Course Overview:
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. On this course you will gain a fundamental understanding of Python Programming Language. Become a proficient Python programmer by learning along with skilled mentors. Learn how to search and navigate the tech documentation and efficiently handle errors and exceptions.
Get comfortable developing Python programs on your own through a series of coding exercises Become familiar with industry standards and learn the best practices for writing code Master your programming skills by working on real life projects; great for a resume building Create two of your projects for your portfolio of code.
Course Objectives:
- Installing
- Sorting
- Filtering
- Grouping
- Aggregating
- De-duplicating
- Pivoting
- Munging
- Deleting
- Merging
- Visualizing
Pre-requisites:
- Basic / intermediate experience with Microsoft Excel or another spreadsheet software (common
- functions, vlookups, Pivot Tables etc)
- Basic experience with the Python programming language
- Strong knowledge of data types (strings, integers, floating points, booleans) etc
Target Audience:
- Data analysts and business analysts
- Excel users looking to learn a more powerful software for data analysis
Course Duration:
- 14 hours – 2 days
Course Content:
Introduction to Python Programming
- What is Python?
- Why Python for Analysts?
- Basic Python operations
- Variable Types in Python
- Control Structures in Python
- Pillars of programming: Python built-in Data types
- Concept of mutability and behavior of different Data structures.
- Control flow statements: If, Elif and Else
- Definite and Indefinite loops: For and While loops
- Writing user-defined functions in Python
- Read and write Text files with python
- Learn how to manipulate data with Python
- Functions and Procedures
- Python lists, tuples and dictionaries.
Python as Object Oriented programming language
- Python classes, objects
- Python attributes and methods
- Inheritance
Functional Programming
Introduction to Jupyter
- Data analysis using python (numpy, pandas, series)
- Data visualisation using python libraries (ex; matplotlib, seaborn)
- Closing and Remarks
