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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Data standardization and transformation 3. Quality assurance and validation techniques - Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - Data collection, integration and storage 1. Data collection methods and sources 2. Data integration and merging 3. Data formats and storage systems
Topic 2: Data Visualization and Communication	15%	- Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices
Topic 3: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results
Topic 4: Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles
Topic 5: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Classification: logistic regression, k-NN, decision trees 2. Model training, testing and evaluation 3. Regression: linear, multiple, polynomial - Model performance and optimization 1. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score