Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

In this 1-hour long project-based course, you will be able to identify and fully comprehend the basics of the MYSQL workbench and create a new connection to the local server. you will also learn how to create a new database and drop it, create new tables, and delete them. Moreover, You will be able to rename columns of a table, connect tables with each other, and add data to tables. And finally, you will learn how to add columns and apply some features professionally on these columns using some keywords such as PRIMARY KEY, FOREIGN KEY, NOT NULL, AUTO_INCREMENT, and DISTINCT and update the tables with new data. SQL is used by all the big names in tech like Netflix or Airbnb. If you target Google, Facebook, or Amazon, they, of course, have their database systems. But SQL will be there too to query and analyze the data. This guided project is for beginners in the field of data management data modeling and databases. It provides you with the basics of creating the whole database. It equips you with knowledge of the first steps in modeling. 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 course is the seventh course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R. This course will also cover the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, you will: - Examine the benefits of using the R programming language. - Discover how to use RStudio to apply R to your analysis. - Explore the fundamental concepts associated with programming in R. - Explore the contents and components of R packages including the Tidyverse package. - Gain an understanding of dataframes and their use in R. - Discover the options for generating visualizations in R. - Learn about R Markdown for documenting R programming.

Google Sheets is a robust, cloud-based application that empowers you to create sophisticated spreadsheets. Whether you are working at your desk—or from your smartphone or tablet on-the-go—Google Sheets helps you organize, analyze, and share your most important data. In this course for Sheets users, you’ll learn how to make your own supercharged spreadsheets, incorporating powerful functions and visualizations to accelerate your data analysis and share meaningful insights with your team. Follow along with exercises and a companion spreadsheet to practice new skills as you encounter them. About the Instructor Malia is a tech professional based in Los Angeles who uses Google Workspace and Google Sheets everyday to manage projects, collaborate with remote teams, and make data-driven decisions.

In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose. Before you attempt this project, you should be familiar with programming in Python. You should also have a theoretical understanding of Convolutional Neural Networks, and optimization techniques such as gradient descent. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional Neural Networks. 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.

Have you ever wondered how SQL queries work in R? Have you ever thought about whether it is possible to use or write SQL queries in R? Then, you are in the right place. This project-based course Manipulate R data frames using SQL in RStudio is for people who are learning R and who may be well-versed in SQL or even for experienced R programmers who seek useful ways for data manipulation in R. It is for people who are interested in advancing their knowledge and skills in using SQL in R. In this project, we will write very nice queries to manipulate the gapminder and UCBAdmissions R data frames using the sqldf package in RStudio. This project is extremely important for you as an R and SQL user. You will understand how the SQL SELECT statement works to interact with R to get the desired result. We will start this hands-on project by installing and importing the required packages and data sets for this project. Be rest assured that you will learn a ton of good work here. By the end of this 2-hour-long project, you will be able to use SELECT statements together with the WHERE clause to set conditions on data retrieved from R data frames. Also, you will understand how to use the WHERE clause together with other SQL operators like AND, OR, IN, NOT IN, BETWEEN- AND, NOT BETWEEN- AND, and other comparison operators to retrieve data from the data frames. Going forward, we will consider how to use wildcard characters with the LIKE and NOT LIKE operators for pattern matching. By extension, we will learn how to create data summaries or aggregates using SQL aggregate functions. In this project, we will move systematically by first introducing the SELECT statements using simple examples. Then, we will write slightly complex queries to solve some SQL challenges. Therefore, to complete this project, it is required that you have prior experience with using SQL and R. I recommend that you should complete the projects titled: “Getting Started with R” and “Querying Databases using SQL SELECT statements” before you take this current project. These introductory projects in using SQL and R will provide every necessary foundation to complete this current project. However, if you are comfortable writing queries in SQL, please join me on this wonderful ride! Let’s get our hands dirty!

In an age now driven by "big data", we need to cut through the noise and present key information in a way that can be quickly consumed and acted upon making data visualization an increasingly important skill. Visualizations need to not only present data in an easy to understand and attractive way, but they must also provide context for the data, tell a story, achieving that fine balance between form and function. Excel has many rivals in this space, but it is still an excellent choice, particularly if it's where your data resides. It offers a wealth of tools for creating visualizations other than charts and the chart options available are constantly increasing and improving, so the newer versions now include waterfall charts, sunburst diagrams and even map charts. But what sets Excel apart is its flexibility, it gives us total creative control over our designs so if needed we could produce our own animated custom chart to tell the right story for our data. Over five weeks we will explore Excel's rich selection of visualization tools using practical case studies as seen through the eyes of Rohan, an environmental analyst. Rohan is required to produce visualizations that will show trends, forecasts, breakdowns and comparisons for a large variety of environmental data sets. As well as utilising the usual chart types he wants to use conditional formats, sparklines, specialised charts and even create his own animated charts and infographics. In some cases, he will also need to prepare the data using pivot tables to drill down and answer very specific questions. We are going to help him achieve all this and present our finished visualizations in attractive reports and dashboards that use tools like slicers and macros for automation and interactivity. These are the topics we will cover: Week 1: Dynamic visualizations with conditional formatting, custom number formatting, sparklines and macros Week 2: Charting techniques for telling the right story Week 3: Creating specialised and custom charts Week 4: Summarising and filtering data with pivot tables and pivot charts Week 5: Creating interactive dashboards in Excel This is the second course in our Specialization on Data Analytics and Visualization. The first course: Excel Fundamentals for Data Analysis, covers data preparation and cleaning but also teaches some of the prerequisites for this course like tables and named ranges as well as text, lookup and logical functions. To get the most out of this course we would recommend you do the first course or have experience with these topics. In this course we focus on Data Visualization in Excel, join us for this exciting journey.

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

In this 1-hour long project-based course, you will learn how to pull down Stock Data using the R quantmod package. You will also learn how to perform analytics and pass financial risk functions to the data. 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 Performing Confirmatory Data Analysis in R. In this project, you will learn how to perform extensive confirmatory data analysis, which is similar to performing inferential statistics in R. By the end of this 2-hour long project, you will understand how to perform chi-square tests, which includes, the goodness of fit test, test for independence, and test for homogeneity. Also, you will learn how to calculate correlation for numeric variables and perform regression analysis. Also, you will learn how to interpret the results of a test and make viable decisions. By extension, you will learn how to explore some built-in R datasets to perform the different tests. Note, you do not need to be a data scientist or statistical analyst to be successful in this guided project, just a familiarity with basic statistics and performing hypothesis test in R suffice for this project. A fundamental prerequisite is having a good understanding of the theory of hypothesis test. So, I recommend that you should take the Hypothesis Testing in R project before taking this project.