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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

A. math
B. re
C. pandas.read_html()
D. sqlalchemy

Answer: C

NEW QUESTION # 112
Which method in a Python class is responsible for initializing the object's attributes at the time of creation?

A. __getattr__()
B. __init__()
C. __main__()
D. __str__()

Answer: B

NEW QUESTION # 113
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A. Features with different units and scales
B. Distance-based modeling algorithms
C. Presence of duplicate data
D. Boolean feature encoding

Answer: A,B

NEW QUESTION # 114
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

A. Line chart
B. Scatter plot
C. Box plot
D. Pie chart

Answer: C

NEW QUESTION # 115
What is the primary goal of pre-processing data before performing analysis?

A. To minimize the number of data points
B. To ensure the data is clean, consistent, and suitable for modeling
C. To compress datasets for storage efficiency
D. To obscure sensitive information

Answer: B

NEW QUESTION # 116
......

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