The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them. To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. ________________________________________ WEEK 1 Module 1: Introduction to Spreadsheets In this module, you will be introduced to the use of Excel spreadsheets and various basic data functions of Excel. Topics covered include: • Reading data into Excel using various formats • Basic functions in Excel, arithmetic as well as various logical functions • Formatting rows and columns • Using formulas in Excel and their copy and paste using absolute and relative referencing ________________________________________ WEEK 2 Module 2: Spreadsheet Functions to Organize Data This module introduces various Excel functions to organize and query data. Learners are introduced to the IF, nested IF, VLOOKUP and the HLOOKUP functions of Excel. Topics covered include: • IF and the nested IF functions • VLOOKUP and HLOOKUP • The RANDBETWEEN function ________________________________________ WEEK 3 Module 3: Introduction to Filtering, Pivot Tables, and Charts This module introduces various data filtering capabilities of Excel. You’ll learn how to set filters in data to selectively access data. A very powerful data summarizing tool, the Pivot Table, is also explained and we begin to introduce the charting feature of Excel. Topics covered include: • VLOOKUP across worksheets • Data filtering in Excel • Use of Pivot tables with categorical as well as numerical data • Introduction to the charting capability of Excel ________________________________________ WEEK 4 Module 4: Advanced Graphing and Charting This module explores various advanced graphing and charting techniques available in Excel. Starting with various line, bar and pie charts we introduce pivot charts, scatter plots and histograms. You will get to understand these various charts and get to build them on your own. Topics covered include • Line, Bar and Pie charts • Pivot charts • Scatter plots • Histograms

Welcome to the Preparing for the SAS Viya Programming Certification Exam course. This is the third and final course in the Coursera SAS Programmer specialization. You will apply what you have learned in the first two courses by writing code to execute in SAS Cloud Analytic Services and practicing for the SAS certification exams. This is an advanced course, intended for learners who have completed the first two courses in the Coursera SAS Programmer specialization: SAS Programming for Distributed Computing in SAS Viya and CASL Programming for Distributed Computing in SAS Viya. By the end of the course, you be prepared to take either of these SAS credential exams: - SAS® Viya® Programming Associate - SAS® Viya® Programming Specialist

In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.

In this project you will learn how to set up PyCaret for your natural language processing tasks, compare and create models effectively, visualize your models and corpus. All this with just a few lines of code.

This course is organized into two parts presenting the theoretical and practical foundations of geographic information systems (GIS). - Together theses courses constitute an introduction to GIS and require no prior knowledge. - By following this introduction to GIS you will quickly acquire the basic knowledge required to create spatial databases and produce high-quality maps and cartographic representations. - This is a practical course and is based on free, open-source software, including QGIS. If you study or work in the fields of land management or the analysis of geographically distributed objects such as land use planning, biology, public health, ecology, or energy, then this course is for you! In this first part of the course, we will focus on the digitization and the storage of geodata. In particular, you will learn: - To characterize spatial objects and/or phenomena (territory modeling) with respect to their position in space (through coordinate systems, projections, and spatial relationships) and according to their intrinsic nature (object/vector mode vs. Image/raster mode); - About the different means used to acquire spatial data; including direct measurement, georeferencing images, digitization, existing data source, etc.); - About the different ways in which geodata can be stored - notably, files and relational databases; - How to use data modeling tools to describe and create a spatial database; - To query and analyze data using SQL, a common data manipulation language. The second part of this course will focus on methods of spatial analysis and geodata representation. In this section, you will learn: - How to describe and quantify the spatial properties of discrete variables, for example through spatial autocorrelation; - To work with continuous variables. In particular, we will look at sampling strategies, how to construct contour lines and isovalue curves, and we will explore different interpolation methods; - To use digital elevation models and create their derivative products (i.e. slope, orientation); - How to evaluate the interaction between different types of geodata through overlay and interaction techniques; - How to create effective maps based around the rules of graphic semiology; - Finally, we will also explore other, increasingly common, forms of spatial representation such as interactive web-mapping and 3D representations. You can find an interactive forum for course participants on our Facebook page: https://www.facebook.com/moocsig

In this 1-hour long project-based course, you will learn exploratory data analysis techniques and create visual methods to analyze trends, patterns, and relationships in the data. By the end of this project, you will have applied EDA on a real-world dataset. This class is for learners who want to use Python for applying data visualization and data analysis, and for learners who are currently taking a basic machine learning course or have already finished a machine learning course and are searching for a practical data visualization and analysis project course. Also, this project provides learners with basic knowledge about exploratory analysis and improves their skills in creating maps which helps them in fulfilling their career goals by adding this project to their portfolios.

In this course you will experience the process of defining, creating, and managing relational database tables using the SQL language. Tables are used as the containers for the data in a database. As such, the structure, or makeup, of each table in a relational database is critical, since it must be designed and created specifically to meet the needs of the data it will contain. The table’s structure indicates which pieces of data are stored in a table, as well as the type and size of each piece of data. Throughout the course, you’ll be exposed to guidelines and rules that database designers use to make sure that the tables will keep the data as safe and accurate as possible. You’ll learn to use SQL code to incorporate the constraints that help the database management enforce those rules. As you work through and complete hands-on tasks, you’ll become familiar with SQLiteStudio, the database management system used in the course. Tables that are well-designed and created correctly improve data integrity--and make data retrieval easier! 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.

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

In this course, you will learn how to find GIS data for your own projects, and how to create a well-designed map that effectively communicates your message. The first section focuses on the basic building blocks of GIS data, so that you know what types of GIS files exist, and the implications of choosing one type over another. Next, we'll discuss metadata (which is information about a data set) so you know how to evaluate a data set before you decide to use it, as well as preparing data by merging and clipping files as needed. We'll then talk about how to take non-GIS data, such as a list of addresses, and convert it into "mappable" data using geocoding. Finally, you'll learn about how to take data that you have found and design a map using cartographic principles. In the course project, you will find your own data and create your own quantitative map. Note: software is not provided for this course.