A Day In The Life of a Data Scientist
- Offered byLinkedin Learning
A Day In The Life of a Data Scientist at Linkedin Learning Overview
Duration | 1 hour |
Total fee | ₹1,150 |
Mode of learning | Online |
Difficulty level | Beginner |
Credential | Certificate |
A Day In The Life of a Data Scientist at Linkedin Learning Highlights
- Earn a sharable certificate
A Day In The Life of a Data Scientist at Linkedin Learning Course details
- In this course, learner 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 learner to tackle the next challenge or choose 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
A Day In The Life of a Data Scientist at Linkedin Learning Curriculum
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