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…

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…

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) 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.

Jupyter notebooks are great for interactive programming and visualization of outputs. For this very reason, however, it is sometimes quite difficult to…

Aakash N S

Founder, Jovian

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