Welcome to Excel Power Tools for Data Analysis. In this four-week course, we introduce Power Query, Power Pivot and Power BI, three power tools for transforming, analysing and presenting data. Excel's ease and flexibility have long made it a tool of choice for doing data analysis, but it does have some inherent limitations: for one, truly "big" data simply does not fit in a spreadsheet and for another, the process of importing and cleaning data can be a repetitive, time-consuming and error-prone. Over the last few years, Microsoft have worked on transforming the end-to-end experience for analysts, and Excel has undergone a major upgrade with the inclusion of Power Query and Power Pivot. In this course, we will learn how to use Power Query to automate the process of importing and preparing data for analysis. We will see how Power Pivot revolutionises the actual analysis process by providing us with an analytical database inside the Excel workbook, capable of storing millions of rows, and a powerful modelling language called DAX which allows us to perform advanced analytics on our data. We will finish off by venturing out of Excel and introducing Power BI, which also uses the Power Query and Power BI architecture but allows us to create stunning interactive reports and dashboards. This is the third course in our Specialization on Data Analytics and Visualization. The previous courses: Excel Fundamentals for Data Analysis and Data Visualization in Excel, cover data preparation, cleaning, visualisation, and creating dashboards. To get the most out of this course we would recommend you do the previous courses or have experience with these topics. In this course we focus on Excel Power Tools, join us for this exciting journey. Please note that Power Query, Power Pivot and Power BI Desktop are only available on the Windows platform, so Mac users will require Bootcamp running Windows or a Virtual machine with a Window O/S. While Power Query is available as an add-in Excel 2010 and 2013, the tools have changed significantly, and this course has only been designed and tested for Excel 2016 and later. For an optimal experience, we recommend Office 365.

In this hands-on project, you will use Keras with TensorFlow as its backend to create a recurrent neural network model and train it to learn to perform addition of simple equations given in string format. You will learn to create synthetic data for this problem as well. By the end of this 2-hour long project, you will have created, trained, and evaluated a sequence to sequence RNN model in Keras. Computers are already pretty good at math, so this may seem like a trivial problem, but it’s not! We will give the model string data rather than numeric data to work with. This means that the model needs to infer the meaning of various characters from a sequence of text input and then learn addition from the given data. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed. Please note that you will need some experience in Python programming, and a theoretical understanding of Neural Networks to be able to finish this project successfully. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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 intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.

In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python, Neural Networks, and the Keras framework. 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 will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox" of methods to deal with uncertainty in your daily life. The course is split in 5 modules. In each module you will first have an easy introduction into the topic, which will serve as a basis to further develop your knowledge about the topic and acquire the "tools" to deal with uncertainty. Additionally, you will have the opportunity to complete 5 exercise sessions to reflect about the content learned in each module and start applying your earned knowledge right away. The topics covered are: "Probability", "Conditional Probability", "Applications", "Random Variables", and "Normal Distribution". You will see how the modules are taught in a lively way, focusing on having an entertaining and useful learning experience! We are looking forward to see you online!

"Fake News" is a word used to mean different things to different people. At its heart, we define "fake news" as any news stories which are false: the article itself is fabricated without verifiable evidence, citations or quotations. Often these stories may be lies and propaganda that is deliberately intended to confuse the viewer, or may be characterized as "click-bait" written for monetary incentives (the writer profits on the number of people who click on the story). In recent years, fake news stories have proliferated via social media, partially because they are so readily and widely spread online. Worse yet, Artificial Intelligence and natural language processing, or NLP, technology is ushering in an era of artificially-generated fake news. Both types of fake news are detectable with the use of NLP and deep learning. In this project, you will learn multiple computational methods of identifying and classifying Fake News. 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.

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks. You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language. The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights. No prior knowledge of R, or programming is required.

Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: model evaluation with ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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 is a self-paced lab that takes place in the Google Cloud console. In this lab, you will explore existing datasets with Data Catalog and mine the table and column metadata for insights.

In this project, we will build a Naïve Bayes Classifier to predict whether a given resume text is flagged or not. Our training data consist of 125 resumes with 33 flagged resumes and 92 non flagged resumes. This project could be practically used to screen resumes in companies.