Researchers to test clay as algae bloom treatment

Minnesota researchers plan to test a promising technique that uses clay to remove potentially toxic algae from lakes and ponds.

Harmful algal blooms are a problem in many lakes, ponds and streams across the U.S.

The main cause isn’t a type of algae at all, but a form of bacteria called cyanobacteria that thrives in warm, nutrient-rich water. It can produce toxins that can affect water quality and harm people, animals and ecosystems.

Clay is widely used as a treatment method in other countries, but it hasn’t gotten as much attention in the U.S.

Now researchers from the University of Minnesota are proposing to test its use in Minneapolis stormwater ponds with a history of algal blooms.

The project stems from a 2020 federal law that directed the U.S. Army Corps of Engineers to develop methods to prevent and reduce harmful algal blooms.

“This whole program is really looking at new techniques to reduce these algal blooms and try to look at a holistic and natural way at reducing them throughout the nation,” said Trevor Cyphers, a fisheries biologist at the Army Corps of Engineers’ St. Paul office.

University of Minnesota researchers Miki Hondzo and Judy Yang have been leading the research into using clay to treat harmful algal blooms.

The Minneapolis demonstration project is a “real-world situation” to test clay-based treatment techniques that could be used on both large and small lakes, Cyphers said.

“I think it could produce a lot of good, useful tools that we could use to reduce these negative algal blooms in the future,” Cyphers said.

A warning sign about blue-green algae stands near the shoreline as swimmers enjoy Lake Nokomis little beach despite visible algae blooms.

Kerem Yücel | MPR News 2025

Under the proposal, researchers would spray a mix of natural clay and water on the surface of one stormwater pond. Another pond would be sprayed with a mixture of water and a synthetic clay called laponite, which naturally degrades and is generally considered non-toxic.

The clay particles and the cyanobacteria cells clump together and sink to the bottom.

“Once the algae is underwater, it then dies,” Cyphers said.

Chris Filstrup, a lake and stream scientist at the U of M’s Natural Resources Research Institute in Duluth, said the treatment method has shown success in removing cyanobacteria in the lab. They also tested it in larger tanks at Ohio State University, Filstrup said.

“We're really curious to continue to evaluate how it does when you upscale it to bigger and bigger ecosystems, like natural stormwater ponds” and eventually lakes, he said.

Researchers found that the synthetic clay, or laponite, actually removed more cyanobacteria at a lower dosage rate than traditional clay, Filstrup said. And because it’s translucent — meaning it allows light to pass through — it doesn’t cloud up the water, he said.

After the treatment, researchers plan to take samples of the water and sediment to measure how effective the clay-based treatment was. They’ll also look at whether it has any toxic effects.

The results will help inform scientists about whether the treatment methods could be used on a larger scale, such as Lake Erie, where harmful algal blooms are a costly annual problem.

“There's no limit of where it could be used if these clay-based treatments are useful, and they don't cause any major problems,” Cyphers said.

Stormwater ponds are an ideal place to test the treatment method because there are few fish or other aquatic creatures and no currents moving the water, Cyphers said.

Scientists are doing additional studies in the lab of the potential effects of clay on freshwater mussels or other aquatic organisms, he said.

The public has until June 29 to comment on an environmental review of the proposed project. If no significant impacts are found, the work would begin this summer or in 2027.

While clay treatments offer promise, Filstrup noted that they only treat cyanobacteria blooms — which are often a symptom of larger issues affecting a lake’s health, such as nutrient runoff or climate change.

“We’ve got to have those techniques, but I think you also have to look at the underlying problems and (be) trying to solve those as well,” he said.

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Brandy Hicks

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Data Science vs Business Analytics

1. What is Data Science?
2. What is Business Analytics?
3. Key Differences Between Data Science and Business Analytics
a.Basic Definition
b. Type of trends
c. Type of Data
d. Coding or Programming languages
e. Companies
4. Data Science vs Business Analytics
Roles and Responsibilities
Career path
Skills required
Type of Data
5.Conclusion

The popularity of Data Science has increased rapidly in the past few years and continues to increase with every passing data. As the organisations continue to create massive amounts of data, the implementation of Data Science becomes an obvious scenario.

