How do I become a data scientist? How do I get real-world project exposure? I have already done some basic courses; how do I solidify my understanding of the advanced topics? I don’t have a data science degree; how do I showcase my skills to my potential employer?
These are some of the questions I get asked repeatedly on LinkedIn. I understand the common concerns since I had them too. I don’t have the universally accepted best solution for these concerns, but I can share what I did when I was in a similar situation.
I took a 3 step approach to data science — Learn, Create and Share. In short, the idea is everyone was a beginner once, and you need to start somewhere. So you learn and create projects and share them with the community. As you consistently do this over time, your work becomes your portfolio.
The data scientist nanodegree is an important aspect of my journey because it encourages everyone to learn the topics, create projects and share them with the community. It synced so well with my approach.
In this article, I’ll detail why I chose the nanodegree to everything I did to complete it within 30 days. By the end of it, you’ll have an action plan to have your name too on the certificate.
Starting with the why
Why do you need to complete the nanodegree in 30 days? To me, I saw tremendous value in the degree. It aligned with my approach to breaking into the data science industry.
However, with great value comes great investment. I couldn’t afford to pay $1600 for the recommended 4 month period. I took the pay-as-you-go option, which means the sooner I finish, the lesser my investment.
That was me being honest with you. Now, it’s time you be honest with yourself:
What’s your “why”?
Do you want to build a portfolio? Do you want to learn advanced topics? Do you find it expensive too? Write them all down on a piece of paper. We often lose the motivation down the journey, and Simon Sinek recommends we always start with identifying our “why.”
If you can afford to pay for more months, there’s nothing wrong with having some more breathing space. This article will focus on keeping up with the pace of 1 month, but you can apply the same strategies for 2 or 3 months.
Completing the pre-requisites first
We need to understand that this is an advanced program designed to prepare students for Data Scientist jobs. Hence we should have a high comfort level on basics before starting the program.
Luckily these courses are available at Udacity itself for free, which I completed ahead of enrolling.
- Basics of Python
- SQL for Data Analysis
- Intro to Descriptive Statistics
- Intro to Inferential Statistics
- Basics of machine learning
I see most beginners making the same mistake of learning these free courses after enrolling. Why would you pay for the same course that’s already available for free?
I recommend you skim through this upfront and familiarize yourself with the topics before starting with the nanodegree.
Now, we’re all set to start learning the nanodegree!
Adopting the “Project-First” approach
Unlike other platforms such as Coursera, EdX, Udemy, or Datacamp, Udacity follows a project-based learning style. The idea here is that our learnings will be based on real-world projects, and we’re evaluated on how well we apply what we learned on the project.
These projects are graded, and we need to meet the requirements to pass them. Let’s focus on the mandatory projects now:
- Write a data science analysis report — You’ll analyze any dataset of your choice by applying the famous CRISP-DM methodology and present this report in the form of a blog post.
- Build disaster response pipelines — You’ll be using both data engineering and machine learning to classify emergency messages from major natural disasters around the world.
- Design a recommendation engine — You’ll be building simple recommendations based on the user behavior of a real social network.
- Data science capstone project — This is an open-ended end-to-end project where you’re expected to apply all the elements of the data science lifecycle from problem definition to deployment.
There are many other optional projects which are not reviewed (more on this later.) Since I knew what to expect from the projects, I better absorbed the course content. That’s the beauty of the project-first approach.
Understanding the projects and what you’re evaluated on helps you view the course content more mindfully.
Borrowing the brains
Something I learned throughout the data science journey is to seek help early in my career. Learning from experts ahead of me, without a doubt, has accelerated my learning journey.
The nanodegree comes with a community of learners who are learning alongside us. In addition, we get access to mentors who are experts in the relevant fields.
As soon as I enrolled, I scheduled a call with my mentor and cleared all my doubts regarding the program. Most of what I’m writing about in this article is all thanks to them. Whenever I was stuck in a project, I’d ask a question and interact with the learning community.
Nothing will be handed over to you in a silver platter; you should always ask. After all, you’re paying for it! Without a doubt, it fast-tracked my completion.
Creating my personalized graduation blueprint
All advice and no action don’t give us any results.
This is the most important step of all. I created something I called the “graduation blueprint,” which outlined my exact schedule for learning and implementing the project for the month.
Every week I’d start by viewing the project outline and the course content from Monday to Friday. I could only dedicate 1–2 hours as I was working full-time. During the same weekend, I’d spend about 10 hours and complete the project.
I’d submit projects during the weekend and move on to the next section. In a nutshell, for 4 projects, I took 4 weeks, with weekdays for viewing the lectures and weekends for implementing them.
I left a couple of days buffer for the end of the month since the project needed to be reviewed before the end of 1 month. I don’t recommend you follow the same plan; I recommend creating something on your own because you know what works best for you.
What’s important is to ensure you create a personalized graduation blueprint and religiously follow it for that time period.
Making the most beyond graduation
Suppose you have followed the blueprint and completed the nanodegree within the period; congratulations! You should see the excitement on my face when I finally graduated with the data scientist nanodegree.
We indeed rushed to complete the nanodegree, but we retained lifetime static access to the course content. So let’s go back and complete the projects which we skipped that were optional:
- Creating a pip package
- Building web applications
- Experimental design for promotions strategy
- LinkedIn and GitHub review
These are excellent projects and are a great addition to your portfolio, so let’s not miss out on them.
In hindsight, the nanodegree was pivotal in solidifying some of the foundations I already had. A field like data science constantly requires us to learn and be updated with the industry’s trends.
In the end, remember your learning journey is in your hands.
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