Managing Big Data with MySQL

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/analytics-mysql

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About Course

This course is an introduction to how to use relational databases in business analysis. You will learn how relational databases work, and how to use entity-relationship diagrams to display the structure of the data held within them. This knowledge will help you understand how data needs to be collected in business contexts, and help you identify features you want to consider if you are involved in implementing new data collection efforts. You will also learn how to execute the most useful query and table aggregation statements for business analysts, and practice using them with real databases. No more waiting 48 hours for someone else in the company to provide data to you – you will be able to get the data by yourself! By the end of this course, you will have a clear understanding of how relational databases work, and have a portfolio of queries you can show potential employers. Businesses are collecting increasing amounts of information with the hope that data will yield novel insights into how to improve businesses. Analysts that understand how to access this data – this means you! – will have a strong competitive advantage in this data-smitten business world.

Course Syllabus

The Coursera Specialization, "Managing Big Data with MySQL" is about how 'Big Data' interacts with business, and how to use data analytics to create value for businesses. This specialization consists of four courses and a final Capstone Project, where you will apply your skills to a real-world business process. You will learn to perform sophisticated data-analysis functions using powerful software tools such as Microsoft Excel, Tableau, and MySQL. To learn more about the specialization, please review the first lesson below, "Specialization Introduction: Excel to MySQL: Analytic Techniques for Business." In this fourth course of this specialization, "Managing Big Data with MySQL” you will learn how relational databases work and how they are used in business analysis. Specifically, you will: (1) Describe the structure of relational databases; (2) Interpret and create entity-relationship diagrams and relational schemas that describe the contents of specific databases; (3) Write queries that retrieve and sort data that meet specific criteria, and retrieve such data from real MySQL and Teradata business databases that contain over 1 million rows of data; (4) Execute practices that limit the impact of your queries on other coworkers; (5) Summarize rows of data using aggregate functions, and segment aggregations according to specified variables; (6) Combine and manipulate data from multiple tables across a database; (7) Retrieve records and compute calculations that are dependent on dynamic data features; (8) Translate data analysis questions into SQL queries that accommodate the types of anomalies found in real data sets. By the end of this course, you will have a clear understanding of how relational databases work and have a portfolio of queries you can show potential employers. Businesses are collecting increasing amounts of information with the hope that data will yield novel insights into how to improve businesses. Analysts that understand how to access this data – this means you! – will have a strong competitive advantage in this data-smitten business world. To get started with this course, you can begin with, "Introduction to Managing Big Data with MySQL." Please take some time to not only watch the videos, but also read through the course overview as there is extremely important course information in the overview.

Deep Learning Specialization on Coursera

Course Introduction

Managing Big Data with MySQL This course is the next evolution of the "Managing Data with MySQL" course series. In this course you will learn how to use the command-line interface to manage and visualize large datasets with MySQL. We'll learn about the data types that are supported by MySQL, how to use indexes and filter statements, and how to use the MySQL Data Model to identify which rows from a dataset to be displayed. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that is relevant to your query. We'll use the MySQL Data Model to identify which rows to be displayed, so that you can find data that

Course Tag

Data Analysis MySQL Teradata SQL

Related Wiki Topic

Article Example
Big data In the provocative article "Critical Questions for Big Data", the authors title big data a part of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth". Recent developments in BI domain, such as pro-active reporting especially target improvements in usability of big data, through automated filtering of non-useful data and correlations.
MySQL Cluster Starting with MySQL Cluster 7.2, support for synchronous replication between data centers was supported with the Multi-Site Clustering feature.
Big data Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, ranging from a few dozen terabytes to many petabytes of data.
Big data Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the 2016 U.S. Presidential Election with varying degrees of success. Forbes predicted "If you believe in "Big Data" analytics, it’s time to begin planning for a Hillary Clinton presidency and all that entails.".
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MySQL Third-party proprietary and free graphical administration applications (or "front ends") are available that integrate with MySQL and enable users to work with database structure and data visually. Some well-known front ends are:
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Big data Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing. Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information. A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data. Vast amount of sensory data in addition to historical data construct the big data in manufacturing. The generated big data acts as the input into predictive tools and preventive strategies such as Prognostics and Health Management (PHM).
Big data There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners did not favour it.
Big data Big data can be used to improve training and understanding competitors, using sport sensors. It is also possible to predict winners in a match using big data analytics.
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Big data Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."
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Big data Big data sets come with algorithmic challenges that previously did not exist. Hence, there is a need to fundamentally change the processing ways.
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Big data Ioannidis argued that "most published research findings are false" due to essentially the same effect: when many scientific teams and researchers each perform many experiments (i.e. process a big amount of scientific data; although not with big data technology), the likelihood of a "significant" result being actually false grows fast – even more so, when only positive results are published.
Big data Big data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions. Some areas of improvement are more aspirational than actually implemented. The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase. This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data. There is now an even greater need for such environments to pay greater attention to data and information quality. "Big data very often means `dirty data' and the fraction of data inaccuracies increases with data volume growth." Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed. While extensive information in healthcare is now electronic, it fits under the big data umbrella as most is unstructured and difficult to use.
Big data Big data can be described by the following characteristics: