IBM Cognitive Class
SQL and Relational Databases 101
Are you ready to dive into the world of SQL and relational databases? In just a few hours, you'll be able to discuss SQL basics and explain various aspects of the relational database model. Try our hands-on exercises as we guide your first steps into SQL and relational databases. About This Course Data is one of the most critical assets of any business. Data needs a database to store and process data quickly. SQL is a language used for a database to query data. In this introductory course, you'll learn the basics of the SQL language and the relational databases. You'll start by learning about the relational model and relational model concepts and constraints. By the end of this course, you will have learned and used the five basic SQL statements, some advanced SQL syntax, and join statements. This isn't your typical textbook introduction. You're not just learning through lectures. At the end of each module there are assignments, hands-on exercises, review questions, and also a final exam. Successfully completing this course earns you a certificate. So let's get started! Course Syllabus Module 1 -SQL and Relational Databases 101 Introduction to SQL and Relational Databases Information and Data Models Types of Relationships Mapping Entities to Tables Relational Model Concepts Module 2 - Relational Model Constraints and Data Objects Relational Model Constraints Introduction Relational Model Constraints Advanced Module 3 - Data Definition Language (DDL) and Data Manipulation Language (DML) CREATE TABLE statement INSERT statement SELECT statement UPDATE and DELETE statements Module 4 - Advanced SQL String Patterns, Ranges, and Sets Sorting Result Sets Grouping Result Sets Module 5 - Working with multiple tables Join Overview Inner Join Outer Join
GratisDocker Essentials: A Developer Introduction
Learn how to use containers for your applications. Create and run your first Docker container. Then, learn how to run containers in production and solve problems of orchestration such as high availability, service discovery, and reconciliation. About this course Docker is a development tool and a virtualization technology used to automate the deployment of applications inside software containers. A container on the other hand is a standard way to package an application that contains all the configuration files, and other dependencies to operate the application. Containers work by isolating the application inside the container so that everything outside the container can be standardized. Docker has provided a set of tools to simplify the use of containers. This has led to massive adoption of containers by developers and operators. In this course, you will focus mostly on Docker container technologies. In this course, you'll follow a series of hands-on labs that demonstrate how to use containers for your applications. You'll start with the basics: creating and running your first Docker containers. By the end of the course, you'll get a brief introduction to running containers in production with the and how to solve problems of advanced orchestration such as high availability, service discovery, and reconciliation. Course syllabus Set up Docker Lab 1: Run your first container What are containers? 1. Run a container 2. Run multiple containers 3. Remove the containers Lab 2: Add CI/CD value with Docker images Docker images 1. Create a Python app (without using Docker) 2. Create and build the Docker image 3. Run the Docker image 4. Push to a central registry 5. Deploy a change 6. Understand image layers 7. Remove containers Lab 3. Orchestrate applications with Docker Swarm Container orchestration overview 1. Create your first swarm 2. Deploy your first service 3. Scale your service 4. Apply rolling updates 5. Reconcile problems with containers 6. Determine how many nodes you need Final exam
GratisData Science Methodology
Grab your lab coat, beakers, and pocket calculator ... wait what? Wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed. About This Course Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Learning Objectives In this course, you will learn: The major steps involved in tackling a data science problem. The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. How data scientists think! Course Syllabus Module 1: From Problem to Approach Business Understanding Analytic Approach Module 2: From Requirements to Collection Data Requirements Data Collection Module 3: From Understanding to Preparation Data Understanding Data Preparation Module 4: From Modeling to Evaluation Modeling Evaluation Module 5: From Deployment to Feedback Deployment Feedback
GratisPython for Data Science
This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you'll be able to write your own Python scripts. If you want to learn Python from scratch, this course is for you. This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you. COURSE SYLLABUS Module 1 - Python Basics Your first program Types Expressions and Variables String Operations Module 2 - Python Data Structures Lists and Tuples Sets Dictionaries Module 3 - Python Programming Fundamentals Conditions and Branching Loops Functions Objects and Classes Module 4 - Working with Data in Python Reading files with open Writing files with open Loading data with Pandas Working with and Saving data with Pandas Module 5 - Working with Numpy Arrays and Simple APIs Numpy 1D Arrays Numpy 2D Arrays Simple APIs API Setup
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