Duration
5 days
Overview
This course aims to be a comprehensive course in using the power of Python to read data from various sources, prepare the data for analysis, create exploratory visualizations and an introduction to machine learning algorithms. At the end of the course participants will be sufficiently equipped to start their journey towards becoming a full fledge Data Scientist.
Audience
This short course intended audiences are IT professionals, Data Analyst, Data Scientists and professionals who want to learn about Python programming. The programme is suitable for personnel with some experience in programming, data management and business reporting. The effective number of participants for this course is between 10 – 12.
Pre-requisite
Basic knowledge of programming is preferable.
Objectives
After completing this module, participants will learn the following
- 1. Identify types of data that can be extracted
- 2. Cleaning and preparing data
- 3. Common approach in analyzing data
- 4. Visualization using Python
- 5. Python machine learning
Course Outline
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 – 0930
Introduction to Python and Data Science
0930 – 1040
Installing and setting up the Python environment
1040 – 1100
Morning Break
1100 – 1200
Basic Python syntax and data types
1200 – 1300
NumPy, sets, list, and tuples data storage
1300 – 1400
Lunch
1400 – 1440
Control structures in Python
1440 – 1540
Creating and using functions, date and time functions
1540 – 1600
Afternoon Break
1600 – 1700
Laboratory exercise – My first Python program
DAY2
0900-1000
Comprehension of the use of lists and indexing data with dictionaries
1000-1040
Sub-setting (Lists, Matching, Handling Missing Values)
1040-1100
Morning Break
1100-1300
Reading and writing real-world data (text file, CSV and SQL)
1300-1400
Lunch
1400-1500
Introduction to pandas and managing Data Frames
1500-1540
Using pandas for data conditioning
1540-1600
Afternoon Break
1600-1700
Laboratory exercise – Working with Python pandas data frame
DAY3
0900-1000
Shaping data for analysis
1000-1040
Handling raw text, using bag of words, TF-IDF and DTM shaping
1040-1100
Morning Break
1100-1200
Sub-setting observations & variables, summarizing data
1200-1300
Laboratory exercise – Getting, format (sub-setting) and store data.
1300-1400
Lunch
1400-1500
Feature creation, combining variables
1500-1540
Understanding binning, discretization and data distribution
1540-1600
Afternoon Break
1600-1700
Laboratory exercise – Preparing extracted data analysis
DAY4
0900-1000
Introduction to MatPlotLib
1000-1040
Exploratory graphs in Python
1040-1100
Morning Break
1100-1200
Visualizing data
1200-1300
Laboratory exercise – Visualizing data
1300-1400
Lunch
1400-1500
Wrangling data and reducing dimensionality (feature selection)
1500-1540
Basic clustering of data and handling outliers
1540-1600
Afternoon Break
1600-1700
Laboratory exercise – Presenting analytics using Python
DAY5
0900-1000
Regression
1000-1040
Association Rule and Frequent Itemset Mining
1040-1100
Morning Break
1100-1200
Knn, Decision Trees and Random Forest
1200-1300
Support Vector Machines
1300-1400
1400-1500
Text processing using NLTK
1500-1540
Laboratory exercise – Text classifier
1540-1600
Afternoon Break
1600-1700
Wrap-up
Programme scope, delivery arrangements and suitability are confirmed during enquiry.