Don’t we all wish that our days were longer than 24 hours? So that we can fit more stuff in every day? But, unfortunately, time today is – and always has been – very tricky to handle, especially if you’re trying to squeeze in learning a new skill into your already full daily schedule. That’s why it’s always a great thing to find a resource that can teach you something new in a relatively short amount of time.
When I want to learn a new skill, library, or programming language, I would have to do that either after work (which is not ideal) or on the weekend. And often I try my best to learn all the essential knowledge within the weekend. However, we all know that Data Science is a broad field with many specific topics underneath.
Wouldn’t it be great if you can learn a new skill or concept every day? And then move on to the next concept? For example, I know that you can learn the basics of any data science concept that can be learned within a day – or 24 hours. Then, once you learned these basics well, you can build up this knowledge and start building applications.
In this article, I will share with you 5 online courses, all available on Coursera, and all are short, informative, and can be finished in one day. These courses are a great option if you want to learn something on the weekend but don’t feel like registering for a 50+ hours course.
№1: Foundations: Data, Data, Everywhere
No better way to start a list of data science courses than with a data analysis one. The first course on the list is one offered by Google, and it is an introductory course in data analysis. Foundations: Data, Data, Everywhere is the first course of a series of courses meant to prepare you for the Google Analytics Certificate.
This course is divided into 5 modules, collectively designed to be done in 20 hours. During these 20 hours, you will learn the key skills of analyzing data, such as cleaning and visualizing the data, understanding basic tools you will need to do so, like SQL, R, and Tableau and setting up a professional, efficient developing environment.
№2: Introduction to Machine Learning in Production
Next up is a course intended for those with some knowledge of deep Learning, Python, and AI. Introduction to Machine Learning in Production is a course offered by DeepLearning.AI and is the first course in their series about machine learning production engineering (MLOps). This courses series is meant to take you beyond implementing a machine learning algorithm and prepare it to be released.
This introductory MLOps course is divided into three sections, and all are meant to be completed within 10 hours of learning. You will learn the production engineering skills you need to be a successful machine learning specialist through those three modules. In addition, you will learn ML lifecycle, how to solve any problem in a structured way, and how to perform efficient analysis to understand your ML model.
№3: Introduction to Data Engineering
Every data scientist I know, including myself, has been asked probably more than once, "So what does a data scientist do?". The simple yet, not really simple question is one of the reasons why all data science roles naming are extremely confusing. One of these confusing titles is Data Engineer. The next course on this list, Introduction to Data Engineering, should helo you answer that question.
IBM offers this course and is divided into 4 modules that can be completed within 10 hours. In this course, you will go through the basic concepts, tools, and data engineering processes. You will also understand the modern data ecosystem and how do data scientists, data engineers, and data analysis interact in this ecosystem.
№4: Analyze Datasets and Train ML Models using AutoML
Next up is another machine learning-related course. Analyze datasets and train ML models using AutoML. DeepLearning offers this introductory course. AI and AWS and focus on preparing your dataset for ML model training using AutoML. To take this course, you need to have some fundamental Python and Jupyter Notebook skills.
This course is divided into 4 modules and is meant to be completed within 14 hours. It is the first of a series of courses that teach you all the fundamental knowledge you need to apply exploratory data analysis (EDA), use AutoML on your models, and some text classification algorithms. At the end of this course, you will be able to build, train, and deploy scalable ML pipelines using the AWS cloud.
№5: Google Cloud Big Data and Machine Learning Fundamentals
Last on this shortlist of short courses is a course about Big Data and Machine learning. Google Cloud Big Data and Machine Learning Fundamentals is an intermediate-level course part of many Google offered courses related to data engineering and data analysis.
This 4 module course is meant to be completed within 14 hours and will introduce you to the big data capabilities of the Google Cloud Platform. This course will take you through using Cloud SQL to migrate your existing MySQL workload to the Google Cloud for a faster and more efficient pipeline. You will also learn how to query the data and perform interactive analysis to understand your data better.
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Takeaways
Data science is a term that is often used to describe many concepts and terms simultaneously. Data science is a field that contains many smaller areas and concepts. To become a data scientist or dive into the data science world, you will need to learn all these smaller areas and concepts.
I would argue that you can learn all the basic information you need for each small area or concept within 24 hours. That’s a dedicated 24 hours of learning. It’s often great if you can find resources designed to be learned within 24 hours because we are all aware that finding resources needs 24 hours of its own – if not longer, sometimes.
That’s why I decided to share with you some online courses that are designed to be short, concise, to the point, and most importantly, can be done, start to finish within 1 day. So, next time you want to learn something new and only have the weekend to do so, give one of the courses in this article a try.
