperwork saves time and effort — both of which could be spent on patient care instead. 

On a broader scale, the accessibility of healthcare on demand means it’s easier to address issues when they pop up. The more people who can be proactive about their healthcare, the lower the hospitalization rates are and the less burden on the medical system overall.

What’s Next in On-demand Healthcare

You can expect to see an increase in healthcare on demand apps in the market, with a focus on even more advanced patient care options and extended services moving to a virtual (versus in person) environment. 

#2. Artificial Intelligence & Virtual Reality

There’s no question that artificial intelligence and virtual reality have been a game changer when it comes to innovations in healthcare. 

The combination of AI & bioinformatics is being used to improve overall healthcare for the entire population. The field is growing so rapidly that specialized education programs that focus on AI in healthcare are becoming more popular. 

AI is often used to automate repetitive tasks and process large sequences of data, so when considering what that could mean for the future of AI in healthcare, doctors will have more and more time to focus on their patients. Things like surgery, clinical diagnosis and decision support, early detection and drug development are all aided by AI-focused innovations in healthcare. 

Also, Virtual Reality (VR) can be used to simulate surgery, calm patients, allow physicians across the globe to partner for patient care, increase speed of healing for physiotherapy and more. 

What’s next for AI & Virtual Reality in Healthcare

More advanced AI that can extract information for a patient’s overall healthcare footprint, the use of AI systems for specialist diagnostics in general care, advanced virtual assistance for patients and more. 

#3. Mobile Medical Devices

The global market for wearable medical devices is expected to surpass $46 billion by 2025. From mobile heart monitors to oximeters and devices to monitor blood sugar, innovations in healthcare have made all of our lives a little easier.

Mobile medical devices can help consumers monitor some aspects of their health more closely and promote better health, from keeping track of how many steps they get during a day to hitting their target heart rate zone during a workout.   

For patients with more complex medical issues or who may be at higher risk for certain outcomes, mobile medical devices can monitor vital statistics. This data can provide health care practitioners day to day data they may not have had access to otherwise and this data may also help predict possible outcomes or if some is at risk of a major medical event. 

What’s Next for Mobile Medical Devices

Advanced devices related to mental health monitoring, more devices for monitoring vitals, devices communicating to various healthcare professionals in real time and more. 

#4. Big Data

Thanks to technology, healthcare providers and managers have endless amounts of data at their fingertips. 

Innovations in healthcare leverage big data by using statistics from a specific or a broad group mined from medical imaging, pharmaceutical research, wearable or other medical devices, electronic health records and more.

There are countless ways that big data can help the field of healthcare, particularly when it comes to preventative medicine. By using data to monitor and analyze individuals and various populations, medical professionals can more accurately assess risk factors and potentially minimize and mitigate certain outcomes. 

Big data also comes into play for medical research. Knowledge is power, so the more information researchers have about past and current patients, treatments and outcomes, the better solutions they can develop.

Data can also play a pivotal role in reducing medical errors. The more data you can gather about what went right or wrong, the better positioned providers are to identify and avoid common mistakes.

What’s Next in Big Data for Healthcare

Continued innovation for data driven patient care, advanced logistics for prioritizing patient care, greater integration of IoT technology for patient monitoring and more. 

The Future of Innovations in Healthcare

Innovations in healthcare have brought numerous benefits to our lives and as we look towards the future, we can expect this to not only continue, but increase in speed. From drone delivery of medical supplies to disruptive approaches to things like stem cell or cancer research, the world of healthcare innovations is and will continue to be an exciting place. 

Are you interested in an exciting career that combines healthcare and technology?

Learn more about how Udacity’s AI for Healthcare Nanodegree allows you to be at the forefront of the revolution of AI in Healthcare, and transform patient outcomes. 

AI is everywhere. The proliferation of artificial intelligence has streamlined how we live and work. You can find AI examples in nearly every industry — including healthcare, entertainment, and tech.

The relevance of AI in every industry is part of the beauty of being an AI specialist. You don’t have to get pigeon-holed into one particular industry. Virtually every industry can benefit from someone skilled in AI.

To put a finer point on how AI specialists can find work in the industry of their choice, here are three AI examples in wildly different vocations that benefit from using this technology.

AI Example 1: Helping to Heal

Healthcare professionals spend years in school learning how to diagnose and treat patients with various illnesses. Even with all of that training, things will slip through the cracks because the amount of data to observe is too much for one person to analyze. 

Artificial intelligence has changed that.

In an interview between Web MD , David B. Agus, MD —a professor of medicine and engineering— talked about how applying machine learning to millions of patient data points helped healthcare workers discover that putting ovarian cancer patients on a beta-blocker helped them live almost 5 years longer. 

Individual doctors and researchers may have never made this connection, but machines using AI were able to connect the dots.

This is just one AI example applied to healthcare that can help medical professionals use this technology to improve patient outcomes.

