Data Science is practical
Science’ in Data Science can be misleading. After all Data Science is a very practical endeavour. You need to code a lot in order to really get it.
So you want to be a better Data Scientist? Code. Gather databases and visualize, analyse, understand it. And then learn how to apply that knowledge to new data sets.
A big part of being a Data Scientist is to make sure you’re doing all your analysis on as much data as possible, because that data is just more data to analyze.
You can always go back and try to get your best set of results again, but as a Data Scientist you really want to get as much data as you can before you start looking at it in a more systematic way.
Start building a Github portfolio
The sooner you start building a Github portfolio of open source projects the better. This way you’ll not only learn a lot, you’ll also get a better chance at finding your next dream job. It hugely increases your visibility as a Data Scientist and allows you to document your Data Science journey.
If you’re interested in working in the Data Science and you’re not sure which company to choose, try and get a quote from a company that is in a very specific niche or category. It’ll be easier to convince the recruiters that you can fill a specific job requirement rather than being seen as a Data Scientist for the masses.
Practically speaking the best way to do that, would be to research the companies in a particular niche — for example through Crunchbase — and then build a portfolio of Data Science projects related to that niche. If that’s niche is a your hobby, it’s a win-win situation.
What makes a good data science project
If it is something you think you can do, or something that you think is a good fit with the data science mindset, and you have the chance to learn the skills and experience needed to do it, take it!
Data science projects are all about learning, and the better the data science skills you have, the more likely you are to build a successful project.
The following is a list of the most important aspects to consider when creating a data science project.
- The project needs to have some sort of measurable result.
- The project should be simple.
- The project should be well understood by the client.
- The project should be in the client’s control.
- The project should be in their best interest.
- The project should provide clear, concise information and results.
You should start a project with the data. It is often the first step. Start with what you know, what you don’t, and the reasons why.
You should have a clear project agenda and timeline.
The project must start with a clear idea of what you are trying to do. If you don’t, you will not have a clear direction.
Experiment
Data Science is all about experimenting. A good tool can give you a better understanding of what your data looks like, but it’s only useful if you can extract insight from it. When looking at a dataset, you need to be able to do a lot more than see if it has a pattern.
To get insight from a dataset, you need to know what to look for. What are the patterns that you can identify and how can you turn that into actionable insights?
If you’ve got your head around these questions, then you can start to get insights that can lead to business outcomes. You can start to use these insights to start to improve the quality of your work and work on your skills.
Once you’ve got a solid understanding of what you’re looking for, there are 3 ways you can go about getting insight from your data.
Finally, one of the best ways to look at your data is by creating your own report. Have a spreadsheet where you note everything you did in a project. This way you’ll keep everything well organized, you’ll learn a lot and be ready to tackle more complex projects.
Good luck!
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If you want to read more about Data Science I recommend these texts:
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