Career prospects are bright for those qualified to work in healthcare data analytics. Perhaps you work in data analytics, but are considering a move into healthcare where your work can improve people’s quality of life. If so, this course gives you a glimpse into why this work matters, what you’d be doing in this role, and what takes place on the Path to Value where data is gathered from patients at the point of care, moves into data warehouses to be prepared for analysis, then moves along the data pipeline to be transformed into valuable insights that can save lives, reduce costs, to improve healthcare and make it more accessible and affordable. Perhaps you work in healthcare but are considering a transition into a new role. If so, this course will help you see if this career path is one you want to pursue. You’ll get an overview of common data models and their uses. You’ll learn how various systems integrate data, how to ensure clear communication, measure and improve data quality. Data analytics in healthcare serves doctors, clinicians, patients, care providers, and those who carry out the business of improving health outcomes. This course of study will give you a clear picture of data analysis in today’s fast-changing healthcare field and the opportunities it holds for you.

Visualizing data patterns often involves re-arrangement and elimination to determine patterns. For example, in a list of data with yearly rainfall amounts, to quickly determine the years with the most rainfall, the data can be sorted according to rainfall in descending order. A filter could be used to limit the amount of data observed, for example, to only show rainfall amounts greater than an inch. A merge can be used to join two datasets together, for example rainfall and temperature data from two different sources. The ability to sort, merge and filter data has always existed using SQL with database data, now it can be done in application memory space using Python. In this course, you will create an application that reads data from two CSV files. You will learn how to merge, sort, and filter the data to ultimately produce a regression plot to determine a possible correlation between two data sets.

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

In this course, we'll make predictions on product usage and calculate optimal safety stock storage. We'll start with a time series of shoe sales across multiple stores on three different continents. To begin, we'll look for unique insights and other interesting things we can find in the data by performing groupings and comparing products within each store. Then, we'll use a seasonal autoregressive integrated moving average (SARIMA) model to make predictions on future sales. In addition to making predictions, we'll analyze the provided statistics (such as p-score) to judge the viability of using the SARIMA model to make predictions. Then, we'll tune the hyper-parameters of the model to garner better results and higher statistical significance. Finally, we'll make predictions on safety stock by looking to the data for monthly usage predictions and calculating safety stock from the formula involving lead times.

In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. • Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data. • Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions. • Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

By the end of this first course in the Total Data Quality specialization, learners will be able to: 1. Identify the essential differences between designed and gathered data and summarize the key dimensions of the Total Data Quality (TDQ) Framework; 2. Define the three measurement dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; 3. Define the three representation dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; and 4. Describe why data analysis defines an important dimension of the Total Data Quality framework, and summarize potential threats to the overall quality of an analysis plan for designed and/or gathered data. This specialization as a whole aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story. Material features online lectures, videos, demos, project work, readings and discussions. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the second course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at https://www.youtube.com/watch?v=Oi4mmeSWcVc&list=PLQvThJe-IglyYljMrdqwfsDzk56ncfoLx&index=11.

In this 1.5-hour long project-based course I will show you the basic concepts to create data visualizations in D3.js. You will learn how to use SVGs, select, and bind data in order to create a  bar chart. We will be visualizing firecracker injuries in 2019. This data is from the U.S. Consumer Product Safety Commission (CPSC) Fireworks Annual Report. Inspired by recent events this summer we are going to visualize firework injuries in the United States.  Firework injury data can be found at U.S. Consumer Product Safety Commission (CPSC) .