Foundations of Data Analytics

In this lesson, students will learn how to craft compelling and well-structured introductory notes that effectively highlight the significance of data. They will explore key elements of an engaging introduction, understand how to set the context, and learn techniques to capture their audience’s attention from the outset. By the end, students will be equipped to create powerful introductions that emphasize the transformative power of data.

In this lesson, students will explore the key differences between data science and data analysis, gaining a clear understanding of their unique roles, skills, and objectives. Through comparison and real-world examples, learners will be able to distinguish these fields and identify which approach suits specific data-related challenges. By the end, students will have a solid foundation to navigate and apply both disciplines effectively.

In this lesson, students will explore the fundamentals of data analytics and understand the importance of using a structured framework to manage data projects effectively. They will learn about the key stages involved in data analysis, including collection, cleaning, and interpretation, with a focus on the OSEMN framework as a practical tool for organizing and executing data analytics tasks. By the end of the lesson, students will be equipped to approach data projects systematically, ensuring accuracy, efficiency, and successful outcomes.

In this lesson, students will explore the OSEMN Framework, a foundational model for understanding the data science workflow. They will learn how to effectively Collect, Explore, Sample, Model, and Interpret data to solve real-world problems, gaining essential skills to navigate each stage of a successful data science project. By the end, learners will be equipped to apply the framework to organize and execute data analysis tasks efficiently.

In this lesson, students will learn how to effectively set and evaluate goals using SMART criteria, ensuring objectives are clear and achievable. They will also explore the role of KPIs in tracking progress, understanding how to select primary and secondary indicators to measure success accurately in various contexts. By the end, learners will be equipped to use these strategies for data-driven decision-making and goal achievement.

In this lesson, students will learn how to apply the OSEMN framework—Obtain, Scrub, Explore, Model, and iNterpret—to a real-world marketing campaign for a flower shop. Through a practical case study, they will develop skills in gathering and cleaning data, exploring trends, building predictive models, and interpreting results to make data-driven marketing decisions. By the end, students will understand how to systematically analyze data to optimize marketing strategies and achieve specific business goals.

In this lesson, students will explore a variety of free data sources, learning how to access and evaluate datasets from platforms like Google Dataset Search, the U.S. Census Bureau, Pew Research Center, Eurostat, OECD, and Kaggle. They will gain practical skills in navigating these resources to gather reliable information for analysis and insights, empowering them to simplify data access for their projects and research. By the end, students will be able to efficiently find and utilize diverse public datasets to support their data-driven decision-making.

In this lesson, students will learn the essential steps for cleaning and preparing their datasets for analysis, including identifying and removing duplicates, ensuring consistent formatting, and addressing missing or incorrect data. By mastering this checklist, learners will be equipped to improve data quality and ensure accurate, reliable analysis results.

In this lesson, students will learn how to craft compelling data stories through slide presentations by effectively interpreting and communicating insights. They will explore key components of data storytelling, including selecting appropriate visuals, structuring presentations to emphasize context and relevance, and translating complex data into actionable recommendations for diverse audiences. By the end, learners will be equipped to turn raw data into clear, engaging narratives that inform and influence decision-making.

In this lesson, students will analyze how access to affordable childcare impacts women's participation in the workforce and the gender pay gap. Through data-driven exploration, they will evaluate hypotheses about the effects of childcare costs on employment rates and wage disparities, gaining insights into the role of data analytics in addressing gender inequality and supporting economic growth.

In this lesson, students will explore how access to childcare influences women's participation in the workforce and impacts the gender pay gap. Through analyzing research findings and data, learners will understand the relationship between unpaid care work, formal childcare, and economic gender disparities, gaining insights into potential strategies for promoting gender equality in the workplace.

In this lesson, you will learn how to interpret data effectively and craft compelling narratives from your insights. You'll also gain practical experience applying a structured framework to real-world analytics challenges, empowering you to turn data into actionable strategies and confidently pursue your career in data analytics.

Discover the fundamentals of data analytics, including essential techniques for data cleaning, exploration, and validation. Through hands-on application of the OSEMN framework, students will learn to manage analytics projects from start to finish and develop skills in interpreting and effectively communicating data-driven insights. This course equips learners with the core knowledge and communication skills necessary to excel as data analysts.

In this lesson, students will explore the fundamentals of artificial intelligence and delve into the exciting world of generative AI, learning how machines can create original content like text, images, and music. They will understand recent advancements, practical applications, and the tools used in this innovative field, gaining insights into how AI is transforming creativity and problem-solving across industries.

In this lesson, students will explore the revolutionary capabilities of generative AI technologies across text, images, audio, and video creation. They will learn how these tools enhance content development, understand their practical applications, and critically consider the ethical implications involved. By the end, students will grasp how generative AI is transforming creative and communication processes in various industries.

In this lesson, students will explore how Generative AI is transforming business innovation by enhancing creativity, automating content, and optimizing operations. They will examine real-world applications across industries, learn about customizing GenAI tools to meet specific business needs, and understand how to leverage this powerful technology responsibly to drive competitive advantage.

In this lesson, students will explore how Generative AI revolutionizes the data analytics process by enhancing each stage of the OSEMN framework—from creating synthetic data and automating data cleaning to uncovering hidden patterns and aiding in model development. They will learn how GenAI generates insights, improves data quality, and makes complex analyses more understandable, equipping them with cutting-edge skills to transform data analysis across various applications.

In this lesson, students will explore how generative AI revolutionizes data collection and cleaning by creating realistic synthetic datasets and automating error correction. They will learn the importance of high-quality data for accurate analytics and discover practical applications of GenAI in enhancing data accuracy, privacy, and availability for better decision-making. Ultimately, learners will understand how to leverage GenAI tools to improve the foundation of their data analytics processes.

In this lesson, students will explore how Generative AI is transforming data analytics in leading companies by enabling the creation of synthetic data, automating data cleaning, and enhancing predictive modeling. They will learn practical applications of GenAI, such as improving fraud detection, optimizing pricing, and personalizing recommendations, to understand its powerful role in revolutionizing data-driven decision-making.

In this lesson, students will explore how Generative AI is revolutionizing data analytics in top companies by automating data synthesis, cleaning, and predictive modeling. They will learn how leading organizations leverage GenAI to create synthetic data, enhance data quality, and improve predictive insights, ultimately transforming decision-making and operational efficiency.