Cloud Computing Concepts: Part 2

Course Link: https://www.coursera.org/learn/cloud-computing-2

Cloud Computing Concepts: Part 2
Cloud computing and the underlying infrastructure all around us all the time. In this course you will explore the main components of the cloud and their differentiated roles. We’ll cover virtualization, container technologies, storage services, domain controllers, cloud networking, storage management, the cloud management interface, cloud replication, cloud-based storage services, cloud-based storage management, cloud-based storage products, cloud-based storage services integration, cloud-based storage products and solutions, cloud-based storage management, cloud-based storage products and services, cloud-based storage products and services, cloud storage services integration, cloud-based storage services products and services, cloud-based storage services products and services, cloud-based storage services products and services, cloud-based storage services and services.

This is the second half of the two-part course that covers cloud computing and the underlying infrastructure all around us. The course will continue in the next part with a closer look at storage services, file systems, archival storage, virtualization and cloud networking.Cloud Computing Systems
Cloud Networking
Cloud Storage Services
Cloud Authentication
Cabinet Suite for Developers
The Capstone course is the capstone for the specialization, in which you will combine the skills from all the courses in this specialization to create a custom software development management system design.

In the Capstone, you will combine the knowledge and techniques obtained in all the courses in this specialization, to build your own customised version of VMware vRealize Automation Enterprise (VRA) that can manage both a small and large number of VMs. You will also design and build a small utility to manage your VMs.

In the end of the course, you will be able to design a small utility that can manage your VMs, and you will be able to choose the best platform for your applications, if any, and run them on different computers and operating systems.

This course is the first part of a two-part series, in which we will explain more about the behind-the-scenes skills required to build and manage applications in VMware Virtual SAN. Part 2 will be available in early 2018.

By the end of this course, you will be able to:
• Design a small utility to manage your VMs
• Write an application that can run on any operating system
• Design a small utility to manage your VMs, and write the program that does it
• Write an application that can run on any operating system
• Write an application that can run on any operating system
• Design a small utility to manage your VMs, and use it yourself
• Write an application that can run on any operating system
• Design a small utility to manage your VMs, and use it yourself
• Design a small utility to manage your VMs, and

Course Link: https://www.coursera.org/learn/cloud-computing-2

Database systems Specialization

Course Link: https://www.coursera.org/specializations/database-systems

Database systems Specialization
The University of London Business School has created a specialisation specifically designed to help you gain a competitive advantage in the rapidly evolving database technology market. This eight week course is intended to enable you to gain the competitive edge you need to address critical market issues in databases, as well as enhance your skills as a leader in the IT business.

The specialisation is endorsed by CMIEquivalent Database Systems in MySQL, MS Access, CRUD, and File Systems (FAS)
DBA skills for MySQL, MS Access, CRUD, and File Systems (FAS)
Database design and data recovery
Data performance and recovery
Networked Database Systems
Data Structures and Performance
The primary goal of this course is to familiarise you with data structures and performance for the data analysis and development of applications in C++. This will allow you to understand the major design decisions that are required in order to guarantee data integrity and performance. Our course will focus on the major data structures in the data analysis and development arena, including linked lists, trees, and sorted lists. You will learn about the major performance bottlenecks and trade-offs that occur when you try to optimize one of these data structures. You will also learn about other data structures that can be used to efficiently implement algorithms and classes of types.

This course is the third of three related courses in the specialization. The course modules 2-5 have been structured to make them fully integrated with the rest of the specialization, while the course module 6 completes the analysis of a specific implementation detail.

The course material will be applied to the following projects:
1. Analyzing the performance of a graph database, an SQLite3 implementation
2. An implementation of a partial application of a partial application, using C++ Copy-on-Write
3. A full application implementation using C++ Copy-On-Write
4. A full implementation of the partial application, using Java Copy-On-Write
5. A full implementation of the class implementation

Each project will have its own set of requirements, so you have to read the specification carefully and follow the instructions to get started. Our project will require you to work with a specific implementation detail, while the other projects will have more general requirements to get going. You will be able to ask more specific questions about the codebase and the data model, for example, what is the maximum size of a linked list or how to allocate memory for an implementation of a class? How to ensure that each method in a class compiles? How to use threads in a class without performance penalties? How to test for and fix performance issues in your code?

We will guide you through every step of the data structure and performance optimization process, showing you how to capture and control data, make sure that the code is thread-safe, and run efficiently. We will also show you how to analyze and report on the performance of your code, so you can share your findings with other team members.

Data structures and performance are very important topics, you will learn both the techniques and the approaches to optimize them. You will learn both how to start your learning journey by organizing data in a logical and structured way, and how to add more structure to your data models and abstractions. You will then learn how to further organize your data by using filters to limit the size of your data set and improve the efficiency of your code. You will then learn how to perform optimizations and benchmark your code against different data sets using SMP optimizations, for example.

