Tag Archives: Data Science
Chi-square test – Types, Concepts, Examples
The Chi-square (χ2) test is a statistical test used to determine whether the distribution of observed data is consistent with the distribution of data expected under a particular hypothesis. The Chi-square test can be used to compare two distributions, or to assess the goodness of fit of a given distribution to observed data. In this blog post, we will discuss the types of Chi-square tests, the concepts behind them, and how to perform them using Python / R. As data scientists, it is important to have a strong understanding of the Chi-square test so that we can use it to make informed decisions about our data. We will also provide …
Hypothesis Testing Steps & Real Life Examples
Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, and, how to formulate them. The knowledge of hypothesis formulation and hypothesis testing holds the key …
Insurance Machine Learning Use Cases
As insurance companies face increasing competition and ever-changing customer demands, they are turning to machine learning for help. Machine learning / AI can be used in a variety of ways to improve insurance operations, from developing new products and services to improving customer experience. It would be helpful for product manager and data science architects to get a good understanding around some of the use cases which can be addressed / automated using machine learning / AI based solutions. In this blog post, we will explore some of the most common insurance machine learning / AI use cases. Stay tuned for future posts that will dive into each of these …
Invoice Processing Machine Learning Use Cases
Invoice processing is a critical part of any business. It’s the process of creating, managing, and paying invoices. Without invoice processing, businesses would have a difficult time keeping track of their finances. There are many different invoice processing use cases. For example, businesses can use invoice processing to keep track of customer payments, manage vendor contracts, and streamline their accounting processes. Invoice processing can also be used to detect fraud and prevent errors. Machine learning / AI can be used to improve invoice processing in a number of ways. As a product manager, it will be helpful to understand these use cases and how machine learning can be used to …
Tail Spend Management & Spend Analytics
Do you know where your business is spending its money? And more importantly, do you know where your business SHOULD be spending its money? Many businesses don’t have a good handle on their tail spend – the money that’s spent on things that are not essential to the core operations of the company. Tail spend can be difficult to track and manage, but with the help of spend analytics tools and machine learning, it’s becoming easier than ever before. In this blog post, we’ll discuss what tail spend is, how to track it, and how to use analytics and machine learning to make better decisions about where to allocate your …
Procurement Advanced Analytics Use Cases
The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organization, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making. One can get started with procurement analytics with focus …
Probability: Basic concepts, formulas, and examples
Probability is a branch of mathematics that deals with the likelihood of an event occurring. It is important to understand probability concepts if you want to get good at data science and machine learning. In this blog post, we will discuss the basic concepts of probability and provide examples to help you understand it better. We will also introduce some common formulas associated with probability. So, let’s get started! What is probability and what are the different types? Probability is a concept in mathematics that measures the likelihood of an event occurring. It is typically expressed as a number between 0 and 1, with 0 indicating that an event is …
Spend Analytics Use Cases: AI & Data Science
In this post, you will learn about the high-level concepts of spend analytics in relation to procurement and how data science / machine learning & AI can be used to extract actionable insights as part of spend analytics. This will be useful for procurement professionals such as category managers, sourcing managers, and procurement analytics stakeholders looking to understand the concepts of spend analytics and how they can drive decisions based on spend analytics. What is Spend Analytics? Simply speaking, spend analytics is about performing systematic computational analysis to extract actionable insights from spend and savings data across different categories of spends in order to achieve desired business outcomes such as cost savings, …
When to Use Z-test vs T-test: Differences, Examples
When it comes to statistical tests, z-test and t-test are two of the most commonly used. But what is the difference between z-test and t-test? And when should you use Z-test vs T-test? In this blog post, we will answer all these questions and more! We will start by explaining the difference between z-test and t-test in terms of their formulas. Then we will go over some examples so that you can see how each test is used in practice. As data scientists, it is important to understand the difference between z-test and t-test so that you can choose the right test for your data. Let’s get started! Difference between …
Two sample Z-test for Proportions: Formula & Examples
In statistics, a two-sample z-test for proportions is a method used to determine whether two samples are drawn from the same population. This test is used when the population proportion is unknown and there is not enough information to use the chi-squared distribution. The test uses the standard normal distribution to calculate the test statistic. As data scientists, it is important to know how to conduct this test in order to determine whether two proportions are equal. In this blog post, we will discuss the formula and examples of the two-proportion Z-test. What is two proportion Z-test? A two-proportion Z-test is a statistical hypothesis test used to determine whether two …
Decision Making Models: Concepts, Examples, Importance
Making decisions is a critical part of business operations. However, making the right decision is not always easy. There are a number of different decision models that organizations can use to make better decisions. In this blog post, we will discuss some of the most popular decision models, what is their importance, and explain how they can be used to create desired business outcomes with the help of examples. In addition, we will also learn how could data and insights be used to drive decisions while implementing different kind of decision models. A decision scientist should be aware of these concepts fairly well. Decision models & different types Decision making …
Difference between Data Science & Decision Science
Data science and decision science are two data-driven fields that have grown in prominence over the past few years. Data scientists use data to come up with conclusions or predictions about things like customer behavior and assess suitability of those conclusions / predictions, while decision scientists combine data with other information sources to make decisions and assess suitability of those decisions. The difference between data science and decision science is important for business owners to understand in clear manner in order to leverage the best of both worlds to achieve desired business outcomes. In this post, you will learn about the concepts of data science & decision science and what’s …
Loan Eligibility Prediction using Machine Learning
It is no secret that the loan industry is a multi-billion dollar industry. Lenders make money by charging interest on loans, and borrowers want to get the best loan terms possible. In order to qualify for a loan, borrowers are typically required to provide information about their income, assets, and credit score. This process can be time consuming and frustrating for both lenders and borrowers. In this blog post, we will discuss how AI / machine learning can be used to predict loan eligibility. As data scientists, it is of great importance to understand some of challenges in relation to loan eligibility and how machine learning models can be built …
Healthcare & Machine Learning Use Cases / Projects
AI & Machine learning is being used more and more in the healthcare industry. This is because it has the potential to improve patient outcomes, make healthcare more cost-effective, and help with other important tasks. In this blog post, we will discuss some of the healthcare & AI / machine learning use cases that are currently being implemented. We will also talk about the benefits of using machine learning in healthcare settings. Stay tuned for an exciting look at the future of healthcare! What are top healthcare challenges & related AI / machine learning use cases? Before getting into understand how machine learning / AI can be of help in …
Marketing Analytics Machine Learning Use Cases
If you’re like most business owners, you’re always looking for ways to improve your marketing efforts. You may have heard about marketing analytics and machine learning, but you’re not sure how they can help you. Marketing analytics is an essential tool for modern marketers. In this blog post, we will discuss some of the ways marketing analytics and AI / machine learning / Data science can be used to improve your marketing efforts. We’ll also give some real-world examples of how these technologies are being used by businesses today. So, if you’re ready to learn more about marketing analytics and machine learning, keep reading! What is marketing and what are …
Machine Learning Use Cases for Climate Change
Climate change is a serious issue facing the world. The climate changes which are already affecting our planet can be seen in rising sea levels, melting ice caps and glaciers, more severe storms and hurricanes, more droughts, and wildfires increased precipitation in some areas of the world while other regions experience less rainfall. It’s important that we do what we can to reduce climate change risks by reducing greenhouse gas emissions into the atmosphere as well as adapting to climate impacts. Artificial intelligence (AI), machine learning (ML)/ deep learning (DL), data science, advanced analytics have been widely used for decades across different industries such as finance, healthcare, etc., but their …
