Four different Data and Analytics techniques

• Descriptive analytics answers questions like “What happened?”. For example, what was the revenue in December? This approach includes reporting tasks and working with BI tools.
• Diagnostic analytics goes a bit further and asks questions like “Why did something happen?”. For example, why revenue decreased by 10% compared to the previous year? This technique requires more drill-down and slicing & dicing of your data.
• Predictive analytics allows us to get answers to questions like “What will happen?”. The two cornerstones of this approach are forecasting (predicting the future for business-as-usual situations) and simulation (modelling different possible outcomes).
• Prescriptive analytics impacts the final decisions. The common questions are “What should we focus on?” or “How could we increase volume by 10%?”.

What are Machine Learning algorithms?

They are mathematical models that teach computers to learn from data and make predictions without being explicitly told what to do. They're like magic formulas that help us find patterns and make smart decisions based on data.

Some of the main types of Machine Learning algorithms:

1️. Supervised Learning: These algorithms learn from labeled examples. It's like having a teacher who shows us examples and tells us the answers. We use these algorithms to predict things like housing prices, spam emails, or whether a tumor is benign or malignant.
2️. Unsupervised Learning: These algorithms work with unlabeled data. They explore the data and find interesting patterns on their own, like grouping similar things together or reducing complex data to simpler forms. It's like having a detective who uncovers hidden clues without any prior knowledge.
3️. Semi-supervised Learning: This type of algorithm is a mix of the first two. It learns from a few labeled examples and a lot of unlabeled data. It's like having a wise mentor who gives us a few answers but encourages us to explore and learn on our own.
4️. Reinforcement Learning: These algorithms learn by trial and error, like playing a game. They receive feedback on their actions and adjust their strategy to maximize rewards. It's like training a pet: rewarding good behavior and discouraging bad behavior until they become masters of the game.
5️. Deep Learning: These algorithms mimic the human brain and learn from huge amounts of data. They use complex neural networks to understand images, sounds, and text. It's like having a super-smart assistant who can recognize faces, understand speech, and translate languages.