Analyzing Big Data with SQL

Course Link: https://www.coursera.org/learn/cloudera-big-data-analysis-sql-queries

Analyzing Big Data with SQL
In this course, we’ll explore the object-relational programming model that is used to construct SQL statements and our common use of SQL as a service (SAS) service. We’ll also cover the standard input, output, and standard output operations that we see from SQL, as well as how we can use these to extract meaningful information from our data. We’ll use Visual Studio 2015 as our SVC. The course is the first half of a three-part sequence that explores SQL as a service in more depth.

Learning Outcomes

By completing this course, you’ll have a high level understanding of the SQL language and its core functionality. You’ll also have some basic programming skills to get started using in your team. You’ll need to install Visual Studio as your SVC if you do not have a previous version. You’ll continue to use the command prompt as your IDE. You should have a basic understanding of HTML, CSS, JavaScript, and related technologies like Bootstrap 3. You should also have a good understanding of SQL, as described in the larger sequence of this course.

Recommended background

You should be comfortable writing SQL statements in SQL. You should also have a basic understanding of SQL, as described in the larger sequence of this course.

Tools we’ll use

We’ll use many of the tools offered in this course, including SQLite3, Excel, and Hadoop. You’ll also have access to SQL from any computer.

Course outline

The course consists of three parts. In the first part we cover SQLite3 basics, then move on to exploring large data sets using SQL and related programming models. The course wraps up with a focus on SQL as a service (SAS) and how you can use SQL to build SQL statements in your own applications. The third part of the course focuses on an intermediate level of SQL administration and includes a small project to make you comfortable with using SQL in your team.Getting Started
SQLite3
MSSQL
SQL Interoperability
Applied Statistics for Geography I
Applied Statistics for Geography I

Course Overview: https://youtu.be/g-9uLU.In this course you will learn the basic concepts and methods of statistics, along with an introduction to descriptive statistics, their appropriate application, and their limitations. You will also learn about the different types of statistical scales and their use in Geographic Information Systems (GIS). The course consists of three parts. First part covers descriptive statistics, methods of finding values for a fixed variable, and a sample size calculation. Second part covers random sampling, standard deviation reduction, and extrapolating data to represent the complete dataset. Third part focuses on population density and population size estimation. You will also learn about density estimates from 3D GIS as well as other applications.

Course

Course Link: https://www.coursera.org/learn/cloudera-big-data-analysis-sql-queries

Big Data, Genes, and Medicine

Course Link: https://www.coursera.org/learn/data-genes-medicine

Big Data, Genes, and Medicine
Are you curious about the data behind today’s cures? Do you want to know how your own biologic and molecular clock is ticking? This course will take you on a journey to the answer those questions and more! Each week we will introduce new challenges while we delve deeper into the fascinating world of Big Data and how it is used. We will discuss topics such as statistical methods, their limitations, their applicability, and the ethics/values of using data for personal gain.

Already have a basic knowledge of data analysis? If so, welcome to the course!

Disclaimer: As you will notice, the class each week is significantly shorter than the course on average. This is to ensure a high level of quality and continuity. Each week you will complete the challenges one by one while learning a skill set that you can apply to other parts of your life. Each week you will complete the entire course with a minimum of 4 quizzes and a maximum of 12 mastery quizzes. Each week you will also get an opportunity to apply what you’ve learned by doing some additional activities and/or hands-on exercises to enhance your learning. We’ll cover everything needed to get started with data analysis including basic clustering, molecular analysis, and gene expression analysis.

