This course is about giving you the tools you need to use data to gain actionable business insight. Nearly everywhere we go and nearly everything we do, from shopping online to typing a text is infused and enhanced with big data, machine learning, and data analytics. To win in business, and to even be a successful participant, you and I need to learn how to master tools that help us take data and turn it into usable business insights. This course will give you necessary and cutting-edge tools to put you in the game. These tools will allow you to leverage statistical techniques and machine learning to understand the relationships and interrelations of the features of your data and to create models to use those features to predict future outcomes. These tools are basic, classic algorithms in each of the major areas of machine learning. Machine learning is a means of using rules or algorithms to teach a machine to learn your data, allowing you to extract actionable information from the data. In this course, we cover the two largest and most used methods of machine learning: supervised learning and unsupervised learning. Supervised learning examines data that has labels or outcomes, such as the amount of sales for a quarter, fraud or no fraud, and loyalty customer or non-loyalty customer. The algorithms we will cover allow you to use your data to examine these outcomes and predict them in the future. We cover the following four supervised learning algorithms: Regression, logistic regression, K-nearest neighbors, and decision trees. Regression allows you to investigate and predict future numerical outcomes such as sales, costs, and gross margin. Logistic regression, K-nearest neighbor, and decision trees are classification algorithms and they allow you to investigate and predict discrete classification outcomes such as hire or fire, success or failure, fraud or no fraud. Unsupervised learning works differently in that it has no labeled outcomes, thus, these algorithms seek to generate labels by learning from the data. We covered two key unsupervised learning algorithms, k-means clustering, and DBSCAN clustering. In each module, we'll use each of these tools to focus on the second half of the analytics workflow. The data analytics workflow consists of the following parts: First, acquiring and maintaining data, second, getting data ready for analysis, we often call this ETL for extracting, transforming, and loading data, third, data exploration, including the steps we take to understand and view our data, we often call this exploratory data analysis or EDA, fourth, data modeling, including predicting future outcomes and inferring relationships from our data to other data and situations, fifth, creating analysis results and business insight, and sixth, visualizing and communicating findings and solutions. In particular, in this course, we'll focus on steps 4 and 5, and we'll use the algorithms to learn and master those steps. For example, in steps 4 and 5, you've acquired data, cleaned it and prepare it for analysis, and visualized and explored it, now, you want to extract information and business insight from the data and predict future outcomes to help you answer business problems. In each module, we will use realistic business data to practice using these tools. Thus, in each module, we will focus on solving business problems. Our goal in this course is to provide you with a solid framework and foundation for how to understand and practice business analytics. Thus, in the future, when you encounter a data analysis task that you have not encountered before, you will be able to slot it into the framework, understand it quickly, and move more rapidly through the process of assimilating the new information that you need to learn. More importantly, you will be able to put your new knowledge to work for you in solving the business problem. We're excited for you to expand your toolkit by learning more about these tools, data prediction and modeling are the foundation for all data analysis. Opening a new data set and understanding it, it's like unlocking a treasure chest or solving a puzzle, it's like peeling an onion and learning layer by layer, it's like opening a matryoshka doll or a nested doll and finding the truth at the center. As you go through these modules, dig in and play with the data. The more you practice, the more you expand your data toolkit and your ability to solve business problems.
