Data Science: Tools and Techniques for Data Analytics

This course has been superseded

We suggest the following instead:
Data Analysis with R

Course Description

This courses provides an in-depth look at data science techniques and provides attendees with a working knowledge of the most popular data science tools in use today. 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.
4 days
Contact us for pricing
 

Prerequisites

Some experience of programming useful but not necessary.

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 R, R Studio and many of the R packages to carry out data exploration.
Use Python to derive insights from data
Use the most popular Python packages to carry out data analysis
Visualise data relationships using R, Python and other tools
Use big data tools when necessary
Use cloud APIs (Google or Amazon) on data sets
Streamline and organise their data analysis work

Who should attend

Data Analysts, Developers, Business Consultants .

Day One

Introduction to Data Science
Sample Data science problem
Types of data
Exploring data with excel
Introduction to R
Basic Data exploration with R
Introduction to python
Flow of control

Day Two

Lists, tuples, dictionaries and sets
Exceptions
Classes
IPython
Numpy
Scipy

Day Three

Pandas
Matplotlib
Data science techniques
Nearest Neighbour classification
Bayes classification
Decision trees and decision rules
Regression techniques
Association Rules

Day Four

Working with text
Applications with Python and R
Data visualisation
The Tidyverse packages
IBM Watson Analytics
Google analytics APIs
Hadoop
Big Data Tools
Data Analytical Pipeline
Big DataRR ProgrammingData AnalyticsData Science