Data Science: Tools and Techniques for Data Analytics

Request more details:

submit request
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.
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 API’s (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