IBM Cognitive Class
Data Science Tools
Learn about the most popular data science tools, including how to use them and what their features are. ABOUT THIS COURSE In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today. With the tools hosted in the cloud, you will be able to test each tool and follow instructions to run simple code in Python or R. To complete the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio on Cloud and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers. This hands-on course will get you up and running with some of the latest and greatest data science tools. You will Learn: How to use various data science and data visualization tools hosted on Skills Network Labs How to use Jupyter Notebooks including its features and why it's so popular Popular tools used by R Programmers including RStudio IDE IBM Watson Studio including its features and capabilities How to create and share a Jupyter Notebook COURSE SYLLABUS Module 0 - Welcome and Course Introduction Module 1 - Languages of Data Science Module 2 - Data Science Tools Module 3 - Packages, APIs, Data Sets and Models Module 4 - GitHub Module 5 - Jupyter Notebooks and JupyterLab Module 6 - RStudio IDE Module 7 - Watson Studio
GratisBuild Your Own Chatbot
This chatbot course provides a practical introduction that will teach you everything you need to know to plan, build, and deploy your first customer support chatbot. Leveraging the cognitive computing power of IBM Watson Assistant, you will be able to design your own chatbot without the need to write any code. You'll also learn how to quickly deploy your chatbot on WordPress-based sites. If you want to learn how to create virtual assistants — and perhaps create one for your own business — this free chatbot course is for you. This chatbot course provides a practical introduction that will teach you everything you need to know to plan, build, and deploy your first chatbot. Leveraging the cognitive computing power of Watson Assistant, you will be able to design your own chatbot without the need to write any code. You'll also learn how to quickly deploy your chatbot on WordPress-based sites. If you want to learn how to create virtual assistants — and perhaps create one for your own business — this free chatbot course is for you. Course Syllabus Module 1 - Introduction to Chatbots What are chatbots? Chatbots are trending Leader in the Industry Lab 1: Create an instance of Watson Assistant Quizzes Module 2: Working with Intents How chatbots work Understanding Intents Lab 2: Create Dialog Skill and Intents Lab 3: Import Intents Quizzes Module 3: Working with Entities Understanding Entities Lab 4: Create Entities Lab 5: Import and Export Entities Quizzes Module 4: Defining the Dialog Putting it all together Building user-friendly chatbots Lab 6: Implement the Dialog Lab 7: Define Domain-Specific Intents Quizzes Module 5: Deploying your Chatbot Deploying to a WordPress site Lab 8 - Add a preview and retrieve your credentials Lab 9: Deploy your Chatbot Quizzes Module 6: Advanced concepts – Part 1 Working with Context Variables and Slots Lab 10: Explore Context Variables Lab 11: Master Slots Quizzes Module 7: Advanced concepts – Part 2 Understanding Digressions Lab 12: Enable Digressions Lab 13: Get to know the Analytics tab Lab 14: Create your own Chatbot Watson Assistant in the Private Cloud Quizzes
GratisIntroduction to Containers, Kubernetes, and OpenShift V2
This course introduces the core concepts of Containers and Kubernetes and explains how containers differ from virtual machines. It also covers the importance of containers in cloud computing as well as the emerging ecosystem of related technologies such as Docker, Kubernetes, OpenShift, Istio and Knative. ABOUT THIS COURSE Containers and Cloud Native are the most significant invention in IT since the introduction of virtualization. Everyone from a small startup to a large multinational corporation is transitioning to this technology and they are looking for people who have the skills. After completing this course, you will be able to build applications the Cloud Native way and be able to deploy your applications at a scale that will make Google envious. This course introduces you to containers and explains how containers differ from virtual machines. It also covers the importance of containers in cloud computing as well as the emerging ecosystem of related technologies such as Docker, Kubernetes, OpenShift, and Istio. This course is of interest to anyone who wants to be a cloud practitioner and use container skills as developers, architects, system engineers, network specialists and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving and managing cloud solutions. LEARNING OBJECTIVES After completing this course you will be able to: Understand the benefits of containers Build and run a container image Understand Kubernetes architecture Write a YAML deployment file Expose deployment as a service Manage applications with Kubernetes Use ReplicaSets, auto-scaling, rolling updates and service bindings Understand the benefits of OpenShift, Istio and other important tools COURSE SYLLABUS Module 1 Introduction to containers Introduction to Docker Building container images Using container registries Running containers Module 2 Understanding container orchestration Understanding Kubernetes architecture Introduction to Kubernetes objects Using basic Kubernetes objects Using the kubectl command Leveraging Kubernetes Module 3 Using ReplicaSets Using autoscaling Understanding rolling updates Understanding ConfigMaps and secrets Using service bindings Module 4 - The Kubernetes ecosystem The Kubernetes Ecosystem Introduction to Red Hat OpenShift Red Hat OpenShift and Kubernetes Builds Operators Istio
