Citizen data scientists are becoming more essential than ever to organizations that rely on data. If you’re a citizen data scientist or an organization that is looking to upskill your team members, there are some great tools out there for taking their data science skills to the next level. Making the jump from Excel to data science tools like Python can empower citizen data scientists to do even more with the data your organization collects. In this blog, we’ll outline some of the best tools and Python packages to boost the skills of your citizen data scientists!
What Is a Citizen Data Scientist?
While the role doesn’t have one clear definition, a citizen data scientist is generally someone who doesn’t have a formal degree or training in data science but is proficient in using data tools and analyzing data. Many industries are increasingly recognizing the value of upskilling certain team members to citizen data scientists. While having professional data scientists on staff is essential, citizen data scientists can help take on simpler data science tasks and create a more data-driven culture within your organization. They can help to identify trends and patterns and independently make data-driven decisions to help grow the business.
Why Is Python a Perfect Fit?
If you’re working on becoming a citizen data scientist or upskilling team members to become citizen data scientists, Python is the ideal programming language to get them there. But why Python? For one, Python is an open-source language that has numerous free libraries, tools and learning resources that your team can access as they build their skill set.
Python is also considered a beginner-friendly programming language since it has simple and clean syntax. However, simple doesn’t mean that it isn’t robust. Python is an incredibly versatile and powerful programming language that can be mastered for even more complex tasks. In short, Python is a widely used programming language that has a multitude of use cases for beginners, intermediate users and experts alike.
Overcoming the Python Code Learning Curve
If you’re completely new to programming languages, it can still be fairly intimidating to dive in and start learning. Fortunately, Python has a robust and inclusive community that is more than happy to help beginners make the jump. You can find forums, classes, communities, tutorials and much more for the low price of completely free.
There will still be a learning curve, especially if you’re making the jump from a no-code tool like Excel to the more robust Python data science tools. But some tools can make this jump much easier and less intimidating.
Top 5 Beginner-Friendly Data Science Tools
The vast number of resources out there is helpful, but it can also be overwhelming. It can be difficult to know where to start. That’s why we’ve outlined the top 5 beginner-friendly data science tools that will help you on your journey to becoming a citizen data scientist.
1. Pandas: Data Manipulation and Analysis
Pandas is easily one of the most powerful data manipulation and analysis libraries in Python. Pandas can simplify many of the tedious and time-consuming tasks that are essential for proper data analysis. Thanks to Pandas’ DataFrames data structures, it can simplify tasks like data organization, cleaning, transformation, visualization, merges and joins inspection and much more.
Pandas also offers a simple and intuitive API, making it easy to get started with data analysis and manipulation tasks right away. Using Pandas, citizen data scientists can conduct a wide range of analyses, create thorough reports, gain insights, explore patterns and discover trends. Overall, it’s a versatile and indispensable tool for citizen data scientists and data scientists in general who use Python.
2. Jupyter Notebook: Interactive Data Science Environment
Jupyter Notebook is a free, open-source tool that provides an interactive environment for creating and presenting data science projects. In Jupyter Notebook, citizen data scientists can perform numerous tasks, such as running code, creating and sharing documents, running data analyses and creating compelling visualizations.
Jupyter Notebook is a frequently used tool because it’s intuitive and it is great for creating visually stunning presentations that make communication for data projects much more straightforward. Combined with data visualization libraries like Plotly, which we’ll touch on soon, you can make insightful data visualizations for all stakeholders.
The sharing code feature also makes it extremely easy to collaborate with others, speeding up workflows and enabling real-time collaboration between data scientists and other stakeholders.
3. Mito — AI, Code-Gen and Spreadsheet GUI
Mito is a tool that is quickly gaining popularity for its data science capabilities. Mito is a powerful Python-based spreadsheet tool that combines AI and code generation with a familiar spreadsheet GUI. It’s the perfect tool for citizen data scientists who are making the jump from Excel to Python-based data science tools.
This is because Mito allows users to edit and manipulate data in a spreadsheet format and then uses AI to generate the corresponding Pandas code automatically. This helps new Python users learn the language while also giving them the ability to make use of Python’s data capabilities sooner. It’s also great for expert users who want to save some time by expediting coding tasks.
Overall, Mito is a powerful and user-friendly data science platform that makes it easy for anyone to unlock the power of Python. If you're a citizen data scientist who wants to take your data analysis to the next level, Mito is worth checking out.
4. Plotly: Interactive Data Visualization<
Data visualization is an integral part of data analysis and communication. Plotly is a great addition to your data analysis toolkit, as it provides a user-friendly library for creating interactive data visualizations. Plotly gives you the tools you need to create charts and graphs, such as scatter plots, line graphs, bar charts, heatmaps and more.
As mentioned earlier, Plotly can also seamlessly integrate with Jupyter Notebook, so you can enhance your visualizations and make them easier to share and collaborate on.
In short, Plotly’s numerous customization options greatly simplify the process of creating visually stunning and engaging charts. Don’t underestimate the power of visualizations when it comes to data analysis!
5. SciPy: Scientific Computing
Finally, you’ll want a beginner-friendly tool that can help you with complex scientific computing tasks. For that, consider adding SciPy to your data science tool belt. SciPy is an open-source library that is built on top of NumPy, which is an efficient and fast mathematical library for Python. With access to this tool, you’ll have a vast number of scientific and mathematical computing functions at your fingertips. Functions such as optimization, integration, linear algebra, statistics and more.
With the SciPy library, you can easily perform complex mathematical calculations that would normally be challenging and time-consuming to implement on your own. Especially if you're new to using Python. Citizen data scientists can greatly benefit from using SciPy when they’re learning to analyze and model data in Python.
Ready to Exceed the Limitations of Excel? Try Mito for Free!
You don’t have to be held back by Excel’s limitations when you use Mito! Anyone can become a citizen data scientist with the right tools and the right resources. Mito is the best tool for making the transition from Excel to the much more robust Python data science tools. With Mito, you can work in a familiar spreadsheet format as Mito automatically generates corresponding Pandas code for you. As you work, you can see how Mito works, learn the process and learn how to use Pandas code to make the most of Python’s data science capabilities. Best of all, you can try it for free! Get started with Mito today.