Jovian is the platform we wished we had access to when we started learning data science and ML. On Jovian, you can learn data science with practical online courses, build projects on real-world datasets, and interact with a global community of like-minded learners.

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Free Beginner-Friendly and Practical Online Courses

You don’t need a degree in Computer science or Mathematics to learn or apply state-of-the-art data science & ML techniques. Our courses focus on writing code and solving problems while explaining all the required theory & math in plain English.

If you’re interested in data science and machine learning, but don’t know…

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As a data science practitioner, you will often be required to present the insights from your projects to various stakeholders like your team members, company executives, product managers, software engineers, external clients, etc.

Follow these steps to present your data science projects:

Step 1 — Understand your audience and their goals

  • Who are you presenting to? What do they do? Why are they attending?
  • List the questions they are looking to answer by attending your presentation
  • Avoid technical jargon or code if your audience won’t understand it

Step 2 — Create a compelling slide deck for your project

  • Create a clean and simple slide deck with a clear outline and large headings
  • Use images, illustrations, screenshots, and graphs to…

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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:

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 .

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 your projects on your Github/Jovian profile. Here’s mine:

Project 1: Exploratory Data Analysis and Visualization (EDA)

Check out these projects for inspiration:

1. by Michael Chia Yin

2. by Kartik Godawat

3. by Daniela Cruz

Here are the steps for building a project on EDA & visualization:

1. Find…

is a sharing and collaboration platform for data science projects. This post is a follow-up the introductory post on with Jovian.

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…

Aakash N S

Founder, Jovian

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