Applied Data Science and Analytics

By the end of this course, you'll be able to define what makes data "big," distinguish Big Data from a traditional data warehouse, and map the core layers of a Big Data architecture from ingestion to serving. You'll compare processing approaches like batch, streaming, and Lambda, weigh core technologies such as Hadoop, Spark, and Kafka, and evaluate on-premise, cloud, and hybrid hosting against the principles that matter most: performance, availability, scalability, flexibility, and cost. Led by a data and AI cloud architect with over a decade of hands-on experience building solutions for companies of all sizes, this course turns an intimidating buzzword into practical, decision-ready knowledge. You'll move beyond theory with a real-world case study, an inside look at cloud services, and a clear view of the business side of Big Data, from building data teams to avoiding the pitfalls that quietly derail projects. Whether you're a manager scoping your first Big Data initiative or a professional strengthening your foundations, you'll finish ready to make confident, informed choices about implementing Big Data in your own organization.

By the end of this course, you'll be able to run a regression analysis in Excel, SPSS, and R, interpret the results with confidence, and turn them into a story that drives real decisions. You'll uncover the relationships hidden in your data, choose the right type of regression for any question, check whether your model can be trusted, and separate genuine cause from mere coincidence. Data can feel overwhelming: the patterns hide, and it's hard to tell what's real from what's just chance. This course changes that. Guided by a senior data scientist who specializes in turning numbers into stories anyone can understand, you'll move step by step from the fundamentals of ordinary least squares to diagnosing model assumptions, handling outliers, and avoiding the most common analytical mistakes. What makes this course different is its focus on communication as much as calculation. Running the model is only half the job; the real skill is explaining what it means. You'll build clean charts, translate coefficients into dollars and decisions, and present findings that stakeholders actually remember. Whether you're a beginner or brushing up, you'll leave able to make your data talk.