Introduction to Data Science

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Description:
This courses provides an introduction to data science techniques and provides attendees with a working knowledge of some popular data science tools. The techniques learned can be applied to both small and Big Data sets, and on many types of data. Theoretical concepts are combined with hands-on exercises to give attendees the skills to derive valuable insights from real data.
Prerequisites:
This course is for anybody using data as part of their job and who need to gain insights from that data. Participants should have basic Excel skills.
Learning Outcomes:
At the end of this course, attendees will be able to:
Understand the stages in a data analysis project
Select the appropriate analysis tool to suit the data and project requirements
Use Excel to investigate a data problem
Visualise data relationships with Excel
Use advanced features of Excel such as What-If and Goal-seeking
Use R, R Studio and many of the R packages to carry out data exploration.
Investigate relationships in data with R
Visualise data relationships using R
Techniques for classification with R
Bayesian methods
Regression with R
Developing models and evaluating models
Streamline and organise their data analysis work
Day One Content :
Introduction to Data Science
Sample Data science problem
Types of data
Exploring Titanic data with excel
Graphing excel data
Advanced Excel techniques
Basic Data exploration with R
Visualising data with R
Day Two Content:
Nearest Neighbour classification
Bayes classification
Decision trees and decision rules
Regression techniques
Data visualisation
The Tidyverse packages
Data Analytical Pipeline