Recently, I was approached by a work colleague who has a mutual interest in Data Science. He wanted to know about my plans for becoming a Data Scientist, and if I could lend some insight to his plans. So I thought, why not make a post here to share our findings and help someone else in their journey! (I’ve included a bunch of links to resources that have been helpful to me, or have come highly recommended!)
Disclaimer: Please note, I am not an expert on Data Science (yet!), I’m currently just a student learning and working in Data towards a goal. If anyone reads this and has better ideas on how I should be going ahead in my path to Data Science, by all means reach out to me, maybe we can learn from one another!
The Roadmap
(AKA What does a Data Scientist look like?)
The first thing you should do when you set out on any kind of journey is have a solid idea of where you are going. This is the Data Science Roadmap.
These are the kind of skills that make up a Data Scientist. Over time I have sourced items on this list from job advertisements for Data Scientists, colleagues and Data Science podcasts, among other places.
Programming Languages
- Python
- R
- SAS
- SQL
Statistics
- Statistics Degree/Masters/PHD
- Bayesian
- Probabilities
- Regression
Machine Learning
- Deep Learning
- AI
- Supervised
- Unsupervised
Business Intelligence
- Business Analysis soft skills
- BI Visualisation tools like:
- Power BI
- Tableau
- D3.js
Big Data
- Understanding of Cloud
- Microsoft Azure
- Amazon AWS
- Google Cloud Platform
- Hadoop
- Mapreduce
Supplies for the Journey
(Resources that have helped me along the way)
Before you set out for a journey (and along the way) it is always a good idea to gather supplies. My resources have come from podcasts, blogs and people I’ve met on the same journey.
Here’s what I’m doing to work toward becoming a Data Scientist:
Studying – Business & IT Degrees
I’m currently working through my double degree in Business and IT (Data Analysis) at the University of South Queensland (USQ), studying online.
Listening to podcasts
- Super Data Science Podcast (Kirill Eremenko)
- Becoming a Data Scientist Podcast (Renee M. P. Teate)
Taking online courses
- Machine Learning – Andrew Ng (Coursera)
- Machine Learning – Kirill Eremenko & Hadelin de Pontevez (Udemy)
- Deep Learning – Kirill Eremenko & Hadelin de Pontevez (Udemy)
- Artificial Intelligence – Kirill Eremenko & Hadelin de Pontevez (Udemy)
- Python for Data Analysis – Jose Portilla (Udemy)
Reading books
- The Elements of Statistical Learning – Trevor Hastie
- An Introduction to Statistical Learning (With applications in R) – Trevor Hastie
- Naked Statistics – Charles Whelan
- Dataclysm – Christian Rudder
- DMBOK – Data Management Book of Knowledge
Kaggle
Stack Overflow
- Solve other peoples’ programming issues to practice my own skills
- Invaluable resource for understanding Excel, Python, VBA and more!
If you’re on the same journey as me, please reach out on LinkedIn, or in the comments below. I’d love to continue to share resources with other people on the road to becoming a Data Scientist!
This list is always expanding and changing as my knowledge of Data grows. I hope these resources are as helpful to you as they have been to me.
girl vs. data 