Data structures and performance are very important topics, you will learn both the techniques and the approaches to optimize them. You will learn both how to start your learning journey by organizing data in a logical and structured way, and how to add more structure to your data models and abstractions. You will then learn how to further organize your data by using filters to limit the size of your data set and improve the efficiency of your code. You will then learn how to perform optimizations and benchmark your code against different data sets using SMP optimizations, for example.

The course assumes some prior knowledge of programming and data analysis, but it also assumes that you have some basic computer science background.Introduction to Data Structures
Data Sprinklers
Trees
Sorted Lists
Data Structures and Performance
The primary goal of this course is to familiarise you with data structures and performance for the data analysis and development of applications in C++. This will allow you to understand the major design decisions that are required in order to guarantee data integrity and performance. Our course will focus on the major data structures in the data analysis and development arena, including linked lists, trees, and sorted lists. You will learn about the major performance bottlenecks and trade-offs that occur when you try to optimize

Course Link: https://www.coursera.org/specializations/database-systems

Best Coursera Courses for Data Science

Here is a list of best coursera courses for data science.

1. Introduction to Data Science Specialization

This data science specialization provided by IBM, which include 4 sub courses. In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. The specialization entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs. It will familiarize learners with various open source tools, like Jupyter notebooks, used by Data Scientists. It will teach them about methodology involved in tackling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and projects to apply their newly acquired skills and knowledge.

1) What is Data Science?
2) Open Source tools for Data Science
3) Data Science Methodology
4) Databases and SQL for Data Science

2. Applied Data Science Specialization

This data science specialization also provided by IBM, which include 4 sub courses. This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. It appeals to anyone interested in pursuing a career in Data Science, and already has foundational skills (or has completed the Introduction to Applied Data Science specialization). You will learn Python – no prior programming knowledge necessary. You will then learn data visualization and data analysis. Through our guided lectures, labs, and projects you’ll get hands-on experience tackling interesting data problems. Make sure to take this specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.

1) Python for Data Science
2) Data Visualization with Python
3) Data Analysis with Python
4) Applied Data Science Capstone

3. Applied Data Science with Python Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

1) Introduction to Data Science in Python
2) Applied Plotting, Charting & Data Representation in Python
3) Applied Machine Learning in Python
4) Applied Text Mining in Python
5) Applied Social Network Analysis in Python

4. Data Science Specialization

This data science specialization provides by Johns Hopkins University, which covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. The specialization includes 10 sub courses:

1) The Data Scientist’s Toolbox
2) R Programming
3) Getting and Cleaning Data
4) Exploratory Data Analysis
5) Reproducible Research
6) Statistical Inference
7) Regression Models
8) Practical Machine Learning
9) Developing Data Products
10) Data Science Capstone

5. Data Science at Scale Specialization

This data science specialization provides by University of Washington, which covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project. The specialization includes 4 sub courses:

1) Data Manipulation at Scale: Systems and Algorithms
2) Practical Predictive Analytics: Models and Methods
3) Communicating Data Science Results
4) Data Science at Scale – Capstone Project

6. Advanced Data Science with IBM Specialization

This data science specialization also provided by IBM, which include 4 sub courses. As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You’ll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

1) Fundamentals of Scalable Data Science
2) Advanced Machine Learning and Signal Processing
3) Applied AI with DeepLearning
4) Advanced Data Science Capstone

7. Genomic Data Science Specialization

This data science specialization provides by Johns Hopkins University, which covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. The specialization includes 8 courses:

1) Introduction to Genomic Technologies
2) Genomic Data Science with Galaxy
3) Python for Genomic Data Science
4) Algorithms for DNA Sequencing
5) Command Line Tools for Genomic Data Science
6) Bioconductor for Genomic Data Science
7) Statistics for Genomic Data Science
8) Genomic Data Science Capstone

8. Data Mining Specialization

This data science specialization provides by Illinois State University, which teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 – 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization. The specialization includes 6 courses:

1) Data Visualization
2) Text Retrieval and Search Engines
3) Text Mining and Analytics
4) Pattern Discovery in Data Mining
5) Cluster Analysis in Data Mining
6) Data Mining Project

9. Data Analysis and Interpretation Specialization

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world’s biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions.

1) Data Management and Visualization
2) Data Analysis Tools
3) Regression Modeling in Practice
4) Machine Learning for Data Analysis
5) Data Analysis and Interpretation Capstone

10. Executive Data Science Specialization

Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects. In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

1)A Crash Course in Data Science
2)Building a Data Science Team
3)Managing Data Analysis
4)Data Science in Real Life
5)Executive Data Science Capstone

11. Other Useful Data Science Common Courses:

1) Data Science Math Skills
2) Data Science Ethics
3) How to Win a Data Science Competition: Learn from Top Kagglers

Posted by BestCourseraCourses, Post Link: https://bestcourseracourses.com/best-coursera-courses-for-data-science/