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Training
Course Information
Course Name
Python-Data Sc: Python for Data Science
Duration
5 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
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.
Overview
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 Profile
Audience Profile
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.
Prerequisities
Prerequisites
Basic knowledge of programming is preferable.
At Course Completion
At Course Completion
After completing this module, participants will learn the following
- Identify types of data that can be extracted
- Cleaning and preparing data
- Common approach in analyzing data
- Visualization using Python
- Python machine learning
Course Outline
Course Outline
Module 1: Day 1
- Introduction to Python and Data Science
- Installing and setting up the Python environment
- Basic Python syntax and data types
- NumPy, sets, list, and tuples data storage
- Control structures in Python
- Creating and using functions, date and time functions
- Laboratory exercise – My first Python program
Module 2: Day 2
- Comprehension of the use of lists and indexing data with dictionaries
- Sub-setting (Lists, Matching, Handling Missing Values)
- Reading and writing real-world data (text file, CSV and SQL)
- Introduction to pandas and managing Data Frames
- Using pandas for data conditioning
- Laboratory exercise – Working with Python pandas data frame
Module 3: Day 3
- Shaping data for analysis
- Handling raw text, using bag of words, TF-IDF and DTM shaping
- Sub-setting observations & variables, summarizing data
- Laboratory exercise – Getting, format (sub-setting) and store data.
- Feature creation, combining variables
- Understanding binning, discretization and data distribution
- Laboratory exercise – Preparing extracted data analysis
Module 4: Day 4
- Introduction to MatPlotLib
- Exploratory graphs in Python
- Visualizing data
- Laboratory exercise – Visualizing data
- Wrangling data and reducing dimensionality (feature selection)
- Basic clustering of data and handling outliers
- Laboratory exercise – Presenting analytics using Python
Module 5: Day 5
- Regression
- Association Rule and Frequent Itemset Mining
- Knn, Decision Trees and Random Forest
- Support Vector Machines
- Text processing using NLTK
- Laboratory exercise – Text classifier
- Wrap-up
