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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
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. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources - 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. Handling missing, duplicate and invalid values 2. Quality assurance and validation techniques 3. Data standardization and transformation
SQL and Database Integration	10%	- 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 - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
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. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Correlation and covariance analysis 2. Measures of central tendency and dispersion 3. Frequency distributions and percentiles
Data Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Audience-focused presentation 3. Choosing appropriate chart types - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q140-Q145):

NEW QUESTION # 140
What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A. To filter rows based on index values
B. To sort the DataFrame by a specific column
C. To append new rows to the DataFrame
D. To split the data into subgroups for aggregation

Answer: D

NEW QUESTION # 141
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

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

Answer: A,B

NEW QUESTION # 142
Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?

A. To compress all values into integers
B. To convert strings into numeric values
C. To make all features interpretable as binary values
D. To prevent features with larger scales from dominating distance calculations

Answer: D

NEW QUESTION # 143
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.xlabel()
B. plt.title()
C. plt.annotate()
D. plt.legend()

Answer: C

NEW QUESTION # 144
In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

A. The data shows no apparent relationship
B. The correlation is weak and likely negative
C. Advertising has no effect on sales
D. There is a strong positive correlation

Answer: D

NEW QUESTION # 145
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