Register for our AI for Healthcare in the TIme of COVID-19 Virtual Conference to learn more about the practical applications of AI and how it’s helping fight the spread of this deadly virus.

AI Example 2: Streamlining Travel

According to the National Safety Council, 38,000 people lost their lives in car crashes in 2019. Of those accidents, 90% of them involved human error, particularly due to distracted driving.

Artificial intelligence doesn’t suffer from the same distractions.

Self-driving cars are able to take in and compute vast amounts of data without feeling the urge to look away from the road or send a quick text to a friend. 

While there have been reported accidents from companies testing the technology, autonomous vehicles are significantly safer than regular cars.

Top experts studying self-driving cars predict that vehicle automation will become the norm within the next decade. It’s not uncommon to see new vehicles come with AI features like automatic parking. In fact, Tesla already sells cars that can drive themselves on the freeway.

If you’re interested in learning more about vehicle automation, check out Udacity’s Self-Driving Car Engineer Nanodegree program.

AI Example 3: Smarter Entertainment

Due to shelter in place orders that are mandatory all over the world, people are spending more time than usual consuming various forms of entertainment. 

But even with extra time on your hands, there is so much entertainment content available that it’s actually impossible to consume it all, even if you kept the TV on nonstop. 

Sure, you can get recommendations from friends, but they likely have vastly different tastes than you do.

That’s where AI comes into play. 

Companies like Netflix and Spotify leverage machine learning algorithms to learn what kind of entertainment you like best. Once you’ve finished watching a show or listening to a song, they will recommend a new one for you to try, based on large amounts of data points, like your age, location, and previously watched content.

Become an AI Expert

There’s never been a better time to work in AI. According to LinkedIn’s 2020 Emerging Jobs Report, jobs for AI specialists has grown by 74% every year for the last four years.

Based on the way technology is constantly iterating job growth will continue, if not increase.

If you’re interested in making a meaningful impact using AI in an industry you’re passionate about, check out Udacity’s School of AI to begin your learning journey. 

Programs range from technical (Intro to Machine Learning with TensorFlow and Deep Learning) to business (AI for Business Leaders and AI Product Manager), so there’s something for everyone.

*This article originally appeared on the Udacity Blog on September 7, 2021

Data mining is the process of combing through mountains of data to find patterns and insights. When it comes to business, making decisions based on data increases the effectiveness of running your company and a greater return on investment (ROI).

“Businesses that utilize data mining are able to have a competitive advantage, better understanding of their customers, good oversight of business operations, improved customer acquisition, and new business opportunities,” according to WGU.

That all sounds great, but how does it actually work in the real world?

Understanding Data Mining for Business Analytics

There are seven steps for data mining to be used effectively by businesses.

1. Define the problem

Data mining, especially when used for business analytics, is not just taking whatever data is available and looking for patterns. Instead, the process begins by clearly defining a business problem that you want to be solved. For instance, it could be finding ways to increase sales or get more return customers.

2. Select your dataset

When using data mining as a business strategy, it’s important to be selective about what data you collect. The best datasets are perfectly curated to give insight into the business problem. For instance, if you were looking at ways to get more return customers, you would want to collect data based on the customer and what they have bought from you in order to create customer profiles. Data like age, location, and income would all be useful.

3. Collect data

Once you’ve determined what data you collect, you’ll need to use a data engineer to create the data pipeline for actually collecting the data from customers and putting it in a usable format.

4. Analyze data

Once the data is collected, a data scientist will sift through the data to remove outliers and the like. Then, they will analyze the data and search for patterns that can help solve the business problem.

5. Make business decisions and changes based on outcomes

Once the results are in, it’s time to make concrete decisions based on the data. Since these choices are backed up by data, it’s easier to feel confident in the direction you choose.

6. Track changes

Believe it or not, the work is not over. Once you’ve made changes aligned with the results of the data, it’s important to keep collecting and analyzing data. Over time, it will tell you if the decisions you made are working.

7. Adjust and repeat

Check in regularly with your data and see if you see the results you want. If you do, keep doing what you’re doing and maybe make additional changes. If you don’t, hypothesize why the changes didn’t work and try again.

Areas to Use Data Mining for Business Analytics

The kind of problems to be solved using data mining for business will vary greatly depending on the type of business. Some common use cases include:

• Understand the customer base

• Analyze the competition

• Increase effectiveness of marketing

• Retain employees

• Grow sales

• Improve customer experience

The possibilities are endless and typically companies that use their data mining to creatively solve business problems are well rewarded.

Learn to Harness the Power of Data Mining for Business Analytics

If you think that data mining, analytics and online training programs for businesses could be a useful tool at your job, check out Udacity’s Business Analytics Nanodegree program. In as little as three months, you could be helping your company succeed by using business intelligence. Check it out!