Duration
5 days
Course Description
Are you planning to become a data scientist? If yes, then you have to learn Python programming language. Why? Python is the number one programming language in the world of data scientists. It emphasises on code readability and clear programming on both small and large scales, allowing you to focus on your research, product, or project.
Learning Outcomes
Upon completion of this course, you will be able to:
- Recognise the meaning of the terms “Data Science” and “Machine Learning”.
- Understand the basics of Python.
- Develop and write code easily in Python.
- Deal easily with files and file systems.
- Deal with different sources of data.
- Analyse and visualise data to gain new insights.
Day 1
Introduction to Programming
- What is Algorithm?
- What is Programming?
- The Natural Language of the Computer
- Machine Language
- Programming Language Levels
- Translators
Python Basics
- Identifiers, Lists, and Tuples
- Dictionaries, Sets and Strings
- Operators, Control Structures and Loops
Day 2
Jupyter Notebook
- Installing and Running Jupyter
- User Interface
- Checkpoints
Functions
- Lambda and Map Functions
- Globals and Locals
Pythonic Programming
- List Comprehension
- Generator Expressions
- Exceptions Handling
- Modules and Packages
Modules
- Documentation
- Packages and Namespaces
Working with Files
- Create, Read, Update, Delete (CRUD) a File
Day 3
Object-Oriented Programming
- OOP in General
- Classes
- Objects
- Constructors
- Instance/Class Data
- Instance/Class Method
- Inheritance
OS Module
- Working with File Systems
- Walking Directory Trees
- Paths
- Filenames
- Directories
Working with Files
- Creating a File
- Reading a File
- Updating a File
- Deleting a File
- Working with JSON Data
- What is JSON and Why Is It Important?
- Module, Serialisation and Deserialisation
Web Scraping (BeautifulSoup)
- What is Web Scraping
- HTML Tags
- BeautifulSoup Module
- Webpage Scraping Phase
Day 4
Introduction to Matrix Processing (NumPy)
- What is NumPy?
- Ndarray Object, Data Types
- Array Attributes, Array Creation Routines
- Indexing and Slicing
- Array Manipulation
- Mathematical Functions
Data Analysis (Pandas)
- What is Pandas?/li>
- Series
- Dataframes
- Data Importing
- Data re-processing
- Data Grouping
Data Visualization (Matplotlib)
- What is Matplotlib?
- Line Graphs
- Bar Graphs
- Pie Charts
- Histograms
- Scatter Plots
- Graph Attributes
- Text Annotation
Day 5
Introduction to Applied Machine Learning (Scikit-learn)
- What is Machine Learning?
- Machine Learning Algorithm Types
- Main Steps in Machine Learning Projects
Introduction to Scikit-learn Module
Capstone Project Final Evaluation (Exam)
Programme scope, delivery arrangements and suitability are confirmed during enquiry.