If any company wishes to grow along with enhancing its user satisfaction, Data Science is something they need. Data Science uses modern techniques and tools to draw insights from that data which helps in making effective business decisions. It also uses several complicated Machine Learning algorithms to form predictive models.

Business Analytics is a practice used by companies to figure out what is happening in their business and how they can improve it. It helps in the overall decision making along with some future planning.

Since every company today is producing chunks of data, they need some data-oriented methods to draw insights from their past and present data to understand their loopholes which in turn helps them make some strategies keeping the current market trends in mind.

Now, when you know the basics of both Data Science and Business Analytics, it’s time to dive in deep and understand the main differences between the two popular terms.

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Key Differences Between Data Science and Business Analytics

There are several steps that are common in both like data gathering, data modelling, and drawing insights from that data. But, this is definitely not it, Data Science and Business Analytics are two big oceans that might meet somewhere, but are entirely different.

Let’s have a look at the differences between the two in elaboration.

Basic Definition

Data Science as the name suggests is the science of data, i.e. study of data using several Machine Learning algorithms, statistical tools, and other technological support. It is a combination of diverse fields like programming skills, mathematical principles, analytical thinking, and domain expertise to draw insights from huge amounts of data.

Business Analytics focuses on the business data and uses several analytical tools to draw insights from that data eventually scaling the business. It is a data-driven approach that focuses on historical data, identifying trends from there, checking out if there is any pattern and if there was a problem, what is the root cause of that problem.

Type of trends

Data Science focuses on all the trends and patterns leaving no page unturned to make an effective business model.Business Analytics revolves around the trends and patterns that reveal insights related to a particular business.

Type of Data

Data Science focuses on all types of data structured, semi-structured and unstructured data. To understand that structured data is highly refined and everything is just in front of your eyes, unstructured data is all complicated with no clarity on the type of data. So, Data Science uses several tools and techniques to work on different types of data. Business Analytics is concerned with organisational data. It uses several data analytics tools and other statistical principles to explore the organisational data and have an effective decision-making process.

Coding or Programming Languages

Data Science requires some rigorous algorithmic coding, statistical tools, and other analytical work to draw insights from tons of data. Languages like R and Python are widely used in several Machine Learning algorithms. Also, when unstructured data is concerned, knowing a programming language is a must. Apart from R and Python, you can also choose to learn C, C++, Perl and Java.

Business Analytics requires minimum coding as it is mostly focused on drawing insights using several statistical methods. Even if there is something advanced to be done, you can use advanced statistical methods as mostly the data is concerned with a single problem. So, business analytics tools like Tableau and Splunk are enough to draw insights from the organisational data.

Companies

Data Science is used in several big sectors today like e-commerce, machine learning, design and manufacturing, and marketing and finance. Data Science helps companies to understand how they can use their data effectively, whether it is about taking important business decisions or hiring more employees or even keeping a check on the workflow.

Business Analytics is used in industries like healthcare, marketing and finance, supply chain, and telecommunications. The biggest advantage of using business analytics is the reduction of risk as when the decisions are made using Business Analytics there are several factors covered like customer data, their preferences, market trends, the popularity of products etc, which may be missed otherwise.

Now, when you know the difference between Data Science and Business Analytics, let’s distinguish between a Data Scientist and a Business Analyst.

Data Scientist vs Business Analyst

Data Science is way bigger than Business Analytics and considers several factors that Business Analytics doesn’t even think of. While Business Analytics just focuses on business-related issues, Data Science even digs into the influence of factors like weather, customer preference, and several seasonal factors.

Let’s understand the differences between the two on a professional level, i.e. the differences between a Data Scientist vs. a Business Analyst.

Roles and Responsibilities:

Roles and Responsibilities of a Data Scientist include extracting and organising data. They draw meaningful insights from that data which could be structured or unstructured. To do all of it, they must have good knowledge of Machine Learning, Statistics, Probability, and other mathematical skills. Furthermore, they must have a firm grip on concepts like Python, R, Spark, Hadoop, and Tensor flow.

