In this project, you will learn the foundation of data analysis with Microsoft Excel using sales data from a sample company. You will learn how to use sorting and filtering tools to reorganize your data and access specific information about your data. You will also learn about the use of functions like IF and VLOOKUP functions to create new data and relate data from different tables. Finally, you will learn how to create PivotTables to summarize and look at comparisons within your data. These skills will help you efficiently perform data analysis on many different types of data and give you the foundation to expand your toolbox as you explore other strategies for data analysis.

This is the fourth course in the Data Warehouse for Business Intelligence specialization. Ideally, the courses should be taken in sequence. Effectively and efficiently mining data is the very center of any modern business’s competitive strategy, and a data warehouse is a core component of this data mining. The ability to quickly look back at early trends and have the accurate data – properly formatted – is essential to good decision making. By enabling this historical overview, a data warehouse allows decision makers to learn from past trends and challenges. In essence, the benefit of a data warehouse is continuous improvement. By the end of the course, you will be able to enhance Conformity And Quality of Data by gaining the knowledge and skills for using data warehouses for business intelligence purposes and for working as a business intelligence developer. You’ll have the opportunity to work with large data sets in a data warehouse environment and will learn the use of MicroStrategy's Online Analytical Processing (OLAP) and Visualization capabilities to create visualizations and dashboards. The course gives an overview of how business intelligence technologies can support decision making across any number of business sectors. These technologies have had a profound impact on corporate strategy, performance, and competitiveness and broadly encompass decision support systems, business intelligence systems, and visual analytics. Modules are organized around the business intelligence concepts, tools, and applications, and the use of data warehouse for business reporting and online analytical processing, for creating visualizations and dashboards, and for business performance management and descriptive analytics. This course is intended for business and computer science university students, IT professionals, program managers, business analysts and anyone with career interests in business intelligence. In order to be successful in this course, you should have either completed Course 3 of the Data Warehousing for Business Intelligence Specialization or have some prior experience with data visualization and document management.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use a newly available ecommerce dataset to run some typical queries that businesses would want to know about their customers’ purchasing habits.

The practice of investment management has been transformed in recent years by computational methods. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods. If you follow along and implement all the lab exercises, you will complete the course with a powerful toolkit that you will be able to use to perform your own analysis and build your own implementations and perhaps even use your newly acquired knowledge to improve on current methods.

Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.

In this one-hour based project you will apply the basics of working with relational databases within the SQL Server Management Studio (SSMS) environment. You will use the tools in SSMS to diagram an existing database and to create and run some SELECT and CREATE TABLE structured query language commands. You will also acquire the basic terminology that applies to all relational databases. By successfully completing the hands-on practices assigned, you will be better prepared for applying these basic concepts in SSMS in SQL Server classes. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

While telling stories with data has been part of the news practice since its earliest days, it is in the midst of a renaissance. Graphics desks which used to be deemed as “the art department,” a subfield outside the work of newsrooms, are becoming a core part of newsrooms’ operation. Those people (they often have various titles: data journalists, news artists, graphic reporters, developers, etc.) who design news graphics are expected to be full-fledged journalists and work closely with reporters and editors. The purpose of this class is to learn how to think about the visual presentation of data, how and why it works, and how to doit the right way. We will learn how to make graphs like The New York Times, Vox, Pew, and FiveThirtyEight. In the end, you can share–embed your beautiful charts in publications, blog posts, and websites. This course assumes you understand basic coding skills, preferably Python. However, we also provide a brief review on Python in Module 1, in case you want to refresh yourself on the basics and perform simple data analysis.

Develop data analysis skills that support public sector decision-makers by performing policy analysis through all phases of the policymaking process. You will learn how to apply data analysis techniques to the core public sector principles of efficiency, effectiveness, and equity. Through authentic case studies and data sets, you will develop analytical skills commonly used to analyze and assess policies and programs, including policy options analysis, microsimulation modeling, and research designs for program and policy evaluation. You will also learn intermediate technical skills, such as Chi-squared tests and contingency tables, comparing samples through t-tests and ANOVA, applying Tukey's honest significant difference to correct for multiple tests, understanding p-values, and visualizing simulations of statistical functions to help answer questions policymakers ask such as “What should we do?” and “Did it work?” In addition, you will practice statistical testing and create ggplot visuals for two real-world datasets using the R programming language. All coursework is completed in RStudio in Coursera without the need to install additional software. This is the third of four courses within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.

The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that integrates your learning across all courses in the specialization. In response to business requirements presented in a case study, you’ll design and build a small data warehouse, create data integration workflows to refresh the warehouse, write SQL statements to support analytical and summary query requirements, and use the MicroStrategy business intelligence platform to create dashboards and visualizations. In the first part of the capstone course, you’ll be introduced to a medium-sized firm, learning about their data warehouse and business intelligence requirements and existing data sources. You’ll first architect a warehouse schema and dimensional model for a small data warehouse. You’ll then create data integration workflows using Pentaho Data Integration to refresh your data warehouse. Next, you’ll write SQL statements for analytical query requirements and create materialized views to support summary data management. For data integration workflows and analytical queries, you can use either Oracle or PostgreSQL. Finally, you will use MicroStrategy OLAP capabilities to gain insights into your data warehouse. In the completed project, you’ll have built a small data warehouse containing a schema design, data integration workflows, analytical queries, materialized views, dashboards and visualizations that you’ll be proud to show to your current and prospective employers.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.