Data Analytics Project
Setting out to do a Data Analytics or Business Analytics Project?
You need to master some steps to produce an amazing project. Your project must not start as a big project but you can start from building beginner projects down to advanced projects according to how knowledgeable you are about the subject matter.
In this article, we highlighted some steps to take to accomplish an outstanding data analytics project
- Decide what type of Project To Do
The first thing you should consider is what problem you want to solve, a project that does not solve a problem now or in the future is fancy but useless. You should also consider the gains of investing time and resources into the project. You also need to know the tools and collaboration you need to get the project done. You can start with just a Minimum Viable Product (MVP) to demonstrate the problem your project solves.
2. Collect your Dataset
The second thing to do after deciding on the project you want to do is to get your dataset. What type of data will you need for this project, will you collect fresh data by yourself or you will use pre-existing data to build or prototype your project. You can get quality datasets from websites such as Kaggle.com, data.world, zindi.africa and other data hosting websites, etc.
3. Choose your Tools or Programming Languages
You need to process your data and do serious analysis and modeling. Here you need to decide which Integrated development environment which is also known as IDE to use. It is a software or environment that allows programmers to write computer programs in R, Python e.t.c. to execute analysis on their data and build applications, examples of IDEs are VSCODE, Anaconda, Google Colab, Pycharm, Spyder e.t.c.
If your data analytics project will not require programming but a dashboard, you can try Power BI or Tableau or worksheets like Excel.
4. Make a Project Plan/Timeline
Make a project plan. Adopt an Agile project plan which is common in most data science projects. It is advisable you do it one step at a time hitting every milestone at the required time without pressure.
5. Make a Project Technical Document
Documentations are highly important in technical projects, always note down what you are doing, when you are doing them, and what you intend to achieve at the end of the day. Documentation goes a long way in helping you understand your project even better, it can also help you when you may need someone to collaborate with on a project or when you want someone to take up the project from where you stopped.
6. Share your Project.
You should consider sharing your project and be open to critics and recommendations as this will help you get better in your next project. That way you can get motivated, find people to collaborate with, and learn from others.
Building projects are every data scientist’s treasure. They show your capability to solve an industry problem using data. They can also be added to your portfolio and it gives you a boost when companies or recruiters are looking for competent hands. Endeavor to build as many projects as possible to add to your portfolio.