If you enjoy this course and are interested in science, join millions of learners who are also passionate about health and medicine. The Big Data revolution is upon us and the opportunities are endless!Welcome & Introduction
Data Analysis & Bioconductor
Regional Variables
Sequence Analysis
Big Data Applications: Machine Learning
Machine learning is the process of getting computers to act on their own, by using machine learning algorithms. Machine learning requires deep learning, but it is possible to get computers to act on their own through simple tweaks in hardware and software. In this course, we will focus on the practical application of machine learning in various types of data, including text, music, pictures, and other images. We will learn both how to implement machine learning algorithms in C and how to use various open source and free software tools for machine learning research. You will also learn about the use of Python as a general purpose language for building machine learning models. Along the way, you will also learn how to use Python 3 as a general purpose operating system, as well as some practical techniques for programming it. This course is the third and last in the series on Data Science and Machine Learning.Welcome & Introduction to Machine Learning
Image Analysis & Sequencing
Text Analysis
Photo & Video Analysis
Big Data Applications: Regular Expression
This course will introduce you to regular expression and the basic rules that underlie its use in computer programs. You will start your journey in the world of regular expression by exploring the different parts of the regular expression tree and common

Course Link: https://www.coursera.org/learn/data-genes-medicine

Data Science for Business Innovation

Course Link: https://www.coursera.org/learn/data-science-for-business-innovation

Data Science for Business Innovation
Want to make important business decisions more efficiently? Use data to help you do just that. Developing data-driven decision-making is critical to helping you reach your goals. This course focuses on using data to inform and empower decision-making. You will learn how to use data to inform and empower people in business by developing an understanding of the key data types and how they are used in practice to inform and empower decision-makers. You will learn how to use data to empower people in business by adopting an evidence-based data-driven approach and tracking the types of data used in practice.

This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.Module 1: How Data and Decision-Making Fit Together
Module 2: Data-Driven Decisions and Data-Informative Processes
Module 3: Data-informed Public Officials
Module 4: Data-informed Business Decisions
Data Analysis Tools
Whether you are data analyst, engineer, data trader, or data manager, you will want to understand the various data formats and their characteristics. This course will help you define data analytics and explain the tools used to analyze and extract value from data. You will learn how to use various data analysis tools to explore and visualize data, and how to visualize and interpret data in order to fully benefit from an analysis. You will also learn to use standard statistical techniques to evaluate large datasets and explain the tradeoffs between smaller datasets and more.

This course is designed to provide you with a comprehensive view on data analysis and extract value from data. It will cover topics such as partitioning, data modeling, data visualization, data exploration and summary statistics. You will also learn how to apply basic statistical techniques to extract meaningful information from large datasets. You will also learn the basics of extracting meaning from large datasets.

Learning Goals: After taking this course, you will be able to (a) define what a data analysis is and how it differs from ordinary data analysis, (b) describe the format and techniques used to analyze large datasets, (c) explain the tradeoffs between smaller datasets and more, and (d) apply basic statistical techniques to extract meaningful information from large datasets.

Data Analysis is relevant to all areas of business. It’s important to understand what’s going on and what you’re doing. In this course, we’ll cover everything needed to get you up and running as quickly as possible. We’ll focus on the difference between ordinary data analysis and advanced data analysis, and help you evaluate what’s going on in your data analysis environment. We’ll also take you through the whole

Course Link: https://www.coursera.org/learn/data-science-for-business-innovation

Data-driven Decision Making

Course Link: https://www.coursera.org/learn/decision-making

Data-driven Decision Making
The course covers decision making processes, the variables that influence decisions, and the decision-maker’s role in allocating resources. We will learn how and when decisions are made, and focus on the variables that influence those decisions. We will also discuss how decision making is facilitated. We’ll cover topics such as allocation decisions, task allocation, and the allocation of resources. Finally, we will look at the decision-making process in stages. We’ll also cover topics such as stakeholder involvement and negotiation. We’ll cover the topics that you’ll need to know about to be a productive decision maker.

