Hello guys, if you are thinking of joining Data Science Specialization at John Hopkins University in Coursera but thinking about whether it's worth your time and money, you have come to the right place. Earlier, I have shared the best Coursera courses for Data Science, Cloud Computing, Machine Learning, Python Programming, and today, I will review one of the most popular Data Science specializations on Coursera, Data Science Specialization by John Hopkins University. While Coursera has many top-quality Data Science certifications, this is probably the most popular of them, given its offered by Johns Hopkins University in the USA.
Many instructors have created online courses teaching you how to use data to get insight into what’s known as data science. I’ve been searching on many online platforms until I saw this Data Science Specialization at Coursera offered by John Hopkins University.
You will see in this article what you will learn in this data science specialization, the instructor's reputation, and the people's review so you can make the right decision to take this course or search for another one that fulfills your needs.
Review of Data Science Specialization by Johns Hopkins University on Coursera - Is it really worth the money?
1. The Instructors Reputation
The course is created by three instructors, and let’s start with Jeff Leek, who is an assistant professor at John Hopkins University in biostatistics specialization and has a Ph.D. in this field.Another instructor is called Roger D. Peng and has a Ph.D. in statistics from California university and works as a professor at John Hopkins University.
The last instructor, Brian Caffo, is a professor at John Hopkins University and a Ph.D. holder from the University of Florida in the biostatistics specialization.
2. The Specialization Content and Structure
This data science specialization focuses mainly on using the R programming language to analyze and visualize the data and create machine learning models. So let’s start exploring the content of this course:2.1. The Data Scientist’s Toolbox
Every trade has its tools, and Data Science is no exception. You need tools to extract, cleanse, normalize, and transform data. You also need tools to visualize and play with the data, and that's where this course helps. Since you are new to the data science field, you will get an introduction to this industry and some tools and platforms that will help you master data science.2.2. R Programming
The second one focuses on programming using the R Programming language since it is the main one to do analysis and visualization in this course and install the environment and packages for statistical programming.2.3. Getting and Cleaning Data
Before you can do your job as a data scientist, you first need to obtain and collect the data, so this course will introduce you to the various ways to get data from databases and the web.2.4. Exploratory Data Analysis
After collecting the data, you need to process it and visualize it, which is all about in this course. You will learn the statistical and visualization packages available in the R language.2.5. Reproducible Research
This course focuses on reporting and getting insight into your data after the visualization and analysis process and other stuff to make the analysis process more effective and productive.2.6. Statistical Inference
This course will introduce you to statistics and inference statistics, drawing a conclusion about a population and some theories such as Bayesian and likelihood.2.7. Regression Models
This course is toward statistical analysis and regression models such as linear and regression models, so it introduces machine learning and regression analysis.2.8. Practical Machine Learning
Here, the fun part is where you will apply machine learning algorithms to your data and prediction functions and some ML concepts such as training overfitting and more.2.9. Developing Data Products
This course is all about creating data products using the R language and some packages such as shiny and leaflets, and you will learn some visualization using the Plotly library. Data Visualization is a key skill, and that's what you will learn in this course.2.10. Data Science Capstone
In the last course, you will do a capstone project where you will develop a data product using what you have learned in the past courses and analyze your data and make predictions, and more.3. The People Review
The specialization gets 4.5 stars out of 5 from around 78k who submitted their rating, which seems pretty good and means the content itself is also good as the videos and quizzes.It also has more than 350k enrollment and forgets to mention that the course is available in many languages such as Chines and Arabic and Spanish, Russian, and more, and the statistics show that 43% of the learners have started a new career in data science. Around 19% have received a promotion after completing this specialization.
That's all about the Review of Data Science Specialization by Johns Hopkins University on Coursera. Nowadays, data science is a high in-demand industry that has become almost mandatory for any company to hire an employee who can take advantage of their data to make the right decision, improve their services, and compete with others in the market.
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