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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q112-Q117):

NEW QUESTION # 112
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?

A. A histogram of the data distribution
B. Only the standard deviation of the data
C. A normalized version of the data column
D. Basic descriptive statistics such as count, mean, and quartiles

Answer: D

NEW QUESTION # 113
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

A. TSV
B. XML
C. JSON
D. CSV

Answer: B,C

NEW QUESTION # 114
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

A. .iloc[] allows label-based selection
B. .loc[] can only be used on Series
C. .loc[] does not allow slicing
D. .iloc[] is position-based, while .loc[] is label-based

Answer: D

NEW QUESTION # 115
What is a best practice when organizing data in a spreadsheet for analysis?

A. Place charts in the middle of data tables
B. Merge cells to reduce redundancy
C. Apply conditional formatting to entire rows only
D. Use consistent column headers across all sheets

Answer: D

NEW QUESTION # 116
What is the primary advantage of using web scraping as a data collection method in business analytics?

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

Answer: B

NEW QUESTION # 117
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