In this 1 hour long project, you will build an attractive and eye-catching HR dashboard using Power BI. We will begin this guided project by importing data & creating an employee demographics page that gives us the overall demographic outlook of the organization. We will then create pie charts and doughnut charts to visualize gender & racial diversity. In the final tasks, we will create an employee detail page that will provide you with all the important information about any employee with just a click. We will also explore buttons, themes, slicers & filters to make the dashboard more interactive & useful. By the end of this course, you will be confident in creating beautiful HR dashboards that you can use for your personal or organizational purpose.

Congratulations you've made it to Part 2 of the DPLYR series! In a moment you will be taken to Rhyme where a Virtual Machine with R, R Studio and DPLYR awaits. Once there you will begin the Project where you will be introduced to the Rhyme Interface and subsequently learn how to use the DPLYR verbs in a more advanced way by building on the foundation learned in the previous course. Come in, get experience using R and learn new ways to use the dplyr functions. By the end of this course, you will be able to: To practice the basic dplyr functions and how they are used To learn advanced features of the dplyr verb 'mutate' To implement the verb mutate over a data set in place of a 'for loop' To continue thinking in dplyr verb phrases (ex. filter, aggregate, and transform data)

Talend Certification exams measure candidates’ skills to ensure that they have the knowledge to successfully implement quality projects. It is recommended to have at least 6 months of experience using Talend products and general knowledge of data integration architecture and advanced features before preparing for a Talend certification. At the end of this preparation course, you can take the graded assessments in order to obtain the certificate of course completion. This includes practice test questions that provide a sample of question types, format, and content you might encounter during the Talend Data Integration v7 Certified Developer exam. Please note this is not the actual certification. When you are ready to register for the actual exam, connect to https://www.webassessor.com/talend to register. Preparing for a certification exam can be both exciting and terrifying, but don't worry! This preparation course will introduce the topics you should invest in when preparing for the certification exam.

In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework. You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, recurrent neural networks, and optimization algorithms like gradient descent but want to understand how to use the Tensorflow to start performing natural language processing tasks like text classification. You should also have some basic familiarity with TensorFlow. 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.

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the DP-203 Microsoft Azure Data Fundamentals certification exam. You will refresh your knowledge of how to use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. You will test your knowledge in a practice exam​ mapped to all the main topics covered in the DP-203 exam, ensuring you’re well prepared for certification success. You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the DP-203 proctored exam. By the end of this course, you will be ready to sign-up for and take the DP-203 exam.​ This is the last course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.

Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.

After finishing this project, you will have learned some basic rules about data visualization and can apply them whenever you create charts. In present times, one can find data visualization in a wide range of fields. Businesses show graphs to report on revenue, police departments create maps of crimes in their jurisdiction, and on the website for the city hall, you can likely find visual comparisons of people who moved to the city and those who left the city. For this reason, it is important for a lot of people to know the basics of data visualization.

In this 1-hour long project-based course, you will learn how to analyze a complete viral genome using geometrical methods (skews and Z-curve), 2D- and 3D-plotting in Python, and how to use some important Python libraries (like Tkinter, Matplotlib, and NumPy) helping you accomplish this. You will also learn about the genomes of some viruses including, Corona, SARS, HIV, Zika, Nidovirous, and rubella viruses.

This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.