Data science is rapidly growing in popularity both in companies and in academia. So what exactly is Data science and how does Codio take Data science out of the realm of the weird and wonderful and help students get their hands dirty?
We'll explain this, and much more, in this blog.
Data Science Tools from Codio
To begin, we'd like to direct you to two important resources.
The first is our R Course for Data Science Essentials. This course offers instructors a modular format for data science teaching that can be re-ordered, re-named or have units removed as required. Plus, Codio's interactive interface ensures that students are constantly engaged as they type code directly into the page.
The second resource is our Essential Data Science in Python program, which offers custom code editor integration with each page of content, so you can see for yourself how the computer responds to code. The program also provides code snippets to get students started as well as suggested avenues for investigation.
Each of these tools acts like a textbook, but with the ability to be updated on a regular basis and the flexibility to be used in different ways by instructors. Best of all, they're free for professors to use in their courses.
We encourage you to explore both tools as you move through your teachings.
What is Data Science?
Data science is a combination of techniques and tools that allow you to take data sets of any size and any kind and extract meaning from them.
It uses techniques and methods from many different fields, including mathematics, statistics, computer science, data mining, databases, and data warehouses. Data science encompasses a wide array of subdomains, including statistical learning, ensemble models, computational statistics, Bayesian statistics, and statistical computing.
According to Wikipedia, “Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings.”
Data scientists are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and produce and present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.
What Are the Steps to Learning Data Science?
Learning data science can be difficult, but it is possible to learn with some effort and guidance. Here are five steps that can help you coach your students through the data science learning process:
- Step one is to figure out what you need to learn. Data science is a vast field, and you will not be able to learn everything at once. It is important to focus on the basics first and then expand your knowledge as you become more comfortable with the material.
- Get introduced to common data science languages. Python is a programming language that is commonly used in data science. R for Data science is another popular programming language among data scientists. While it is not as widely used as Python, it is still worth learning for data analysis and manipulation.
- Learn data collection, cleaning, analysis, and manipulation. Tools like Pandas and NumPy will be extremely helpful for data manipulation and analysis.
- Understand machine learning in more depth. There are many different types of machine learning, and it is important to understand the differences between them. For example, supervised learning is where the data is labeled and the algorithm learns from this data. Unsupervised learning is where the data is not labeled and the algorithm has to find patterns in the data.In data science, it is important to be able to use both types of machine learning.
- Keep practicing and learning. Data science is an ever-changing field, so it is important to keep up with the latest trends and technologies. There are many online resources, such as blogs, forums, and online courses, that you can recommend to your students so they stay up to date.
How Do You Introduce Data Science to Students?
There's no doubt that data science is a complex and rapidly-growing field. So how do you introduce data science to students? Here are a few tips:
- Start with the basics. Before diving into the more complex concepts, make sure your students understand the basic principles of data science. This includes topics like data collection, cleaning, and analysis.
- Use engaging and real-world examples. When teaching data science, it's important to use examples that will capture your students' attention and keep them engaged. Choose examples that are relevant to their lives and interests.
- Make it interactive. Data science is an interactive field, so your teaching methods should be too. Encourage your students to ask questions, work on projects together, and get hands-on experience with data.
- Use the right tools. There are a number of different software programs and programming languages that data scientists use. When teaching data science, make sure to use the tools that your students will be most comfortable with.
What Tools are Widely Used When Teaching Data Science?
There are a wide variety of systems, platforms, and tools that can be used to address Data Science requirements. Which tools you use depends very much on what format your data is in and where it is stored, what you already know and love, and what you don’t yet know but ought to.
- Databases: data is often stored in SQL and NoSQL databases and so some familiarity with database queries is important to gain access to the source data in the first place and massage it into the correct format for later-stage analysis tools.
- R: the R programming language is now the most popular language for processing data and visualizing the results.
- Python: The Python language has a very rich set of tools and modules aimed specifically at data science. iPython/Jupyter Notebook is widely used for teaching purposes and libraries such as NumPy, scipy, matplotlib, and many others provide a rich ecosystem.
- Matlab, SPSS and SAS: these and other tools are high-end and expensive tools that are custom written for statistical analysis. They are widely used in the corporate environment and also in academia where academic discounts are available.
- Hadoop and Spark: these are open-source software framework applications for distributed processing of large data sets. They can scale from a single machine to clusters containing thousands of servers. They use simple programming models but are non-trivial to configure and use.
- Excel: you would be surprised just how much can be accomplished with Excel for smaller data sets and tasks. However, we would not consider it a full component of the Data Science tool chest.
How Can Codio Help You Teach Data Science?
Codio provides very considerable assistance for both teachers and students. We help not only with a massively scalable cloud infrastructure but also with teacher support features, a browser-based IDE, class management, LMS integration, and many other features designed to accelerate the pace of teaching and learning.
Codio Technologies
Codio gives students any number of Cloud-based servers with an automatically attached IDE. Each server is a full Ubuntu server with sudo-level access. Where you install components or complete Data Science environments manually or instantly grab them from a Codio template, you can do whatever you like with a Codio project.
You can install any database, programming language, platform, or component that can be installed on any regular Ubuntu server. Each server also gets its own domain name and is fully web-facing, so applications like Jupyter Notebook and R-Studio Server run with no problem.
You can even teach and install Hadoop and Spark for distributed processing applications and Big Data scenarios.
Codio for Teachers
Teachers of data science will find Codio seriously streamlines their workflow and student interactions, allowing them to spend a lot more time with the students and a lot less on setup, configuration, and administration.
- A teacher can create and snapshot custom configurations for commonly used Data Science tools.
- Snapshots can be taken off the shelf by students, avoiding the considerable time associated with environment setup.
- Each project created by a student is a full-fledged Ubuntu server in the Cloud with a Web IDE attached.
- There is no limit to the number of configurations a student can use. Each project is fully independent of others, avoiding the many types of conflict that can occur when trying to install and maintain multiple configurations on a single machine.
- There is no need to prepare environments within a CS lab. As everything is set up by a lecturer, runs in the cloud, and is browser-based, your CS lab machines only need a browser with nothing needing to be installed.
- Students can use their own PCs, reducing the need to invest so heavily in CS computers and also allowing students to work from home.
- Teachers can assign any configuration at any time to an entire class so students get their own machine to work on.
- From Codio’s LMS area, teachers are able to instantly access any student’s environment and assess, grade, or assist.
Codio for Students
There are many advantages for students, too.
- Access course materials or their own projects from any machine, not just CS lab computers.
- Instantly create Data Science environments by using Data Science stack templates or by custom creating their own Stack.
- Create any number of Data Science projects without worrying about the cost. Codio’s container technology means that whether you have one project or ten, the cost is the same. That's not something you can achieve with VMs either.
How to Teach Data Science: A Big Data Case Study
If you would like to hear how Codio is used to teach Big Data using Hadoop and Spark, please read the Kent State Big Data case study.
Can You Self-Teach Data Science?
Of course, you can learn just about anything online these days, especially when using the Codio platform. With our interactive and engaging learning environment, you can learn data science on your own time. Codio offers Data Science courses for learners through Coursera. Access our self-paced course library and set the category filter to “Data Science.”
If you are an instructor looking to teach Data Science or Big Data, Codio offers many features and resources for both students and teachers that allow you to concentrate on doing rather than configuring.
Our free instructor account allows you to experience the Codio platform without restriction. Get started today.
