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