What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values. You will examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems while also learning best practices for responsible data management, understanding the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten." This course will help you answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair. Data scientists and anyone beginning to use or expand their use of data will benefit from this course. No particular previous knowledge needed.

We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best? In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system. We use simple tools: starting with fast calculations by hand, then we show how to use FREE software. The course comes with slides, transcripts of all lectures, subtitles (English, Spanish and Portuguese; some Chinese and French), videos, audio files, source code, and a free textbook. You get to keep all of it, all freely downloadable. This course is for anyone working in a company, or wanting to make changes to their life, their community, their neighbourhood. You don't need to be a statistician or scientist! There's something for everyone in here. ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ Over 1500 people have completed this online course. What have prior students said about this course? "This definitely is one of the most fruitful courses I have participated at Coursera, considering the takeaways and implementations! And so far I finished 12 [courses]." "Excelente curso, flexible y con suficiente material didáctico fácilmente digerible y cómodo. No importa si se tiene pocas bases matemáticas o estadísticas, el curso proporciona casi toda explicación necesaria para un entendimiento alto." "I wish I had enrolled in your course years ago -- it would have saved us a lot of time in optimizing experimental conditions." Jason Eriksen, 3 Jan 2017 "Interesting and developing both analytical and creative thinking. The lecturer took care to bring lots of real live examples which are fun to analyze." 20 February 2016. "... love your style of presentation, and the examples you took from everyday life to explain things. It is very difficult to make such a mathematical course accessible and comprehensible to this wide a variety of people!" ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the learning purposes. By the end of this project, you will be able to build the linear regression model using Pyspark ML to predict admission chances.You will also be able to setup and work with Pyspark on the Google Colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of Linear Regression algorithm. 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 introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.

In this 1-hour long project-based course, you will create an end-to-end classification model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can accurately predict whether a teacher's project proposal was accepted, based on the data they provided in their application. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for classification. Here are the main steps you will go through: frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model. This guided project is for seasoned Data Scientists who want to build a accelerate the efficiency in building POC and experiments by using a low-code library. It is also for Citizen data Scientists (professionals working with data) by using the low-code library PyCaret to add machine learning models to the analytics toolkit In order to be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning 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 the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving Introduction Week 2: Model Serving Patterns and Infrastructures Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging

Supply Chain Network Optimization

Welcome to the “Building a unique NLP project: 1984 book vs 1984 album” guided project. This project is for anyone interested in exploring fun and interactive Natural Language Processing (NLP) projects. Inspired by the cultural phenomenon, Versus, in this project we’re going to be leveraging the NLP to compare 1984, the dystopian social science fiction novel by the English novelist George Orwell and 1984, the sixth studio album by American rock band Van Halen. In this project, we’ll explore the NLP techniques of: 1. summarizing text 2. sentiment analysis 3. word clouds. At the end of this project, learners will be able to demonstrate a beginner's understanding of building NLP projects.

Spreadsheet software remains one of the most ubiquitous pieces of software used in workplaces across the world. Learning to confidently operate this software means adding a highly valuable asset to your employability portfolio. In the United States alone, millions of job advertisements requiring Excel skills are posted every day. Research by Burning Glass Technologies and Capital One shows that digitals skills lead to higher income and better employment opportunities at a time when digital skills job are growing much faster than non-digital jobs. In this second course of our Excel specialization Excel Skills for Business you will build on the strong foundations of the Essentials course. Intermediate Skills I will expand your Excel knowledge to new horizons. You are going to discover a whole range of skills and techniques that will become a standard component of your everyday use of Excel. In this course, you will build a solid layer of more advanced skills so you can manage large datasets and create meaningful reports. These key techniques and tools will allow you to add a sophisticated layer of automation and efficiency to your everyday tasks in Excel. Once again, we have brought together a great teaching team that will be with you every step of the way. Prashan and Nicky will guide you through each week (and I am even going to make a guest appearance in Week 5 to help you learn about my favourite tool in Excel - shh, no spoilers!). Work through each new challenge step-by-step and in no time you will surprise yourself by how far you have come. This time around, we are going to follow Uma's trials and tribulations as she is trying to find her feet in a new position in the fictitious company PushPin. For those of you who have done the Essentials course, you will already be familiar with the company. Working through her challenges which are all too common ones that we encounter everyday, will help you to more easily relate to the skills and techniques learned in each week and apply them to familiar and new contexts.

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.