GratisData Visualization with Python
Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general. It is a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends. In this course you will learn how to create interesting graphics and charts and customize them to make them more effective and more pleasing to your audience. About This Course "A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large data sets. Data visualization plays an essential role in the representation of both small and large scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, seaborn, and Folium. Course Syllabus Module 1 - Introduction to Visualization Tools Introduction to Data Visualization Introduction to Matplotlib Basic Plotting with Matplotlib Dataset on Immigration to Canada Line Plots Module 2 - Basic Visualization Tools Area Plots Histograms Bar Charts Module 3 - Specialized Visualization Tools Pie Charts Box Plots Scatter Plots Bubble Plots Module 4 - Advanced Visualization Tools Waffle Charts Word Clouds Seaborn and Regression Plots Module 5 - Creating Maps and Visualizing Geospatial Data Introduction to Folium Maps with Markers Choropleth Maps
GratisIBM Cloud Essentials V3
Get hands-on experience with IBM Cloud, Cloud Foundry, and best practices for agile and test-driven development. ABOUT THIS COURSE This course introduces you to the IBM Cloud. You will learn about the many offerings and services on IBM Cloud that make it the most open and secure public cloud for developers and enterprises. The course begins with an introduction to the IBM Cloud platform which covers topics such as data center locations and configuring identity and access management. You will discover the various Infrastructure-as-a-Service (IaaS) options available on IBM Cloud. Next, you will learn about the deployment options on IBM Cloud; this includes topics such as Containers, Kubernetes, and OpenShift. You will also become familiar with IBM Cloud services such as Databases, Artificial Intelligence and Watson, Blockchain, Internet of Things, and many others. In addition to videos, you will also see demos of various IBM Cloud features and services in action, as well as perform hands-on labs to gain practical experience with IBM Cloud at no charge. This course is of interest to anyone who wants to be a cloud practitioner and use Cloud skills as developers, architects, system engineers, network specialists, and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving, and managing cloud solutions. This course is also suitable for learners who want to prepare for IBM Cloud Foundations Certification. LEARNING OBJECTIVES After completing this course, a learner will be able to: Understand the different infrastructure services available on IBM Cloud. Access IBM Cloud using graphical interfaces, command line tools, and APIs. Discover appropriate IBM Cloud services available to deliver specific functionality. Articulate the different ways IBM Cloud delivers services to developers and operational teams. Summarize core groups of available database, integration, analytics, artificial intelligence, and DevOps services. Deploy an application on IBM Cloud using a Starter Kit. COURSE SYLLABUS Module 1 - Introduction to IBM Cloud IBM Cloud Overview Locations, Regions, and Zones Account Types and Support Plans Billing and Usage Cost Estimator IAM Module 2 - Infrastructure (Compute, Networking, and Storage) Virtual Servers and Bare Metal Block and File Storage Object Storage Network Services Virtual Private Cloud VMWare Module 3 - Deploying Applications Containers and Kubernetes OpenShift Cloud Foundry Cloud Functions Module 4 - Services on IBM Cloud Databases Integration Artificial Intelligence Analytics DevOps Blockchain Internet of Things Cloud Paks Module 5 - Final Exam Final Exam
GratisBig Data 101
How big is big and why does big matter and what does Apache Hadoop have to do with it? In this course you will see the Big Data big picture and you will learn the terminology used in Big Data discussions. About This Course Get answers to fundamental questions such as: What is Big Data? How do we tackle Big Data? Why are we interested in it? How does Big Data add value to businesses? Gain insights on how to run better businesses and provide better services to customers Get recommendations on how to process big data on platforms that can handle the volume, velocity, variety and veracity of Big Data Learn why Hadoop is a great Big Data solution and why it's not the only Big Data solution Course Syllabus Module 1 - What is Big Data? Characteristics of Big Data What are the V’s of Big Data? The Impact of Big Data Module 2 - Big Data - Beyond the Hype Big Data Examples Sources of Big Data Big Data Adoption Module 3 - The Big Data and Data Science The Big Data Platform Big Data and Data Science Skills for Data Scientists The Data Science Process Module 4 - BDUse Cases Big Data Exploration The Enhanced 360 View of a Customer Security and Intelligence Operations Analysis Module 5 - Processing Big Data Ecosystems of Big Data The Hadoop Framework