Learning Objectives
By the end of the course, you will be able to:
– Describe the variables that influence the course of action
– Use decision making instruments to manage resources and change the course of action
– Assess and allocate resources
– Change the course of action through decision making
– Use tools and techniques to manage resource use and change the course of action

Pre-requisites

Learners will need prior success in the course on the following exams:
– The Business Decision-Making Capstone
– The Customer Value-Driven Architect Capstone
– The Supply Chain Foundations Foundational Capstone
– The Product Development Foundational Capstone
– The Quality Assurance Organization Foundational Capstone
– The Product Management Foundational Capstone

Suggested Reading

To summarize, learners will need to know the following:

1. The business decision-making process
2. The use of decision making models and frameworks.
3. The use of metrics to measure decision making processes and processes.
4. The use of design thinking in decision making.
5. The use of stakeholder involvement in decision making.
6. The use of negotiation in decision making.
7. The use of compromise in decision making.

Presentation Skills
The presentation skills necessary for the project will be assessed and demonstrated on the slides. Two presentation skills are recognized: presenting information effectively, and presenting a clear vision. Both skills are graded and graded again, and a final presentation is graded.Evaluation Report and Resources
Challenges and Opportunities
Measurement
Final Presentation
Data Visualization with Tableau Project
In this project-based course, you will follow your own interests to create a portfolio worthy single-frame viz or multi-frame data story that will be shared on Tableau Public. You will first create a project proposal, outlining the aim of the project, design the project, and input data sources. You will then get data from the project, make dashboards and run dashboards to gather data and render views. You will then get data visualization needs, including data coverage, date and time data, line art and charting, and data filtering and filtering

Course Link: https://www.coursera.org/learn/decision-making

Foundations for Big Data Analysis with SQL

Course Link: https://www.coursera.org/learn/foundations-big-data-analysis-sql

Foundations for Big Data Analysis with SQL
This course is the second course in the specialization about learning how to use SQL to perform data analysis. It focuses on the concepts and strategies to understand the code that is executed by the SQL server. The course includes the use of MySQL as the SQL server and the use of SQLite3 as the database. You will learn how to get SQL to execute arbitrary code on your own servers, as well as the use of the DB2 data model for efficient access control. You will also learn how to use linear algebra and probability to model the behavior of the SQL and to perform SQL analysis on data.

No prior knowledge of SQL is required, although the course assumes some familiarity with the SQL language and programming concepts. It is recommended that you have a basic grasp of the programming concepts in the Foundations of Big Data Science specialization.

The course codebase is available on Github. You can run it from within your favorite IDE (including Nano), or download the snapshot here.The Data Model and the Analysis Strategy
An Introduction to SQL
Basics of SQL
Functions and Encoding Data
Foundations of Virtual Instruction
This course is for you if you are looking to dive deeper into Virtual Instruction, or increase your skill level in Deploying Your Child, Pupil, or Peer. This course will cover the specialized topic of Virtual Instruction in detail, with an emphasis on the role and importance of the teaching profession in the increasingly fragmented and increasingly internationalized classroom.

This course is the second part of a four-part series. The goal of the course is to get you up to speed on the topic of Virtual Instruction and, as a result, deepen your understanding of the primary skills needed in the field.

The first part of the course focuses on the topic of certifications and systems of professional education (SOPE). We will cover topics such as curriculum, board, and education system standards, as well as state and local requirements. The second part of the course focuses on the topic of virtual instruction. We will cover topics such as curriculum mapping, certificate and credentialing, effective and minimum standards, and supervision and accountability. We will also discuss the topic of voluntary participation in the program, as well as the various degrees of virtual instruction. We will also discuss the topic of safe environments for virtual instruction, including information technology embedded in the curriculum, and the impact of virtual instruction on the quality of instruction.

Upon successful completion of this course, you will be able to understand the information presented in this course and will have a much better understanding of the field of Virtual Instruction. A strong leadership challenge will be a plus for your self-esteem and motivation.

