Python Programming Language and Basic Concepts
– Overview of development environments used for programming with Python
– Anaconda and Python Development Environment (Spyder, Jupyter Notebook)
– Google Colaboratory (The Environment Where Applications Will Be Performed During the Training)
– Applications of Python programming language
– Basic Concepts of Python Language
– Coding Structure
– Control Structures and Loops
– Function Usage
– Data Types and Collections
– Using Built-in Modules (math, random, statistics, etc.)
– File Operations with Python
– Creating, Reading, Writing, and Closing Files
– Working with CSV and Excel File Types
Overview of Data Analysis
– What is Data Analysis? What Can Be Done with Data Analysis?
– What is Data Science? What Are the Elements of Data Science?
– How Does the Process of Extracting Useful Information from Data Work?
– CRISP-DM Methodology and Its Demonstration on an Example
Developing Data Analysis Applications with Python
– Tools Used for Developing Data Analysis Applications and Their Uses
– Data Preprocessing Processes
– Reading data from Excel, CSV, etc. files
– Gaining information about the dataset (how many data points we have, whether we have missing data)
– Adding and deleting data,
– Detecting and cleaning repeated data
– Filtering within data
– Completing missing data
– Statistical Operations on Numerical Data
– Basic operations (finding the largest, smallest, average values, etc.)
– Deriving statistical distributions (standard deviation, variance, correlation, etc.)
– Data Visualization
– Drawing graphs (line, bar, pie, etc.)
– Operations on graphs
– Data Analysis Application Studies on Ready Datasets
