A Review of Udacity’s Data Science Courses — Recommendations based on my experience completing 4 courses in 2021
Nowadays, the term “Data Science” is becoming a popular topic, and many people may make it one of their choices when considering their future career path.Data science? and a Google search will find a variety of online learning sites along with an introduction to its meaning. There are free learning materials on YouTube, and there are paid learning materials on Udemy, Data Camp, Coursera, and many others, making it difficult to choose. Yes, it was the same for me when I just started in January 2021.
Fortunately, I was able to take Udacity if I wanted to at Mazda, where I work, and I have completed four courses this year.For my certificate of completion, please see my profile at the link below.
For those of you who are thinking about which one to take, I thought I would review each course from the following perspectives.
What you will learn from reading this article ?
- Who should attend the course?
- Prerequisite skill levels
- Advantages/Disadvantages
Programming for Data Science with Python
- Who should attend the course?
This course is perfect for people like me who are new to Python and starting their data scientist journey. If you are thinking about taking this course after reading the syllabus, you should take this course first.
- Prerequisite skill levels
You don’t need any special skills, but make sure you take the time to study, and Udacity has various ways to help you develop study habits after the course. Udacity has a lot of ways to help you get into the habit of studying after you take the course, such as planning and reminders. But at the end of the day, you still need to do it.
- Advantages/Disadvantages
You don’t need any special skills, but make sure you take the time to study, and Udacity has various ways to help you develop study habits after the course. Udacity has a lot of ways to help you get into the habit of studying after you take the course, such as planning and reminders. But at the end of the day, you still need to do it.
Data Analyst
- Who should attend the course?
This course focuses on data analysis, which is the entrance to data science.
This course will provide students with the knowledge of statistics, data wrangling, and visualization necessary for data analysis using Python. In particular, the concept of A/B testing is difficult to understand at first, but it provides a strong foundation to help you learn later on. If you are an aspiring marketer, knowledge of A/B testing is very important. If you are planning to think in terms of data, you will be able to acquire essential knowledge for yourself.
- Prerequisite skill levels
If you are taking this course, you might want to reconsider a bit if you are new to Pytrhon. For example, you may be wondering how to implement a function in Python. I started the course in such a state, so I had some difficulties. Data wrangling, in particular, may seem like a very high hurdle to overcome, but it is a basic skill that should be called “education” when dealing with data, so do your best to finish the project!
- Advantages/Disadvantages
This course is a gateway to data science, so I think it is very useful and I hope you will consider taking it. If there is anyone I would not recommend this course, it would be someone who wants to learn machine learning. Next, I’ll introduce a course for you.
Machine Learning — Introduction with PyTorch
- Who should attend the course?
This course will focus on machine learning with Python liblary such as scikit-learn, PyTorch. In some regions, Tensor Flow is more common than PyTorch, so Tensor Flow can also be selected.You will learn the basics of supervised learning, unsupervised learning, deep learning, the difference between each model, and the skills of model evaluation and tuning, which are very important in practical use. If you think this is it, just take a look at the next section.
- Prerequisite skill levels
From now on, it’s all about machine learning techniques! If you choose this course because you think it is, you will need data analysis techniques for your final project. Since this is the last project, you should be familiar with Python, but data analysis techniques are overwhelmingly more important than Python skills. The reason why I wrote in the previous section that data analysis is the entrance to data science is based on my experience here.
- Advantages/Disadvantages
This course will give you the freedom to work with the Python library for machine learning. However, in my opinion, if you are just starting to learn machine learning, choosing other simple AI courses would be a better option than this course. On the other hand, if you already have an understanding of machine learning, you may find this course a bit lacking.
Data Scientist
- Who should attend the course?
Thank you for reading this far. This course is
This course is designed for people who have a strong interest in data science and can successfully complete the above course. The course outline includes projects that allow you to apply data science to real business processes, natural language processing, business A/B testing experiments, recommendation systems, and big data applications. And most importantly, it was the most fun course.
- Prerequisite skill levels
It is obvious that Python needs to be freely available, and if you don’t have experience in practical use of data science, you may not want to take this course. However, if you have a strong interest in data science, you should definitely take this course. The projects themselves are not too difficult, except for the last project. The projects themselves are not too difficult, except for the last one, but I think what you need is an insatiable interest in data science.
- Advantages/Disadvantages
This course is very good if you want to experience the whole project using big data. The real big data is more than 100GB of text-only data, so it is very fun to understand what S QL, PySpark, and Pandas are good at and then analyze the business-critical “churn”. I can experience a project to analyze user behavior based on time and page access history and put it together as a predictive model. I believe that gaining the above experience, in addition to gaining practical experience in data science, will lead you to find new possibilities in your future.
Next
After I finished the Data Scientist course, I was amazed at how much better my work was going. If things aren’t going well now, wouldn’t it be good to get the power of a game changer?Is there such a thing? No, no, no. The game changer for me, of course, is