GratisData Analysis with Python
Learn modern techniques of Data Analysis using Python and popular open-source libraries like pandas, scikit-learn and numpy and transform data into insights. LEARN TO ANALYZE DATA WITH PYTHON Data Analysis has always been a very important field, a highly demanded skill and a well-paid occupation. Until recently, it has been practised using mostly closed, expensive, and limited tools like Excel or Tableau. Python, pandas, scikit-learn and other open-source libraries have changed Data Analysis forever and have become must-have tools for anyone looking to build a career as a Data Analyst. Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! You will learn how to: Import data sets Clean and prepare data for analysis Manipulate pandas DataFrame Summarize data Build machine learning models using scikit-learn Build data pipelines Data Analysis with Python is delivered through lectures, hands-on labs, and assignments. It includes the following parts: Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimensional arrays, and SciPy libraries to work with various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. COURSE SYLLABUS Module 1 - Importing Datasets Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets Module 2 - Cleaning and Preparing the Data Identify and Handle Missing Values Data Formatting Data Normalization Sets Binning Indicator variables Module 3 - Summarizing the Data Frame Descriptive Statistics Basic of Grouping ANOVA Correlation More on Correlation Module 4 - Model Development Simple and Multiple Linear Regression Model Evaluation Using Visualization Polynomial Regression and Pipelines R-squared and MSE for In-Sample Evaluation Prediction and Decision Making Module 5 - Model Evaluation Model Evaluation Over-fitting, Under-fitting, and Model Selection Ridge Regression Grid Search Model Refinement
GratisData Science 101
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do, this field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today. ABOUT THIS COURSE Find out the truth about what Data Science is. Hear from real practitioners telling real stories about what it means to work in data science. This course was formerly named Data Science 101. COURSE SYLLABUS Module 1 - Defining Data Science What is data science? There are many paths to data science Any advice for a new data scientist? What is the cloud? "Data Science: The Sexiest Job in the 21st Century" Module 2 - What do data science people do? A day in the life of a data science person R versus Python? Data science tools and technology "Regression" Module 3 - Data Science in Business How should companies get started in data science? Tips for recruiting data science people "The Final Deliverable" Module 4 - Use Cases for Data Science Applications for data science "The Report Structure" Module 5 -Data Science People Things data science people say "What Makes Someone a Data Scientist?"
GratisIntroduction to Cloud
This course introduces you to the core concepts of cloud computing. You will gain the foundational knowledge required for understanding cloud computing from both business and practitioner perspectives. You will also learn about cloud adoption and emerging technologies, cloud computing service and deployment models, different types of cloud storage, and emergent cloud trends. About this course You will learn about the definition and essential characteristics of cloud computing, its history, emerging trends, and the business case for cloud computing. You also learn about the various cloud service models (IaaS, PaaS, SaaS) and deployment models (Public Cloud, Private Cloud, Hybrid Cloud), the key components of a cloud architecture (Virtualization, VMs, Storage, Networking, Containers ), and Emergent Cloud Trends (Hybrid Multicloud, Serverless, Microservices, Cloud Native, Application Modernization). Course syllabus Module 1: Overview of Cloud Computing Definition and essential characteristics A brief history and evolution of Cloud Key cloud service providers and their services Module 2: Cloud Adoption and Emerging Technologies Business case for Cloud Computing Emerging technologies supported by Cloud: AI, IoT, Blockchain, Analytics Module 3: Cloud Computing Service and Deployment Models Service Models: IaaS, PaaS, SaaS Deployment Models: Public, Private, Hybrid Module 4: Components of Cloud Computing Cloud Infrastructure Overview Virtualization, VMs, Bare Metal Secure Cloud Networks Containers Module 5: Cloud Storage Direct Attached File Storage Block Storage Object Storage Content Delivery Networks (CDN) Module 6: Cloud Native and Emergent Cloud Trends Hybrid Multicloud Serverless Microservices Cloud Native Application Modernization
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