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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Data distribution analysis 2. Correlation analysis 3. Descriptive statistics computation 4. Pattern identification - Data Analysis Workflow 1. Results interpretation and presentation 2. Data exploration and cleaning 3. Analysis and modeling 4. Problem definition
Topic 2: Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data import/export operations 3. Data sources and acquisition methods - Introduction to Data Analysis 1. Data analysis process lifecycle 2. Types of data (structured, unstructured, semi-structured) 3. Data analysis concepts and terminology
Topic 3: Python Programming for Data Analysis	30%	- Control Flow and Functions 1. Loops (for, while) 2. Function definitions and parameters 3. Return values and scope 4. Conditional statements (if, elif, else) - Python Data Types and Structures 1. Numbers, strings, booleans 2. Lists, tuples, dictionaries, sets 3. Data type conversions - File Operations 1. Context managers (with statement) 2. Reading from files (text, CSV) 3. Writing to files
Topic 4: Working with Data Using Python Libraries	30%	- Pandas Library 1. Data selection and filtering 2. GroupBy operations 3. DataFrame operations (merge, join, concat) 4. Series and DataFrame structures 5. Handling missing data - NumPy Fundamentals 1. NumPy arrays and operations 2. Vectorized operations 3. Array indexing and slicing 4. Basic statistical functions - Data Visualization 1. Matplotlib basics 2. Creating basic charts (line, bar, scatter, histogram) 3. Seaborn introduction 4. Customizing plots