In this course you will learn to write advanced SQL (Structured Query Language) retrieval queries using SQLiteStudio. Retrieving data from a relational database is one of the primary methods used by application and web developers to display data and populate web pages. Since a database can be made up of a complex combination of relational tables, retrieving that data can be challenging. You can meet those challenges by gaining experience with some of the more advanced SQL coding techniques. Through hands-on practice you will write SQL code to use functions and grouping, sub queries, calculated fields, and conditional expressions. In addition, you will experiment with alternative methods of joining tables for data retrieval. 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.

In this project, you will learn how to analyze data and identify trends using a variety of tools in Microsoft Excel. Conditional formatting and charts are two tools that focus on highlighting and representing data in a visual form. With conditional formatting, you can define rules to highlight cells using a range of color scales and icons and to help you analyze data and identify trends or outliers. You will then use PivotTables to create summaries of the data that focuses on specific relationships which you will represent as a line chart and column chart. Both conditional formatting and charts are two useful ways of visually analyzing data and exploring trends.

Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Welcome to the specialization course Relational Database Systems. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a very simple way how several types of information systems and databases are available to solve different problems and needs of the companies. Objective: A learner will be able to design, test, and implement analytical, transactional or NoSQL database systems according to business requirements by programming reliable, scalable and maintainable applications and resources using SQL and Hadoop ecosystem. Programming languages: For course 1 you will use the MYSQL language. Software to download: MySQL Workbench In case you have a Mac / IOS operating system you will need to use a virtual Machine (VirtualBox, Vmware).

In this 2-hour long project-based course, you will learn how to create effective charts and a dynamic dashboard to visualize data sets. You will be able to work with vlookups, pivot tables and basic formulas and be able to create dynamic charts, sparklines, and a robust, dynamic dashboard to present the data. By the end of the project you will be able to: - Understand the terminologies of spreadsheets - Work with basic formulas in Google Sheets - Create 8 Basic Charts for visualizing data - Generate Dynamic Charts from a dropdown list - Generate Sparklines to represent data - Build a dashboard and introduce Basic and Advanced Charts - Use Slicers to filter data and create a robust and dynamic dashboard Note: If you don't have a Google account, you will need to create one to be able to complete the content.

The Capstone project is an individual assignment. Participants decide the theme they want to explore and define the issue they want to solve. Their “playing field” should provide data from various sectors (such as farming and nutrition, culture, economy and employment, Education & Research, International & Europe, Housing, Sustainable, Development & Energies, Health & Social, Society, Territories & Transport). Participants are encouraged to mix the different fields and leverage the existing information with other (properly sourced) open data sets. Deliverable 1 is the preliminary preparation and problem qualification step. The objectives is to define the what, why & how. What issue do we want to solve? Why does it promise value for public authorities, companies, citizens? How do we want to explore the provided data? For Deliverable 2, the participant needs to present the intermediary outputs and adjustments to the analysis framework. The objectives is to confirm the how and the relevancy of the first results. Finally, with Deliverable 3, the participant needs to present the final outputs and the value case. The objective is to confirm the why. Why will it create value for public authorities, companies, and citizens. Assessment and grading: the participants will present their results to their peers on a regular basis. An evaluation framework will be provided for the participants to assess the quality of each other’s deliverables.

By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups. The T-Test is well known in the field of statistics. It is used to test a hypothesis using a set of data sampled from the population. To perform the T-Test, the population sample size, the mean, or average, of each population, and the standard deviation are all required. These will all be calculated in this project. 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.

Welcome to this project-based course Introduction to SQL Window Functions. This is a hands-on project that introduces SQL users to the world of window functions. In this project, you will learn how to explore and query the project-db database extensively. We will start this hands-on project by retrieving the data in the table in the database. By the end of this 2-hour-and-a-half-long project, you will be able to use different window functions to retrieve the desired result from a database. In this project, you will learn how to use SQL window functions like ROW_NUMBER(), LEAD(), LAG(), and FIRST_VALUE() to manipulate data in the project-db database. These window functions will be used together with the OVER() clause to query this database.

This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Google Data Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources. Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you. After completing this course, enroll in the Achieving Advanced Insights with BigQuery course. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.