[MUSIC] Hello and welcome to generate insights. One of Amazon's most successful and profitable initiatives is their investment in data driven decision making, specifically their product recommendation engine. This artifact is used across their e-commerce platform to entice customers into purchasing specific products. Based upon the data Amazon has obtained either directly from them, or through the evaluation of their interaction with Amazon and its numerous offerings over time. Data using the engine includes demographic data of the customer, product viewing and search history, and product related characteristics, such as a product's inherent association with other products offered on the platform. Amazon's product recommendation engine is an example of an insight generation tool, one which leverages an underlying data asset infrastructure to provide insights for you as a potential Amazon customer to make decisions upon. The success of this engine is attributed to whether you decide to purchase the recommended product or not. And ultimately if you are satisfied with your decision. Similarly, organizations only succeed if their leaders make the right decisions. And these can only be made if decision makers are provided access to high quality insights derived from the analysis of an organization's data assets. As a result, organizations now consider data as a crucial commodity, one which can help them to conduct their business efficiently and economically. This commodity requires a new breed of organizational managers, those who are confident in using data analytics to solve organizational problems. Accordingly, organizations now consider data literacy as a critical management skill. And actively seek out those who are able to use data analysis tools, lead teams of specialized data practitioners, and can effectively communicate insights to stakeholders and senior management. In this course, we aim to provide you with the knowledge and skills required to help solve your organization's current and future problems with insights produced as a result of data analytics. This six week course is a mix of theory and practical lessons. Our theoretical lessons will encompass the fundamentals of data and databases, tools used for data analytics, basic and slightly advanced statistical methods, data visualization theory. And tips on how to engage stakeholders during the course of a data analytics project. Our practical components will make use of a powerful data analysis platform, SAS Viya. We will teach you how to use this platform to produce insights for realistic organizational use cases, as well as how to handle raw data and apply the statistical theory we will discuss. This course is not designed to teach you how to be a programmer or mathematician. Our goal is to provide you with the resources needed to leverage data assets to solve organizational problems in a management function. That means showing you how to perform statistical calculations on large data sets to provide stakeholders with immediate guidance on a problem, or providing you with sufficient contextual knowledge on statistical methods to enable you to engage and lead a team of specialized data practitioners. Just remember as you progress through the course, don't be put off by the math and computing we will discuss. Instead think how you can apply the theory and skills taught to further your management capabilities, and engage in discussions and decision making you may have lacked the confidence to do so in the past. Besides, the software we use is straightforward and easy to use, and we will be here to guide you along the way. So let's see what we can expect from each of the six weeks. In the first week, I'll give you an overview of the course, emphasize the importance for data in today's organizations. introduce a data value chain concept, talk about tools used for data analytics, and introduce some common data related jobs and careers in the market. I will also introduce you to SAS Viya, a data analytics platform we'll be using throughout the course for data manipulation and insight generation. In the second week, we'll focus on data and its structure. We will spend time introducing different types of variables, and we'll also speak about basic statistical theory, including the measures of central tendency and dispersion. In this week we'll begin to use SAS Viya to create reports and visualizations using basic statistical theory we will explore. In the third week, we'll continue our introduction of statistical concepts by exploring histograms, distributions, and the empirical rule. We will also outline covariance and correlation, measures exploring the interrelationships between different variables. In the fourth week, we'll shift our focus from statistics to visualizations. We will begin by highlighting the importance of data visualization by reviewing Anscombe's quartet, after which we will explore data cleaning methods in SAS Viya. We will then explore bar and pie charts and discuss some key tips for implementing data visualizations in data analytics projects. In week five, we'll continue to explore data visualizations with the introduction of the multiple variable correlation matrix, and bar and line chart. We will also introduce filtering methods on SAS Viya, as well as key performance indicators and how to embed these into SAS Viya reports. In this week, we will also introduce dashboard theory through an analysis of the work of the prominent information systems academic Steven Few. In the last week, I'll introduce predictive insight generation using regression analysis. We will also explore time series plots and forecasting, ultimately learning how to leverage the powerful capabilities of SAS Viya to predict the future. Throughout the course you'll note on Coursera I've provided rating material, activities, and quizzes for each week. These are designed to reinforce your learning and provide you with opportunities to expand on the course content. You will also have access to the discussion forum, where I will encourage you to contribute your thoughts on the role of data in organizational success. And to demonstrate your learning as we progress through the course. That's it for now. Good luck and I hope you enjoy the course. [MUSIC]
