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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Exploration and Statistical Analysis	25%	- 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 - Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions
SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Executing queries and retrieving results 3. Error handling and best practices
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. Classification: logistic regression, k-NN, decision trees 2. Model training, testing and evaluation 3. Regression: linear, multiple, polynomial
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - 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 principles and best practices 1. Audience-focused presentation 2. Choosing appropriate chart types 3. Color, layout and clarity - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Heatmaps, pair plots and correlation matrices 3. Customization and styling

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

A. tuple
B. list
C. dictionary
D. set

Answer: C

NEW QUESTION # 51
What is a major challenge in aggregating data from multiple sources?
Response:

A. Duplicate records and format inconsistencies
B. Increased algorithmic complexity
C. Overfitting in predictive models
D. Excessive disk space usage

Answer: A

NEW QUESTION # 52
What may occur if a model is evaluated using the same data it was trained on?

A. Overgeneralization
B. Underfitting
C. Overfitting
D. Data leakage

Answer: C

NEW QUESTION # 53
What is a best practice when organizing data in a spreadsheet for analysis?

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

Answer: D

NEW QUESTION # 54
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.rename()
B. df.dropna()
C. df.sort_values()
D. df.replace()

Answer: D

NEW QUESTION # 55
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