Applied Data Analytics

Data sits at the center of every modern business decision, but having data doesn't automatically produce insight. For most students and early-career professionals, the missing piece isn't the data itself, it's a structured way to think about it, question it, and act on it. This course builds that foundation, equipping you with frameworks, tools, and critical thinking skills to work confidently with data. You'll work through a proven five-step problem-solving framework, moving from defining the right business question to delivering data-informed recommendations. You'll practice core analysis techniques in Excel, apply the four foundational stages of analytics, and build dashboards that surface insights at a glance. You'll also map the full range of data sources driving business strategy, from sales and marketing to psychographic and competitive intelligence, sharpening your judgment to select the right source for each decision. By the end of this course, you'll be able to apply a structured analytical framework to real business challenges, evaluate and interpret the data sources that drive meaningful decisions, and communicate findings that influence strategy with clarity and confidence.e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.

Data is everywhere in modern organizations, but most of it is going nowhere. When analysts build dashboards without clarifying who they are for, or present findings without a clear recommendation, data loses its leverage at exactly the moment it should be driving a decision. The analysts who close that gap, knowing when to reach for a dashboard, when to build a data story, and how to move fluently between the two, are the ones shaping strategy rather than just reporting on it. In this course, you'll diagnose the structural and communication failures that keep organizations from acting on their own data. You'll design interactive Power BI dashboards tailored to specific stakeholder needs, construct narratives anchored in context, conflict, and resolution, and select visuals that make problems visible rather than decorative. You'll also build data story conclusions that close with specific, evidence-backed recommendations rather than open-ended summaries. By the end of this course, you'll select the right format, dashboard or data story, for any analytics challenge you face, and combine both into a repeatable workflow that moves organizations from raw numbers to confident, well-supported decisions.

The relationships in your data tell stories that row-and-column tables can't surface. Graph analytics gives you a way to map those connections at scale, turning networks of entities, transactions, and interactions into structures that reveal patterns no traditional query can show. Once you can read those patterns, you'll answer questions that table-based tools simply can't frame. In this course, you'll trace how graph analytics works from first principles, comparing it with traditional relational databases to see exactly where and why it outperforms them. You'll apply the core framework of nodes, edges, and properties to real-world problems in logistics, social media, and financial fraud detection, and then survey the providers and query languages that power graph databases in production today. By the end, you'll be able to identify which data problems call for a graph approach, map any network scenario to the correct nodes, edges, and properties, and choose among the leading graph database providers with a clear, defensible rationale.e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.