Advice for the execution of your resume.
1. The concepts will come faster than you can learn.
There are literally thousands of web pages and forums that explain the use of common data science tools. For this reason, it is very easy to get a side monitor during online learning.
When you start looking for a topic, you need to keep your goal in mind. If you do not, you risk getting caught up in any catchy connection.
The solution, get a good storage system to save interesting web resources. This way you can save material for later, and focus on the topic that is relevant to you at the moment.
If you do it well, you can create an ordered learning path that shows you what to focus on. You will also learn faster and avoid being distracted.
Warning, your list of readings will grow rapidly in the hundreds as you explore new topics that interest you. Do not worry, this brings us to my second piece of advice.
2. Do not stress. It's a marathon, not a sprint.
Having a self-guided education can often seem like trying to read an endless knowledge library.
If you're successful in data science, you need to think about your education as a lifelong process.
Just remember that the learning process is his reward.
During your educational journey, you will explore your interests and find out more about what drives you. The more you learn about yourself, the more you will enjoy the pleasure of learning.
3. Learn -> Apply -> Repeat
Do not be content to learn just one concept and then move on to the next thing. The learning process does not stop until you can apply a concept to the real world.
Not all concepts must have a dedicated project in your portfolio. But it is important to remain rooted and remember that you are learning so you can make an impact in the world.
4. Create a portfolio, show others that they can trust you.
When it comes to it, skepticism is one of the greatest adversities you will encounter in learning data science.
This can come from others, or it can come from you.
Your portfolio is your way of showing the world that you are capable and confident of your skills.
For this reason, creating a portfolio is the single most important thing you can do while studying data science. A good portfolio can get you a job and make you a safer data scientist.
Fill your portfolio of projects you are proud of.
Have you built your web app from scratch? Have you created your own IMDB database? Did you write an interesting analysis of health data?
Put it in your wallet.
Just make sure that the annotations are legible, that the code is well documented and that the portfolio itself looks good.
This is my portfolio. A simpler way to publish your portfolio is to create a GitHub repository that includes an excellent ReadMe (summary page) and relevant project files.
Here is an aesthetically pleasing but simple GitHub portfolio. For a more advanced portfolio, look into GitHub-IO to host your free website. (example)
5. Data Science + _______ = A passionate career
Fill in the blanks.
Data science is a set of tools aimed at making a change in the world. Some computer scientists build artificial vision systems to diagnose medical images, others run through billions of data entries to find patterns in the user's preferences of the website.
Data science applications are endless, which is why it's important to find out which applications excite you.
If you find topics that you are passionate about, you will be more willing to commit yourself to a great project. This leads to my favorite tip in this article.
When you are learning, keep an eye out for projects or ideas that excite you.
Once the learning time has elapsed, try connecting the points. Find the similarities between the projects that fascinate you. Then it takes a while to look for industries that work on these types of projects.
Once you find a sector that you're passionate about, make it your goal to acquire the skills and technical skills needed in that area.
If you can do it, you will be ready to transform your hard work and dedication to learning in a passionate and successful career.