Throughout this guided project we are going to create a simple Parallel Coordinates Plot (PCP) using d3 js. PCP is one of the most common data visualization techniques used to visualize high-dimensional datasets. In this guided project you will create a simple PCP step by step. We will also cover some important topics in data visualization such as Linear and Ordinal scaling to best visualize our data. Having the knowledge of javascript programming language and the basics of d3 js are the two most important prerequisites to get the most out of this guided project.

Welcome to "Simulation of Drum-Buffer-Rope Production Control Using R-Simmer". This is a project-based course which should take about 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, you will gain introductiory knowledge of Drum-Buffer-Rope Production Control, Discrete Event Simulation, be able to use R Studio and Simmer library, create statistical variables required for simulation, define process trajectory, define and assign resources, define arrivals (eg. incoming customers / work units), run simulation in R, store results in data frames, plot charts and interpret the results.

People analytics is a data-driven approach to managing people at work. For the first time in history, business leaders can make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this brand new course, three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. They’ll explain how data and sophisticated analysis is brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration. This course is an introduction to the theory of people analytics, and is not intended to prepare learners to perform complex talent management data analysis. By the end of this course, you’ll understand how and when hard data is used to make soft-skill decisions about hiring and talent development, so that you can position yourself as a strategic partner in your company’s talent management decisions. This course is intended to introduced you to Organizations flourish when the people who work in them flourish. Analytics can help make both happen. This course in People Analytics is designed to help you flourish in your career, too.

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.

In this 2-hour long project-based course, you will learn how to predict mRNA Vaccine Degradation Rates at various positions of the molecule. Our model will predict likely degradation rates at each base of an RNA molecule which will be useful to develop models and design rules for RNA degradation. We will look at how to build a Bidirectional Gated Recurrent Units Neural Network which can predict the degradation for multiple scenarios at each of the base. We will cover how to train the model and evaluate on a test set. We will then finally make predictions using the trained model and compare it with the original degradation rates. 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 one-hour project, you will understand how Azure Video Analyzer works for video analysis and what information is generated for video indexing. You will learn how to deploy a simple Web Application that uses the Azure Video Analyzer SDK to upload and index videos to detect faces, labels in scenes, identify celebrities, and more. Azure Video Analyzer is one of the most popular Artificial Intelligence services in the Azure ecosystem and is favored to analyze images and videos with confidence and low costs. Once you're done with this project, you will be able to use Azure Video Analyzer to analyze and index your videos in just a few steps.

By the end of this guided project, learners will have created an interactive graph that applies principles of data visualization to tell a story using basic sales data. This project will illustrate some of the basic features of Tableau software and allow learners to obtain a good introduction to using the open source software, Tableau Public. In this project, learners will use sample data as a building block to create a shareable interactive data visualization chart. This skill is helpful for anyone interested in learning more about how to begin working with data to tell a story, highlight change over time, see and understand data, or make well-informed data driven decisions. 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 designed for the Python programmer who wants to develop the foundations of Calculus to help solve challenging problems as well as the student of mathematics looking to learn the theory and numerical techniques of applied calculus implemented in Python. By the end of this course, you will have learned how to apply essential calculus concepts to develop robust Python applications that solve a variety of real-world challenges. Video lectures, readings, worked examples, assessments, and Python code are all provided in the course. These are used to illustrate techniques to solve equations, work with functions, and compute and apply derivatives and integrals. If you are interested in starting to develop concepts in fields such as applied math, data science, cybersecurity, or artificial intelligence, or just need a refresher of calculus or coding in Python, then this course is right for you.

In this one hour long project-based course, you will tackle a real-world problem in computer vision called segmentation. Segmentation means taking an image and partitioning it into different regions that capture the different elements of interest in the scene. We will tackle this problem using an unsupervised learning technique called K-means. By the end of this project, you will have segmented an image with unsupervised learning, using code you will write in Python.

Welcome to this project-based course, SQL Date Time Functions. In this project, you will learn how to use SQL Date Time Functions to manipulate tables with data datatypes in a database. By the end of this 2-and-a-half-hour-long project, you will be able to use different Date Time Functions to retrieve the desired result from a database. In this project, you will learn how to use SQL Date Time Functions like CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP, AGE, EXTRACT, TO_CHAR, TO_DATE to manipulate date-like data in the employees table. In this project, we will move systematically by first introducing the functions using a simple example. Then, we will write more complex queries using the Date Time Functions in real-life applications. Also, you will learn how to convert a date to a string and vice versa. Also, for this hands-on project, we will use PostgreSQL as our preferred database management system (DBMS). Therefore, to complete this project, it is required that you have prior experience with using PostgreSQL. Similarly, this project is an advanced SQL concept; so, a good foundation for writing SQL queries is vital to complete this project. If you are not familiar with writing queries in SQL and SQL joins and want to learn these concepts, start with my previous guided projects titled “Querying Databases using SQL SELECT statement," “Performing Data Aggregation using SQL Aggregate Functions,” and “Mastering SQL Joins.” I taught these guided projects using PostgreSQL. So, taking these projects will give the needed requisite to complete this SQL Date Time Functions project. However, if you are comfortable writing queries in PostgreSQL, please join me on this wonderful ride! Let’s get our hands dirty!