Top 10 Python Libraries for Data Scientists

Machine learning and big data applications have seen a surge in usage over recent years. This is due to many factors but the most prominent can be attributed to the demand for businesses to possess data-driven insights. Inevitably this has forced data scientists to find the most efficient methods when building applications and machine learning models that can manage data in this way.

Python is a data scientist's best friend largely because of its simplicity as well as its range of libraries and frameworks that are specifically designed to create applications and manage data. What separates a Python framework or library from a language like R, Java, or Julia for data science is mainly its simplicity and the range of libraries and frameworks available.

In the realm of data science, there’s so much variety when it comes to how you can approach app development. Python is highly flexible, which will always be a major draw for data scientists. With that said, it’s important to not just know your options but ultimately how to leverage them. Here are some of the top choices for data scientists when it comes to Python libraries:

TensorFlow

This is a great choice when integrating machine learning that allows data scientists to get a visual understanding of how data flows through neural networks or processing nodes. It’s an open-source software library that Google created for users to build and deploy machine learning models and then train them at scale.

Pandas

This tool is super powerful for data manipulation and analysis as it provides structures and functions that work with pre-existing data. It also allows data scientists to easily transform and preprocess data which in the long run allows them to extract more of those valuable insights that we mentioned were in demand. Pandas’ ability to handle large datasets and integrate them with other libraries makes it a fundamental tool in a data scientist's toolkit.

OpenCV

As the name implies, this is another open-source library used for real-time computer vision tasks. With OpenCV, data scientists can do things that contribute to a much broader ideal for Artificial Intelligence. This includes tasks such as object detection, facial recognition, image stitching, and video analysis. 

Theano

This is one of the first open-source software libraries for deep-learning. It’s known for its speed (due to its ability to optimize) and efficiency when handling mathematical computations, especially those found in model development for machine learning. Of course, now TensorFlow is the renowned favorite when it comes to deep learning but the two collaborate well and offer unique advantages.

PyTorch

PyTorch is another popular deep-learning framework with dynamic computational graphs and highly productive GPU acceleration (Great for data-intensive apps). It provides an intuitive programming interface that’s flexible and has gained popularity because of how easy it is to use and the level of support it offers for research and prototyping deep learning models.

NumPY

This is an imperative library if you’re dealing with numerical and scientific computing in Python. Healthcare, Finance, Manufacturing, Research, and Education among other industries for example will all utilize NumPY to solve problems and manipulate large datasets unique to their needs.

Matplotlib

This library is your go-to for data visualization and analysis. It works with other Python libraries such as Pandas and NumPy which allows data to easily be manipulated and integrated. For app dev, this will help with the data-driven aspect through its range of features and plotting functionalities. 

Seaborn

Seaborn is a library built on top of Matplotlib for data visualization. It provides a higher-level interface and a variety of statistical visualizations. It simplifies the process of creating visually appealing and informative plots, which makes it valuable for data exploration and sharing results.

Statsmodels

As the name implies, this is a library for statistical modeling and hypothesis testing. It offers a comprehensive set of tools for regression analysis, time series analysis, survival analysis, and other statistical techniques. Statsmodels are very widely used in fields such as economics, social sciences, and finance.

Scikit-learn

This is a widely used machine learning library that provides a range of algorithms and tools for classification, regression, clustering, and dimensionality reduction. It's known for its user-friendly API and comprehensive documentation, making it an excellent choice for both beginners and experienced data scientists.

Choosing What’s Best For You

There’s a lot for data scientists to consider when narrowing down what libraries and frameworks are best for the task at hand. When it comes to Python, the number of options is a huge benefit but it doesn’t come without its challenges. 

If you don’t have expertise in particular libraries, it can be difficult to navigate integration, and learning them on the fly is not easy, nor is it efficient. 

Some quick notes about what you’ll generally want to look for include the following:

• Compatibility and integration: Ensure the library works well with your existing tools and frameworks. 

• Performance and efficiency: Look for libraries that are optimized for speed and that can handle large amounts of data efficiently.

• Documentation and resources: Look for libraries with clear documentation that explains and provides examples of how to use it. 

• Community support: Choose libraries that have an active community of users. 

• Scalability and extensibility: If you anticipate your project growing or taking on larger datasets, choose libraries that can scale and work well with distributed computing.

• Long-term viability: Choose libraries that are regularly maintained and updated. You’ll want to make sure the library will be compatible with newer versions of Python, that it receives bug fixes, and incorporates new features over time. 

The Takeaway

In Canada alone, around 90,000 SME's (Small and Medium Enterprises) disappear annually. This is prior to the introduction of AI, which means that “Staying competitive” is going to take on a whole new meaning in the years to come. You can’t fight it, but you can plan for it by adopting the right approach and consulting with experts who know how to navigate this change.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

8 Things to Know When Building a Reactive Machine Learning System

Every day that a business isn’t working to differentiate itself from its competitors is a day it’s going backward in its industry. As sophisticated IT infrastructures become the minimum standard and with data-driven decision-making fueling innovation, businesses must be proactive about finding the technology that gives them a competitive edge.