The roles and responsibilities of a Business Analyst include communicating with clients and providing them with business solutions. They must have great interpersonal and management skills to assist clients in designing and implementing relevant technical solutions. Along with all the assistance, they are always on their A-game in monitoring the overall business growth.

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Career path – The future

No matter what the sector is, be it healthcare, finance, management or transportation, the data needs to be taken care of and insights must be taken from that data for that industrial segment to grow. So, to make sure this happens, companies are looking for experts and no doubt Data Scientist is one of those job roles that are in most demand today and are one of the highest paying jobs in the world. The demand for Data Scientists is not going to reduce anytime soon considering the rapid production of granular data across the globe.

Business Analyst is one of those jobs that report a great level of work-life balance and job satisfaction. Again, it is one of those job roles that have a lot of openings in the market and one of the well-paid jobs too. Business Analysts are in great demand among organisations that are looking forward to scaling their businesses and improving their overall performance. The best part is the role of a Business Analyst is not limited to one designation, it changes from company to company. There are several roles that you can pursue if you have expertise in Business Analysis like Network Analyst, Project Manager, Data Analyst, and Business Consultant.

Skills required

Skills required to be a Data Scientist include:

Python – Data Science requires a firm hold of programming languages. When it comes to programming in Data Science, Python is one of the most widely used programming languages as it is easy to use and highly adaptable, even for people without a coding background.

Keras – Keras is used for artificial neural networks as they provide a python interface. Hence, they are used when it comes to experimentation with neural nets, that too at a great speed.

PyTorch – PyTorch is another deep learning framework extremely popular for its agility and compatibility with the Python framework. The framework simplifies the overall process to create an Artificial Neural Network (ANN).

Computer Vision – Computer Vision enables the Data Science systems to extract knowledge from images and videos to make necessary decisions.

Deep Learning – Deep Learning is something that makes the entire Data Science system more accurate as it enables the creation of extremely complex models.

Natural Language Processing – Natural Language Processing or NLP is something that is bridging the gap between Data Science and humans, by teaching computer systems how to read and interpret like humans.

Problem-solving – Problem-solving just doesn’t refer to the problem that is in front of you, being a Data Scientist you are responsible for solving problems that may be hidden.

Analytical Thinking – Data Scientists must have an eye for detail and analyse problems before actually starting to deal with them. It is important to examine the problem from all verticals and then reach an effective conclusion.

Skills required to be a Business Analyst include:

Programming skills – Programming Skills are not a must for a Business Analyst, but having some is always a plus. For example – knowledge of R and Python can help you in a quick and effective analysis of data.

Statistical analysis – Business Analysis requires a good knowledge of statistics and knowledge of different statistical methods to interpret real-world situations.

Business Intelligence tools – Business Intelligence or BI tools enable you to understand different trends and insights from business data, which is important to make impactful decisions.

Data mining – Data mining is one of the important skills of Business Analysis as it is about digging relevant information from chunks of data. So, companies use software to look for patterns and graphs in data and make relevant business decisions accordingly.

Analytical problem-solving – Business Analysts are about solving issues coming from customers or other stakeholders, so having the skill of analytically solving problems is a must.

Data visualisation – To make any important and accurate business decisions, the first and foremost step is to visualise or examine data chunks to understand market trends and loopholes.

Type of Data

Data Scientists work on both structured and unstructured data to fetch insights from huge chunks of data.

Business Analysts are just concerned about the structured data. They work on that data with several Business Intelligence tools to draw insights.

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Conclusion

By now, you would be well versed with everything you need to distinguish between the two most popular terms today – Data Science and Business Analytics. You began with learning the basics of the two and once you knew their basics you went on to differentiate between them.

While we were checking the differences between Data Science and Business Analytics, we checked several parameters to differentiate them and saw how they are different in the current scenario. While one is more technical and broad, the other one is comparatively less technical but a lot business-oriented and comparatively more specific.

You not only learned about the difference between the two huge concepts but also saw their differences on the professional level by finally distinguishing between a Data Scientist and a Business Analyst. In that segment you saw how one of them has to be proficient at coding and several statistical tools, after all, they operate on both structured and unstructured data, while the other one needs Business Intelligence tools to work on structured data and draw relevant business insights.

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