In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will evaluate pre-attentive attributes and why they are important in visualizations. You will exam the importance of using the "right" amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites: You have a good ML background and have been creating/deploying ML pipelines You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses) You have completed the MLOps Fundamentals course. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science. Please have a look at the full specialization curriculum: https://www.coursera.org/specializations/advanced-data-science-ibm If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging. After completing this course, you will be able to: • Describe how basic statistical measures, are used to reveal patterns within the data • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. • Identify useful techniques for working with big data such as dimension reduction and feature selection methods • Use advanced tools and charting libraries to: o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling) For successful completion of the course, the following prerequisites are recommended: • Basic programming skills in python • Basic math • Basic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed) In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) • Jupyter notebooks (brought to you by IBM Watson Studio for free) • ApacheSpark (brought to you by IBM Watson Studio for free) • Python We've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps. Of course, you can give this course a try first and then in case you need, take the following courses / materials. It's free... https://cognitiveclass.ai/learn/spark https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68 This course takes four weeks, 4-6h per week

In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability. Additionally, you will learn how to assess a process with respect to how capable it is of meeting specifications, either internal or external, and make decisions about process improvement. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   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, Calculus, Linear Algebra, Probability, and Statistics.

Welcome to this project-based course, SQL CASE Statements. In this project, you will learn how to use SQL CASE statements to query tables in a database. By the end of this 2-hour long project, you will be able to write simple CASE statements to retrieve the desired result from a database. Then, we will move systematically to write more complex SQL CASE statements. Furthermore, we will see how to use the CASE clause together with aggregate functions, and SQL joins to get the desired result you want from tables in a database. Also, you will learn how to use the CASE clause to transpose the result of a query. 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, and performing joins in SQL 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 project on SQL CASE Statements. However, if you are comfortable writing queries in PostgreSQL, please join me on this wonderful ride! Let’s get our hands dirty!

This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

In this 1-hour long project-based course, you will learn how to create a Java Stream object based on an array of data, and understand the distinction between terminal and intermediate stream operations. You will iterate through the data stream using the forEach method, and use a range of Stream methods to perform logical operations on the data stream. You will perform basic statistical calculations on a stream of numeric data, and string operations on a stream of string data. You will learn how to use the map, filter, and reduce Stream methods. Finally, you will learn how to load a CSV file, the COVID vaccination dataset, and turn it into a data stream, and perform basic exploratory analysis of the data. 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.

Welcome to Demand Analytics - one of the most sought-after skills in supply chain management and marketing! Through the real-life story and data of a leading cookware manufacturer in North America, you will learn the data analytics skills for demand planning and forecasting. Upon the completion of this course, you will be able to 1. Improve the forecasting accuracy by building and validating demand prediction models. 2. Better stimulate and influence demand by identifying the drivers (e.g., time, seasonality, price, and other environmental factors) for demand and quantifying their impact. AK is a leading cookware manufacturer in North America. Its newly launched top-line product was gaining momentum in the marketplace. However, a price adjustment at the peak season stimulated a significant demand surge which took AK completely by surprise and resulted in huge backorders. AK faced the risk of losing the market momentum due to the upset customers and the high cost associated with over-time production and expedited shipping. Accurate demand forecast is essential for increasing revenue and reducing cost. Identifying the drivers for demand and assessing their impact on demand can help companies better influence and stimulate demand. I hope you enjoy the course!

This course aims to introduce learners to advanced visualization techniques beyond the basic charts covered in Information Visualization: Fundamentals. These techniques are organized around data types to cover advance methods for: temporal and spatial data, networks and trees and textual data. In this module we also teach learners how to develop innovative techniques in D3.js. Learning Goals Goal: Analyze the design space of visualization solutions for various kinds of data visualization problems. Learn what designs are available for a given problem and what are their respective advantages and disadvantages. - Temporal - Spatial - Spatio-Temporal - Networks - Trees - Text This is the fourth course in the Information Visualization Specialization. The course expects you to have some basic knowledge of programming as well as some basic visualization skills (as those introduced in the first course of the specialization).