Creating a Resume is the first step in applying for any job role. Summarizing your education, skills, projects & experience is a difficult but necessary task. I’m writing to share a step-by-step process to craft an impressive resume for data science roles.
Follow along with this video to create your resume step-by-step from scratch: https://youtu.be/h6XRPmSBEM4
How Recruiters and Employers Evaluate Resumes
To be an effective data science or machine learning practitioner, it’s essential to have some soft skills, apart from the technical knowledge of libraries, frameworks, and algorithms.
Projects often fail due to a lack of clear communication.
If you’re pursuing a career in data science & AI, the Data Analyst role is great starting point. Here’s what you need to know to become a data analyst:
Making a career transition to a new field like data science or machine learning can seem daunting. Indeed, landing your first job is the hardest part. You may be wondering:
What should I learn? Which courses should I take? What projects should I build? How to apply for jobs? How to prepare for interviews? What to do if I’m constantly getting rejected?
Here’s a 5-step process that you can follow to land your first data science job:
Programming is the process of turning your thoughts & ideas into instructions for a computer to follow. Mastery over a language like Python allows you to write code and build anything you can imagine! It’s the closest thing to a superpower.
We’re kicking off a new course called “Data Structures and Algorithms in Python” to help you improve your coding skills. Learn more at pyalgorithms.com .
Not surprisingly, the best way to get better at coding is to write code regularly
Before you start applying for data science jobs, make sure to complete at least one project in each of these three important domains:
You can host these projects on your Github/Jovian profile. Here’s mine:
Check out these projects for inspiration:
1. Analyzing your WhatsApp messages by Michael Chia Yin
2. Understanding your Browsing Patterns using Pandas by Kartik Godawat
3. What Makes a Student Prefer a University by Daniela Cruz
Here are the steps for building a project on EDA & visualization:
1. Find a…
I think Udacity Nanodegrees are quite expensive (around $315 or ₹23,000 per month for 4–6 months). In most cases, there are free or cheaper alternatives that are just as good or better in some cases.
The most important outcome from a Nanodegree program is the projects you build and not the courses or the certificate. I often come across people who have completed a Nanodegree program but have relatively weak projects, making it very difficult for them to find a job.
Keeping this in mind, here’s how I would evaluate a Nanodegree program:
Jupyter notebooks are great for interactive programming and visualization of outputs. For this very reason, however, it is sometimes quite difficult to version control Jupyter notebooks. Git repositories don’t work well for Jupyter notebooks, for a number of reasons:
Deep neural networks are used mainly for supervised learning: classification or regression. Generative Adverserial Networks or GANs, however, use neural networks for a very different purpose: generative modeling.
Generative modeling is an unsupervised learning task that involves automatically discovering and learning the patterns in input data so that the model can be used to generate new examples that plausibly could have been drawn from the original dataset. — Source
While there are many approaches used for generative modeling, GANs take the following approach for learning: