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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q102-Q107):

NEW QUESTION # 102
What is the correct SQL statement to permanently remove a table named customers from a database?

A. DELETE TABLE customers;
B. TRUNCATE TABLE customers;
C. DROP TABLE customers;
D. REMOVE TABLE customers;

Answer: C

NEW QUESTION # 103
What is the primary advantage of using web scraping as a data collection method in business analytics?

A. It guarantees real-time data accuracy
B. It ensures compliance with privacy laws
C. It eliminates the need for data cleaning
D. It allows automation of data collection from public sources

Answer: D

NEW QUESTION # 104
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A. Convert all NULL to string before comparison
B. Replace NULL with zero before importing
C. Use Python's None and SQL's IS NULL clause
D. Use == None directly in SQL queries

Answer: C

NEW QUESTION # 105
Which method is commonly used to handle missing numerical data in a dataset?

A. Dropping all columns
B. Filling with the mean or median
C. Multiplying all missing values by zero
D. Replacing with text labels

Answer: B

NEW QUESTION # 106
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

A. openpyxl
B. sqlite3
C. matplotlib
D. csv

Answer: A

NEW QUESTION # 107
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