One of the big topics right now when it comes to gaining this edge is integrating Reactive Machine Learning, which to say the least, can be a game-changer for the businesses who utilize it effectively.

What is a Reactive Machine Learning System?

Instead of telling you all the things a reactive system is, it’s better to tell you what it isn’t:

• Reactive is the opposite of batch learning where a system takes one big dataset and then uses that to generate its insights and make predictions. Instead, it can process real-time data and respond immediately.

• Reactive learning is not a deliberative agent which focuses on analysis and reasoning before taking action. A reactive system instead uses predetermined rules or patterns that are meant to make the system act quickly.

• Reactive systems are not useful in complex decision-making processes such as long-term forecasting. Instead, something like fraud detection would benefit from a pre-determined system protocol. 

This covers the basics. Machine Learning models are made from algorithms that analyze data, recognize patterns and outliers in that data, and then make predictions or decisions. 

Where Does it Fit in a Business?

The evolution of the internet has exceeded comprehension looking back 20 years. With this, user standards have risen as well which has made integrating machine learning models essential for businesses to meet the standards of their industry. 

3 Ways a Business Might Utilize Reactive ML:

• Automating processes: Think about a chemical testing laboratory with a vast amount of highly sensitive data to be managed. Reactive ML can be used to prevent errors by automating the analysis aspect. As a result, the laboratory cuts down its processing time and increases the efficiency of instrumentation. 

• Energy consumption: Take a utility provider, for example. Reactive ML can optimize how much energy is consumed using real-time data to determine the appropriate adjustment. In addition to this, it can implement demand response programs by taking past data and identifying patterns to make recommendations on energy usage.

• Personalizing recommended content: This is what streaming services like Netflix or Disney+ use in the “suggested” section, or social media platforms for the type of content someone is fed. In this case, ML algorithms will be used to analyze user data and recognize patterns that determine what they’re fed. 

How to Build It

There’s a lot that goes into building a reactive ML system and the specifics will always vary just as with the construction of any complex IT platform. What businesses must do to carry it out effectively can be understood with these basics principles: 

1. Gather data: Collect relevant data that you want to train and validate the reactive ML algorithms on. Make sure that the data is accurate and diverse, and that it fits in the problems domain. Then, clean and preprocess that data to remove noise and handle missing values.

2. Train the algorithms: Choose the ML algorithms that you think best fit the problem at hand. Train the algorithms using your gathered data, adjust hyperparameters and then evaluate performance. Consider using techniques like cross-validation to ensure the system is well-rounded and that you’ll avoid overfitting.

3. Integrate the system: Once you’ve developed the necessary software infrastructure to integrate the reactive ML system, connect components. This may involve building pipelines, creating ingestion and processing mechanisms, and implementing decision-making modules based on the trained data we mentioned previously.

4. Test and evaluate: This is an essential piece of this puzzle. Use the appropriate evaluation metrics to assess the accuracy and effectiveness of the system. Then fine-tune the system based on the results and make the necessary iterations as you go (which leads to the next point). 

5. Monitor and maintain: Consistently monitor the performance of the reactive ML system in your production environment. In addition to this, update the model periodically as new data becomes available or when business requirements change. And lastly, regularly assess the system's impact on organizational outcomes and make adjustments as needed.

Again, these are very baseline as every project is going to have unique variables and every business is going to have unique goals. With that said, the most important part of digital transformation is what comes next, so with that in mind, consider this:

1. How scalable is the system? Whether you’re using a distributed computing framework, cloud services, or anything of the sort, the system needs to be designed while thinking about the volume of data and user requests it will need to handle.

2. What are your requirements for processing speed? If your reactive ML system needs to respond in real-time to user requests or traffic, processing speed becomes a major concern. To ensure it fits your ideal framework, you can optimize algorithms and hardware or use a distributed framework such as Apache Spark. Again, monitor the changes you make and keep looking for opportunities to refine.

3. How does it fit with current systems? When introducing reactive ML into your current systems, you have to consider how it will fit and interact with the infrastructure. APIs or connectors that enable data exchange are what you’re going to need if you want interoperability with existing systems.

The Takeaway

Finding components to build a framework that will support a business long-term is a never-ending quest. Doing what other companies are doing without an in-depth analysis of how the things you want to introduce will serve you long-term could set you back. It’s best to consult with an organization who’s overseen various projects

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

From Novices to AI Experts: Training Your Employees on AI for Business Growth

Integrating AI into your business is one thing, but getting the full benefit and ensuring long-term returns is another. Many companies are investing a vast amount of time and resources to implement AI technologies, but they may be flying blind without some key considerations.

To give you an example of these considerations, let’s take a retail business that implements chatbots to handle customer inquiries. Seems simple enough right? Well, the fact is that having the system is just the tip of the iceberg. The next priority is to familiarize employees with the system and focus on how its capabilities and limitations can impact their work. This training should also outline protocols that explain how to collaborate with the chatbots and when to escalate customer inquiries to human agents.

