What you'll learn
Introduction to computers
Introduction to programming
Data systems
Python
Requirements
No requierments or prerequisites needed.
Wanting to learn something new
Description
This course is for professional data scientists, data processors and GIS professionals who want to automate their workflows and speed up production.No prior knowledge of coding is needed. All the concepts and special words will be explained during the introduction part of the course. Later more in-depth explanations will be given for more involved topics.The last part of the course will be an introduction to Python with is a programing language that is commonly used in workflow automatization in a variety of software. Although we will be using Python as the main practical coding language there are examples of other coding languages used throughout the course. When you understand the basics of coding it is possible to figure out what different programming languages do even if you can understand it all making it possible to learn others.Python is a good choice for a beginner coding language because it does not use a compiler it is an interpreted language which means that one line at a time is converted to machine code making an overarching program structure unnecessary although functions, classes and more can be used later when you are more familiar with its use and want to do more complex coding.No software-specific procedures and functions will be covered because there is such a large area of the data market when python is used within other software that has its dedicated functions. This course will teach you the basics of coding. Even if you are not planning to use Python in the end.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Computer Basics
Lecture 3 Python 2 or 3
Lecture 4 Data Storage
Section 2: Programming
Lecture 5 Operators
Lecture 6 Variables, Keywords and Names
Lecture 7 Statments
Lecture 8 Python Getting Started
Lecture 9 Basic Types in Python
Lecture 10 Variables in Python
Lecture 11 Basic Operators in Python
Lecture 12 Python Recourses for this Course
Section 3: Data Types and Data Systems
Lecture 13 Numeric Data Systems
Lecture 14 Data Types and Data Structures
Lecture 15 Python Nomeric Data Types
Lecture 16 Python String Data Type
Lecture 17 Python List Data Type
Lecture 18 Python Dictionary Data Type
Lecture 19 Python Tuple Data Type
Lecture 20 Python Set Data Type
Lecture 21 Python Date Time Data Type
Lecture 22 Ptheon Array Data Type
Section 4: Program Flow Controle
Lecture 23 Subroutines
Lecture 24 Conditional Statements
Lecture 25 Loops
Lecture 26 Objects Orientated
Lecture 27 Python Functions 1
Lecture 28 Python Functions 2
Lecture 29 Python Functions 3
Lecture 30 Python If, Elsif and Else
Lecture 31 Python Error Handling
Lecture 32 Python Loops: For Loop
Lecture 33 Python Loops: While Loop
Lecture 34 Python Classes Part 1
Lecture 35 Python Classes Part 2
Lecture 36 Python Dataclasses
Section 5: Notebooks
Lecture 37 NoteBooks
Lecture 38 Jupyter Notebook Markdown
Lecture 39 Python Working With Files
Lecture 40 Introduction to Dataframes
Lecture 41 Dataframes Data Cleanup
Lecture 42 DataFrames Data Analyses: Group Data
Lecture 43 Conclusion
Non-developer professionals who want to learn programming to improve productivity and atomize thee workflows,People who want to learn the basics of computer programming

Homepage

https://www.udemy.com/course/coding-for-the-professional-non-developer/