A Day In The Life of a Data Scientist (2020)

Authors

Tamara Greasby

Louis Tremblay

Senior System Engineer

Alfonso Berumen

Consultant, Owner of Los Angeles Data Analytics LLC

Sam Cvetkovski

Data Analytics Leader and Consultant

Lacey Westphal

Data Scientist, Manager of Academic Data Analysis

Sara Anstey

Data Analytics Consultant

Madecraft

Full-Service Learning Content Company

Course details

The best way to understand what it's actually like to work as a data scientist is to spend the day with one. In this course, you can follow along with one day in the life of real data scientists working on real projects. Explore exactly what data science work looks like—getting on-the-job insights to help prepare you to tackle the next challenge or choose your next data science role. Discover how working professionals handle time management, starting from the very first hour of the workday, and tackle major tasks, such as exploring the data, mitigating bias, and presenting their findings. Another thing that separates pros from the rest is process and in chapter three, you can see how data scientists build automation, cross-functional collaboration, and other techniques into their workflows. Plus, learn how to choose and use tools, structure and work well in teams, and serve your clients.

Topics include:
Managing your tasks with a to-do list
Unpacking data
Identifying the problem you're trying to solve
Telling the story of data
Automating analytics reporting
Setting milestones
Measuring success
Choosing the right tools
Structuring a data science team
Working with remote teams
Communicating with your team
Working with clients

Concepts

Introduction

Welcome to the life of a data scientist

Time Management

First hour of the day
Handling distractions and delays
Using to-do lists
Unpacking the data
Minimum viable product
Managing meetings
Big picture thinking

Working with Data

Exploratory data
Biased data sets
Responsibilities of data analytics
Identifying the problem
Presenting data findings
Telling the story of the data

Creating a Process

Using data to solve business problems
Automated analytics reporting
Working cross-functionally
Setting key milestones
Data analytics process
How to measure success in data science

Using Tools

Knowing when to use which tools
Using open-source tools
Python and pandas tools
Jupyter Notebook tools
Tableau and G Suite visualization tools

Structuring a Data Science Team

Centralized organization
Development units
Remote teams
Hierarchy and roles
A culture of collaboration

Working with Teams

Being a good team member
Communication tools
Communicating with your team
Communicating with remote team members
Getting feedback

Working with Clients

Serving the client
Highly regulated clients

Conclusion

The future of data analysis
Start your journey as a data scientist