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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

  • Recruiters and employers often look at dozens, if not hundreds of Resumes every day
  • While screening, most recruiters spend less than 20 seconds on a Resume before rejecting the candidate
  • Your Resume needs to be clear & concise and…


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Effective communication, documentation, presentation and continuous learning

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.

Effective Communication

  • You’ll talk to stakeholders from different teams to gather requirements & deliverables.
  • You’ll work with IT & database admins to get data from multiple sources for analysis/ modeling.
  • You’ll work with software engineers to deploy your pipelines, models & jobs to production.

Projects often fail due to a lack of clear communication.

Documentation & Writing

  • Document every step of your work using READMEs, Google docs, or spreadsheets
  • Track experiment results to iterate systematically, avoid failed ideas…


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and how to learn it

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:

1. Basic Programming (preferably Python)

  • You must be comfortable with variables, statements, functions, loops, classes, modules, etc., and have the ability to write basic programs in Python (or any other programming language).
  • You may need to create automation scripts to periodically pull data from DBs, do some analysis & store the results back or plot some graphs and send emails, etc.
  • Familiarity with the Unix shell, Git, SSH, etc. …


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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:

Step 1. Find some sample job listings for the position you’re targeting

  • There are many roles in data science: Data Analyst, ML Engineer, Data Engineer, etc.
  • Do some research to determine which role suits your…


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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 .

Tip 1: Build something every week

Not surprisingly, the best way to get better at coding is to write code regularly

  • Set aside 3–5 hours every week to work on a mini-project (50–100 lines of code)
  • Push yourself to make…


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and how to build them

Before you start applying for data science jobs, make sure to complete at least one project in each of these three important domains:

  1. Exploratory Data Analysis and Visualization
  2. Classical Machine Learning on Tabular Data
  3. Deep Learning (Computer Vision/NLP)

You can host these projects on your Github/Jovian profile. Here’s mine:

Project 1: Exploratory Data Analysis and Visualization (EDA)

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:

  • Go through the syllabus and carefully study…


Jovian.ml is a sharing and collaboration platform for data science projects. This post is a follow-up the introductory post on sharing & embedding Jupyter notebooks online with Jovian.

See it Live

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:

  • Notebooks are often quite large in size (~10 to 100MB), so they can slow down your repository, since Git is designed to work well only with small code files.
  • Notebooks are not plain…


Image source

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:

Aakash N S

Founder, Jovian.ml

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