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Coursera Data Science Specialization

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Last Updated: 02 July 2021

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General | Latest Info

Data Science has critical applications across most industries, and is one of the most in-demand careers in Computer Science. Data Scientists are detectives of the Big Data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, field of Data Science encompasses the entire data life cycle. That starts with capturing lots of raw data using data collection techniques, and then building and maintaining Data Pipelines and Data warehouses that efficiently acleana data and make it accessible for Analysis at scale. This data infrastructure allows Data Scientists to efficiently process datasets using Data Mining and Data Modeling skills, as well as analyze these outputs with sophisticated techniques like predictive Analysis and qualitative Analysis. Finally, these findings must be presented using Data Visualization and Data reporting skills to help business decision makers. Depending on the size of company, Data Scientists may be responsible for this entire Data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger Data Science team. Computer Science is one of the most common subjects that online learners study, and Data Science is no exception. While some learners may wish to study Data Science through traditional on-campus degree program or intensive abootcampa class or school, cost of these options can add up quickly once tuition as well as cost of books and transportation and sometimes even lodging are include. As an alternative, you can pursue your Data Science Learning plan online, which can be a flexible and affordable option. There are a wide range of popular online courses in subjects ranging from foundations like Python Programming to Advanced Deep Learning and artificial intelligence applications. Students can choose to get certifications in individual courses or specializations or even pursue entire Computer Science and Data Science degree programs online. Best of all, these online courses include lecture videos, live office hour sessions, and opportunities to collaborate with other learners from all around the world, giving you the chance to ask questions and build teamwork skills just like you would on campus. In todayas era of abig dataa, Data Science has critical applications across most industries. This gives students with Data Science backgrounds a wide range of career opportunities, from general to highly specific. Some companies may hire Data Scientists to work on the entire data life cycle, while larger organizations may employ entire teams of Data Scientists with more specialized positions such as Data engineers to build data infrastructure or Data analysts, business intelligence analysts, Decision Scientists to interpret and use this data. Some tech companies may employ much more specialized data scientists. For example, companies building internet of things devices using speech recognition need Natural Language Processing engineers. Public health organizations may need disease mappers to build predictive epidemiological models to forecast the spread of infectious diseases. And firms developing artificial intelligence applications will likely rely on Machine Learning engineers.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Executive Data Science

Table

ClassCourse PageLinks
A Crash Course in Data ScienceCourseraBook
Building a Data Science TeamCourseraBook
Managing Data AnalysisCourseraBook
Data Science in Real LifeCourseraBook

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. Youll get a crash course in Data Science so that youll be conversant in the field and understand your role as leader. Youll also learn how to recruit, assemble, evaluate, and develop team with complementary skill sets and roles. Youll learn the structure of the Data Science pipeline, goals of each stage, and how to keep your team on target throughout. Finally, youll learn some down-to-earth practical skills that will help you overcome common challenges that frequently derail Data Science projects.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Criteria

Specialization opens with Data Scientists Toolbox, providing a broad overview of Specialization itself and of tools and technologies that will be covered throughout the Course. You will learn the basics of Git, GitHub, Markdown and R. With tutorials on how to install RStudio and R packages. Data Scientists Toolbox is basic to the point of being remedial. A large portion of the course is spent reviewing what upcoming courses will cover. It seems disingenuous to charge 50 for a Course that explain what additional courses will cover. Concrete materials in this Course could easily be relegated to pre-requisites of other courses. If you complete the basic tutorial on Git and Markdown and know how to install software, you will have completed all the requirements for this Course. Skip. If you know absolutely nothing about software development, this course may be useful. Otherwise, skip it. The R Programming Course provides an overview of R as a programming language. It covers basic R syntax and data structures and OFFER opportunity to practice writing basic R functions. Give background in other programming languages R Programming Course will not provide you with much benefit. I would recommend following some R Programming introductory tutorials at your own pace. If, however, you do not have much programming experience, this Course will explain the fundamentals and teach you to think programmatically. This is one of few courses that come with recommended textbook. Unfortunately, textbook covers advanced R material and would only be useful after becoming familiar with R Programming. The pace of the book does not match expectations of the course. Take. There is enough R specific material to make this course worth while. Just do expect revelations. You will not be expert in R after this Course. Additional textbook would be wonderful. Getting and Cleaning Data covers reading files into R from a variety of sources: CSV, MySQL, HDFS, among others. You will learn how to reshape data using R and how to generate summary statistics of your data. While getting and cleaning data is a necessary process in Data Analysis, material covered in this Course is basic and disjointed. I would vastly prefer covering how to load one or two different file types and spend more TIME on manipulating and preparing data with R. Skip. This material can easily be learnt when needed using specific Google searches on how to load particular file with R. Consider taking this Course if you need additional practice in R. Exploratory Analysis Course covers plotting in R using base plotting system, lattice package, and ggplot2. It then moves on to explain k-means, dimensionality reduction and principal components Analysis. The material on plotting is great. I only wish that coverage of lattice package were removed and more TIME dedicated to ggplot.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Are certificates worth it?

One big difference between Udemy and other platforms, like edX, Coursera, and Metis, is that the latter offer certificates upon completion and are usually taught by instructors from universities. Some certificates, like those from edX and Metis, even carry continue education credits. Other than that, many real benefits, like accessing grade homework and tests, are only accessible if you upgrade. If you need to stay motivated to complete the entire course, committing to a certificate also put money on line so youll be less likely to quit. I think there is definitely personal value in certificates, but, unfortunately, not many employers value them that much.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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