Over time, employees will benefit significantly from understanding how the system functions. You should be able to ask, “How does the chatbot interpret data? What sort of things do you not let it handle? Can the system learn from customer interactions? What metrics are you using to evaluate performance?” and be able to get a clear answer to every single one. 

While this is just one small-scale example, think about an enterprise business with petabytes of data and AI systems deployed across several departments. The range of considerations will vary dramatically depending on the industry, company size, and of course the business's unique goals. Your employees are your biggest asset, and if they’re going to be working extensively with AI, there are certain things that companies need to incorporate to ensure a smooth operation.

1) Knowing When To Use It

A report from Entrepreneur noted, “Human creativity and ingenuity will always be required to find the problems AI can solve in the first place”. It’s interesting to see tools released to the public like Chat GPT or MidJourney that can automate tedious tasks, but it’s even more interesting to examine the different tasks people deploy those tools for. 

For instance, if you're an advertising agency and you’ve trained your employees on efficient ways to use AI to generate ad copy or certain graphic artwork. Those employees are going to view the tools entirely differently than the high school administrators who see them as a means for students to cheat. 

With that being said, there’s a lot of speculation on the “right and wrong” ways to use these tools which makes it all the more important for businesses to recognize where it fits. In this case, it’s best to have deployment protocols, something that can dictate not only appropriate use cases for AI within the organization but also specific guidelines for managing AI systems.

2) Using AI to Better Understand Your Customer

Artificial intelligence has 3 main functions that are the building blocks for how it understands someone:

• Processing data: This is going to take a large volume of data from places like your website interactions or social media engagement, for example, and then put that data through algorithms to analyze it and extract meaning from it.

• Pattern recognition: AI is masterful at recognizing patterns and anomalies within datasets which makes it very useful for both security (threat detection) and predictive analytics (which we will get to next). These factors play a pivotal role in AI’s ability to use the data it’s processed and understand user behaviour in a range of contexts. 

• Predictive analytics: By far this is one of the most valuable functions of artificial intelligence. Because AI can recognize patterns, organizations are able to make proactive moves rather than reactive ones. Once the systems recognized patterns, it can recognize needs, trends, preferences, and buying patterns, detect threats, and how to optimize resources among other factors.

So, if these systems seem to do it all, why would a company need to educate employees on how to use them? Again, it comes back to capabilities versus limitations. Do your users always want to run inquiries through a chatbot? Or are there some aspects of your operation that require human oversight?

Systems will need guidance to some extent, and regardless of whether two businesses are in the same industry - they will each have unique processes and goals. This is why the emphasis is on collaboration with these systems because a team that knows how to use AI as an extension will be a lot more effective than the team who uses it as a shortcut. 

3) Maximizing Output

Imagine a manufacturing company that implements predictive maintenance systems that can detect potential equipment failures and then schedule maintenance before it happens. However, the employees responsible for the maintenance don’t know how to interpret the system's recommendations. Seems like a good waste of investment on the company's part. 

When a company implements AI in its processes, the goal should be to always have actionable insights. From this, companies can consistently bridge the gap and maximize the results from the system's output. 

This is going to lead us to the next point…

4) Leverage Actionable Insights

Having insights is one thing; taking proactive steps to translate them into tangible outcomes is another, and it’s where the most value is in terms of longevity. Here are 3 general ways businesses can make this happen:

Communication and collaboration: Insights should be distributed to stakeholders across all departments to ensure they have the necessary information to consider during their decision-making processes. 

Facilitate Discussion and Feedback: you should encourage regular feedback sessions where stakeholders can share their thoughts. This is going to create a collaborative environment that lets you consider alternative ways to execute based on AI-generated insights. Additionally, actively listening to feedback and implementing it will create a positive work culture that’s going to make your organization's transition to AI a lot more effective. 

Measure and Evaluate the Impact: Establish KPIs or metrics to measure the effectiveness of each decision made. This feedback loop allows for consistent refinement and streamlining of the decision-making process, again, moving toward a long-term sustainable internal process. 

The Company Who Does Vs. The Company Who Doesn’t

The main reason we’ve alluded to sustainability for training employees on AI is the stark difference between the future of companies that invest in training and those that neglect it. Think about the financial industry 15 years from now, “The company who does” will not only implement new AI systems, but they will educate employees about their significance which will make them more inclined and comfortable when leveraging AI tools. In the long term, processes will be a lot more efficient and the customer experience will stand out.

“The company that doesn’t” is one that may have an AI system, but neglects research, training, and education for employees. In the short term, they might see results from the system, however in the long term, without the necessary research, training, and education, their potential is capped. 

What’s Next for AI?

There’s so much refinement and constant innovation happening in the field of AI which emphasizes the importance of consistently learning more about this technology as a business. As soon as you become complacent in your industry, it’s already starting to work against you, which is why you need a well-educated team that is committed to staying up to date with the latest developments in AI and trends in the industry. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.