Data Science: Principles, Tools and Best Practices
|Accreditation:||CPD - Continuing Professional Development|
|Intake Dates:||February, May and September*|
|Timetables:||Click here for timetables|
|For Frequently Asked Questions||Click here|
This module will investigate what data science is and will cover common terminology such as ‘big data,’ ‘data mining’ and ‘curation of data.’ You will also learn the essential programming skills for real-world application of data science practices.
At the end of this module, students will have acquired key technical and conceptual skills to strategically manage data in their departments and organisation.
Data science terminology
Identifying trends from data
Applied mathematics and statistics
Data cleaning and preparation
Statistical hypothesis and interference. A/B testing.
Familiarise with the meanings of the main processes in data science
- Get to grips with the mathematics behind this field
- Learn how to read and write quality code (pep8) for data analysis and plotting and explore data science tools (git, conda, pip, jupyter) and common libraries (pandas, numpy, scipy)
- Understand the architecture and design of a data science project, how can this optimise performance and increase business competitiveness.
General mathematical background. Notions of statistics, probabilities and algebra.
- Basic understanding of computations Optional: familiarity with coding (python or R) Optional: Professional experience in management, finance, IT or other
- Fluency in English
- Personal Computer with minimum:
Intel Atom® processor or Intel® Core™ i3 processor
4GB Disk space
Operating systems: Windows* 7, macOS, Linux
MEET OUR DELEGATES
Check out the videos below to see what our Executive Education delegates have to say about their learning experience at LSBF.