The planning phase of a data project begins with forming a series of hypotheses, i.e., questions to confirm. A helpful way is to brainstorm the various ways in which a problem can manifest. We introduce mind maps to visualize the problem and the related measures to look at. Confirmatory analysis will be used at a later to confirm if the hypotheses are valid in contributing to the problem. The main purpose of this phase is to generate a series of items to analyze.

In this episode we discuss how to ask questions that deliver actionable insights that are useful for decision making.

In this episode, we dive into the data analytics process, breaking down the journey from problem identification to actionable insights into five essential steps.

In this episode, we explored the four levels of data analytics: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics helps us describe what's happening, like analyzing sales reports. Diagnostic analytics asks why things are happening, looking at factors influencing trends. Predictive analytics predicts the future based on past data, such as predicting employee performance when hiring. Finally, prescriptive analytics gives recommendations on what to do next, like how airlines use pricing strategies. Join us next time as we continue exploring data literacy!

In this episode, we explore the concept of data, from ancient forms like cave drawings to modern digital formats such as pictures, emojis, and social media posts. We discuss how digitization has revolutionized data handling and why data literacy is essential in today's information age. Join us as we uncover the value of data and its significance in our daily lives.