Duration
5 days
Overview
This course aims at providing participants with an introduction to the R and Python programming language, specifically for data analytics.
Delivery Method
This course is covered through lectures, active participation/hands-on, guided group discussions, exercises and demonstrations.
Number of Participants
Effective number of participants per class is 10-12.
Organisational Impact
The knowledge gained from this course will prepare participants to embark on conducting data analytics using the R and/or Python programming language.
Organizational Benefits
To provide employees of the data analysis department
- a systematic approach to use R and/or Python as alternative analysis and analytics method to manipulate data.
- cost reduction because the tools are free.
Personal Impact
The employee should be able to
- acquire the fundamental knowledge in R and Python programming
- apply R and/or Python programming to clean and analyse data
- using R and/or Python for exploratory analysis tasks
- be able to decide which tools will be suitable to handle the task on hand
Learning Objectives
After completing this module, participants will learn the following
- Identify types of data
- Define steps in cleaning data
- Deploy R and Python language to clean data
- Define the common approach in analyzing data.
- Using R and Python for analytics tasks.
Course Contents
The course content will be delivered according to this schedule (E.g. typically a short course has a maximum of 7 hours per day, 9:00 am to 5:00 pm with an hour for lunch)
DAY1
0900 – 0940
Overview of R and Python in Data Analytics
0940 – 1040
Installing and setting Up the R Environment
1040 – 1100
Morning Break
1100 – 1200
R Data Types and Objects
1200 – 1300
Reading and Writing Data
1300 – 1400
Lunch
1400 – 1500
List, Matrix and Data Frame
1500 – 1540
Sub-setting Data
1540 – 1600
Afternoon Break
1600 – 1700
Laboratory Exercise - Working with Data Frame
DAY2
0900 – 1000
Control structures
1000 – 1100
Creating and using functions
1100 – 1120
Morning Break
1120 – 1230
Scoping rules, manipulating dates and times. Using the R “apply” functions
1230 – 1230
Laboratory exercise – Working with dates
1230 – 1430
Lunch
1430 – 1520
Getting data, reshaping data, sub-setting observations & variables.
1520 – 1540
Creating new variables, grouping data and merging data.
1540 – 1600
Afternoon Break
1600 – 1700
Laboratory exercise – “R Programming Exercise”
DAY3
0900 – 1000
R base plotting system
1000 – 1040
Introduction to ggplot plotting system
1040 – 1100
Morning Break
1100 – 1200
Visualizing data
1200 – 1300
Laboratory exercise – ggplot Plot
1300 – 1400
Lunch
1400 – 1500
Installing and setting up the Python environment
1500 – 1540
Understanding the myriad of Python tools for analytics
1540 – 1600
Afternoon Break
1600 – 1700
Python basics (numbers, strings, logic, dates)
DAY4
0900 – 1000
Control structures (conditions and loops)
1000 – 1040
Creating and using (calling) functions
1040 – 1100
Morning Break
1100 – 1200
Storing data using sets, lists and tuples.
1200 – 1300
Reading and writing data
1300 – 1400
Lunch
1400 – 1440
Understanding NumPy and pandas.
1440 – 1540
Reshaping data, sub-setting, missing data handling, slicing and dicing.
1540 – 1600
Afternoon Break
1600 – 1700
Laboratory exercise – “Python Programming Exercise”
DAY5
0900 – 1000
MatPlotLib basics (graph, axes, grids, line, labels)
1000 – 1100
Visualizing data using Python
1100 – 1120
Morning Break
1120 – 1300
Laboratory exercise – MatPlotLib Plot
1300 – 1400
Lunch
1400 – 1500
Text processing with Python
1500 – 1540
Bag of Words, n-grams and TF-IDF
1540 – 1600
Afternoon Break
1600 – 1700
Laboratory exercise – “Text processing with Python”
Pre-requisites
- Basic knowledge of programming is preferable.
Performance Evaluation
- Attendance
- Participation
- Group work
Programme scope, delivery arrangements and suitability are confirmed during enquiry.