Recently I've created my note of all study materials that I have referred to or I refer to often.
Especially I strongly hope that the study material dashboard helps you have an idea of where to start learning. I also included some inspiring websites. Today I would like to introduce some of my favorite study materials.
Conclusion of this article:
・Search for Super Data Science, take their online course, listen to their podcast.
・Let's Start with Data Visualization because it could boost your data science experience.
I remember that I was inspired by this article: WHY DATA SCIENTISTS SHOULD WRITE MORE
I got to know the Super Data Science Team by an online course in Udemy. The instructor, Kirill Eremenko, he taught me an advanced knowledge of Tableau with real-world data such as data from venture capital, data from coal site and so on. He gave us the big picture first, then he told us what kind of data-viz is required and how to create that. The lecture had an entire story at each sections. I appreciate the lecture.
He is also broadcasting SDS Podcast where he invites a data scientist or person who engages in data science area and holds an interview and a conversation for around 1 hour. It is also helpful to have a sense of what is going on in the edge of the industry. Sometimes I learn a new tool and new movement like SQL Sever Database Manager and #Makeovermonday. It is definitely helpful to stay tuned.
This book is quite powerful because it shows us many example of "Dashboards" instead of showing many example of each data visualization. If you are familiar enough with Tableau or other data visualization tools, you may already have a sense of good data visualizations, but in case you have to create a dashboard, you also have to have a sense of a harmony of these visualizations. The dashboard should be informative, should make the complex simple. If you put too many visualizations, even though each of them are beautiful and fine, the dashboard could be destructive and people would get lost. I think we need to learn how we can organize our visualizations. (If you want to learn each visualization technique I recommend Storytelling with Data)
By the way, I would like to share one article from Super Data Science Team.
I agree with that it's good to start with Data Visualization because
・Everyone can see the (big) data quite easily without hard coding,
・According to CRISP-DM, the first step of data science process should be business/data understanding. Data Visualization helps you to take the steps and you will know where to start,
・According to my experience, if you experienced Tableau, learning SQL can be easier because you can imagine what each query is doing by the image of how Tableau works, seriously.
・Eventually, it's a quite fun to visualize data and turn it into a visible insight. I love it because by doing that data can tell us a story.
So, I introduced the most favorite materials of mine, and as you may notice, basically I recommend you Data Visualization. Data Visualization is very fascinating for me.
We also can use Predictive Analytics, Statistics, Machine Learning. They are also quite interesting. But as a newbie in this area, I am much excited when I 'see' the data, and when I 'see' what the story of the data is. Mostly, as far as I experienced (well, I'm not an experienced person but have seen some kind of data including so-called big data) the data is much complicated having many columns, demographic info, numbers, keys for joins and so on. I strongly believe that if you start with Tableau/data-viz, it could be helpful for beginners to get used to such complex data. I think this is a part of a reason why Kirill calls Tableau as 'Data Science Career Hack'. Let's start Tableau, everyone.
I started Tableau Public Page since December, 2017. Now I have 41 visualizations (half of them are of my horse race project), but the rest half is for my study note from blogs or online courses.
I hope I've been improving my Data-viz skills thanks to these useful materials. Data Science are is quite hot and everyone is like competing but also sharing their knowledge to enhance the industry itself. I strongly feel Data Science never ends, like all dashboards can never be completed, which is mentioned in the Big Book of Dashboard that I introduced.
Let's stay tuned and stay improved. We are on the journey of the big data age.
Next time I may write why Data-viz is required and useful. See you.