This course is for current Certificate II students, and those wishing to upgrade to a Level III or IV course.Introduction to Virtual Instruction
Safety in Virtual Instruction
Coaching/Management in Virtual Instruction

Course Link: https://www.coursera.org/learn/foundations-big-data-analysis-sql

Managing Big Data in Clusters and Cloud Storage

Course Link: https://www.coursera.org/learn/cloud-storage-big-data-analysis-sql

Managing Big Data in Clusters and Cloud Storage
Managing big data in clusters and cloud storage in this course will give you the opportunity to learn how to design and build your own cloud storage solutions. In addition, you’ll learn the basic concepts needed to successfully integrate data storage into your cloud computing strategy. You’ll also learn the basics of networking in clouds. You’ll also learn how to use complex storage architectures (large/micro/network-based) with elastic storage. You’ll also learn the basics of network virtualization and virtual disk drives (SAN/NAS). This course should take about four weeks of dedicated effort for each of the three topics. You should also have a working knowledge of cloud computing basics.Know how your data is managed
Design storage solutions
Build storage solutions using elastic storage
Build storage solutions with complex storage architectures
Massive Open Online Course!
The Massive Open Online Course (MOOC) is a fully online, interactive, interactive, and peer-to-peer course, created by educators and researchers in the Massive Open Online DataSource (MOODS) program. Started in 2008, the MOOC has grown immensely since. It now enrolls tens of thousands of people per session who work side by side on their MOOCs. The MOOC is an opportunity for MOOCs to come together around a common goal: expanding their MOOC knowledge base. The MOOC has also grown tremendously in scope; it now includes a collection of nearly 3,000 MOOCs from more than 60 countries.

MOOCs are open online courses that are interactive, structured, and focused on one or more of the Massive Open Online DataSource (MOODS) projects currently underway at the Coursera online platform. The MOOCs are open to anyone and everyone. They are made by individuals and organizations – be they volunteers or business owners, data scientists or administrators – and come from a variety of disciplines.

The MOOCs are open to anyone and everyone. They are made by individuals and organizations – be they volunteers or business owners, data scientists or administrators – and come from a variety of disciplines.

The MOOCs are designed to share knowledge and resources with each other. Participation is key to the success of MOOCs. There is always a strong incentive to join a MOOC if you will gain familiarity with the topics and the projects. Participation is also key to the growth of the MOOCs as they bring together individuals and organizations working on the same problem.

There is always a strong incentive to join a MOOC if you will gain familiarity with the topics and the projects. Participation is also key to the growth of the MOOCs as they bring together individuals and organizations working on the same problem.

Each MOOC has a specific theme and collection of videos/lectures/programs. Some MOOCs have multiple sessions/lectures, while others have shorter/

Course Link: https://www.coursera.org/learn/cloud-storage-big-data-analysis-sql

Spatial Data Science and Applications

Course Link: https://www.coursera.org/learn/spatial-data-science

Spatial Data Science and Applications
Spatial (mapping and navigation) is a key skill for anyone looking to advance in their career, research or education. Although spatial knowledge is a necessity for many high-demand positions, spatial skills are not universally valued. Spatial skills require the mastery of many of the hallmarks of good spatial data science: visualization, problem solving, problem solving in a methodical and methodical manner, attention to detail (e-discovery), and a strong functional approach (problem-proposing).

In this course, you will explore the many facets of spatial data science and apply them to a range of applications, including as part of a team, in industry or in academia. You will learn what spatial skills are, how to assess and practice them, and how to use them for specific goals. You will also explore the application of spatial knowledge in a wide range of applications, including as part of a team, in industry or in academia.

