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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q122-Q127):

NEW QUESTION # 122
What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

A. Use df.merge() with how='outer'
B. Use df.dropna() on the foreign key column
C. Use df.sort_values() to sort both columns before merging
D. Use .isin() to compare the foreign key column against the parent key column

Answer: D

NEW QUESTION # 123
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A. It configures multiple plots in a grid layout within a single figure
B. It overlays multiple plots in the same figure without axes
C. It links Matplotlib with Seaborn's grid styling
D. It creates a 3D surface plot using pandas

Answer: A

NEW QUESTION # 124
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A. [25, 100]
B. [100, 225]
C. [25, 100, 225]
D. [10, 15]

Answer: B

NEW QUESTION # 125
What result will the following Pandas expression return: df['Age'].notnull().all()?

A. Returns True if all values in 'Age' are NULL
B. Returns True if all values in 'Age' are not NULL
C. Returns the number of NULL values in 'Age'
D. Returns True if at least one value in 'Age' is not NULL

Answer: B

NEW QUESTION # 126
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?

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

Answer: B

NEW QUESTION # 127
......

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