Learning Objectives

• Describe the main skills and requirements of spatial data scientists
• Explain the multidimensional nature of spatial data science
• Refine and practice spatial data science skills in a methodical and methodical manner
• Communicate skills and standards effectively
• Assess and practice the skills and standards required for proper and effective use of spatial data science tools
• Recognize and apply the tools and concepts effectively
• Develop and apply an approach to spatial data science that is approach-based, multidisciplinary and methodical
• Communicate skills and standards effectively

Prerequisites

To get the most out of this course, you will need to:
• Have mastered the previous courses in the specialization (Understanding Data, Understanding Scenarios, and Developing Solutions) and be familiar with the codebase
• Have successfully completed previous courses in the specialization
• Have successfully completed the first course in the specialization, Introduction to Software Engineering, and the second course, Project Proposal, in which you will design and implement a project proposal based on your knowledge of the software engineering process
• Have successfully completed the first course in the specialization, Introduction to Database and Information Processing, and the second course, Project Proposal, in which you will design and implement a project proposal based on your knowledge of the software engineering process
• Have successfully completed the other courses in the Specialization, Introduction to Data Analysis and Management, Introduction to Computer Architecture, Introduction to Programming in Python, and Introduction to Machine Learning

>>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<https://www.coursera.org/learn/spatial-data-science

Scalable Machine Learning on Big Data using Apache Spark

Course Link: https://www.coursera.org/learn/machine-learning-big-data-apache-spark

Scalable Machine Learning on Big Data using Apache Spark
This one-week, accelerated program in Python (using Python 3.5) takes you from introduction to machine learning methods to deep learning methods and deep convolutional neural networks. You will learn how to apply these methods in a scalable way for large-scale classification problems. You will also learn how to apply these methods in a scalable way for machine translation and localization tasks.

This is the third course in the Data Engineering Specialization. The specialization focuses on methods and algorithms that can be used to create tabular data in a scalable way.Machine Learning
Deep Learning
Training/Test Set
Machine Translation
Social and Cultural Institutions in Developing Countries
Have you ever seen the stories told by people in the streets or the music played at evening events? Or the stories told by the arts in the galleries? Or the stories told by students in sports stadiums or playgrounds? What do you see and hear? What do you do?

In this course, you will learn how to make an impact in the social and cultural context of low- and middle-income countries. You will learn the skills needed to be a change agent, how to participate constructively in the context of conflict, and how to help alleviate poverty and promote participation.

You will need to be able to speak, read, write, and understand Punjabi, English, and regional English. You will need to have some knowledge of biology and geography. You will also need to have some knowledge of how to navigate the complex web environment of the Global South.

If you are new to studying in India, our pre-requisites are as follows:
1) To be familiar with the social context of low- and middle-income countries.
2) To have attended some classes on india before.
3) To have a general understanding of how low-income countries are run.
4) To have attended some courses on entrepreneurship in low- and middle-income countriesIntroduction to the course
Our context
Participating constructively
Acting in conflict and mediating constructively
Social Entrepreneurship
This course is for the naysayers who want to make a change in their lives. We show you step by step how the ideas, techniques, and resources offered by leading academic institutions in the field of social entrepreneurship can change your life.

We offer a unique opportunity for you to discover the ideas, techniques, and resources that are the lifeblood of successful businesses, as well as transform your community and country. We will guide you through the key stages of creating a change agent in your community or country, and enabling you to bring about change in your country or community. We will also show you how to evaluate and adjust to changes in your country or community, as well as in the global economy.

Course Link: https://www.coursera.org/learn/machine-learning-big-data-apache-spark

Introduction to Big Data

Course Link: https://www.coursera.org/learn/big-data-introduction

Introduction to Big Data
This course introduces the data analytics basics with a focus on practical applications. The course focuses on developing data analysis skills through a series of paired teaching sections. As such, the course is ideal for students of all skill levels and experience levels. The course is suitable for self-study, as well as for students considering a career change or entry-level data analysis. The course is also suitable for students considering a career change or entry-level data analysis, who want to move more into the data analytics phase of their career. The course focuses on practical application and not “how-to-do-it-here-here-here”. There are no required prerequisite skills for the course, but students who plan to work in data analytics or who plan to work on a team will find the course useful.

You will get a firm understanding of the data analytics landscape, be able to identify the components of a data analytic pipeline and understand different phases involved. You will also begin to identify different types of analytical tasks and their requirements, and begin to define your own analytical tasks. You will be able to identify and describe the data processes that lead to interactive, predictive models and their use in business.

The course is suitable for self-study (week 1), but it also makes for interesting reading for those who want to work on a team. The course is suitable for those who plan to work in data analytics or who plan to work on a team. The course is also suitable for students considering a career change or entry-level data analysis, who want to move more into the data analytics phase of their career. The course focuses on practical application and not “how-to-do-it-here-here-here”. There are no required skills for the course, but students who plan to work in data analytics or who plan to work on a team will find the course useful.Big Data and Machine Learning
Data Analysis Pipeline
Advanced Topics
Introduction to Biochemical Signaling
Biochemical signaling is responsible for initiating events in biological processes, and it also plays a role in the control of processes. In this course, we will learn about the different types of signaling, how it is regulated, and how this regulation is implemented in the cell. We will also learn about the different types of crosstalk between signaling and the non-signaling processes in the cell. This will give you the tools to analyze processes and programs in different organisms, and will also give you a good understanding of the biochemical regulation of processes in various organisms.

This is the second course in the series on Introduction to Biochemical Signaling. The course on Biochemical Signaling explained how signaling and crosstalk can lead to errors, and also how errors can propagate in biological systems. The course on crosstalk explained how crosstalk can propagate in biological systems

Course Link: https://www.coursera.org/learn/big-data-introduction

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

Course Link: https://www.coursera.org/learn/cloud-applications-part2

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud
In this course you will learn how to integrate Amazon Web Services (AWS) into your cloud computing infrastructure. You will also learn how to access AWS from a Windows computer or laptop and how to utilize Linux cloud users for AWS. We will also discuss common cloud connectivity protocols such as WiFi, Ethernet, USB, HTTP, SMB and CIFS.

At the end of this course, you will be able to:

1. Integrate AWS (AWS) services into your cloud computing infrastructure
2. Access AWS resources from a Windows computer or laptop using the command line interface
3. Use Linux cloud users for AWS
4. Use common cloud protocols to access AWS services
5. Explain AWS services and their pros and cons
6. Assess network connectivity using different types of network diagrams
7. Create and use virtual networks in AWS
8. Integrate Amazon EC2 (Amazon EC2) into a cloud computing environment
9. Create and use virtual machines in AWS
10. Develop and deploy virtual applications in the cloud
11. Use AWS tools to query and analyze AWS resources
12. Create a network diagram in AWS
13. Create a virtual network in AWS
14. Create a subnet or subnet mask for a virtual computer
15. Create a network route in the cloud
16. Create a virtual machine in the cloud
17. Design a wireless network diagram in AWS
18. Design a TCP or UDP protocol transaction for a virtual computer
19. Assess network connectivity using different types of network diagrams
20. Create a virtual network route in the cloud
21. Create a subnet or subnet mask for a virtual computer
22. Create a network route in the cloud
23. Create a virtual machine in the cloud
24. Design a TCP or UDP protocol transaction for a virtual computer
25. Design a virtual machine route in the cloud

This is the second course in the Cloud Computing Specialization. The Specialization is the specialization that focuses on practical skills and analysis of cloud applications that span all areas of the cloud computing marketplace.Cloud Computing Architectures
Cloud Networking
Accessing AWS Resources
Accessing Amazon EC2 Resources
Coffee Tabletop Simulator
This course is for those new to the art of coffee brewing. We will take you through the entire roasting, roasting equipment, coffee grounds, and brewing process. We will give you a basic understanding of the components of your own coffee table. We will introduce and explain common components of the coffee table, like beans, filters, and wort casks. We will show you how to add flavor and aroma to your brew. We will show you how to adjust the physical and chemical properties of your brew to create a specific profile.

This course is the second part of a three-part series in which Coffee

Course Link: https://www.coursera.org/learn/cloud-applications-part2