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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q18-Q23):

NEW QUESTION # 18
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. [25, 100, 225]
C. [100, 225]
D. [10, 15]

Answer: C

NEW QUESTION # 19
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

A. Returns rows 2 to 4 in 'column1'
B. Returns a new DataFrame excluding 'column1'
C. Returns rows 2 through 5 in 'column1'
D. Returns the entire DataFrame

Answer: C

NEW QUESTION # 20
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?

A. Use dropna() with axis=1
B. Use drop() to remove null rows
C. Apply interpolate() method
D. Use fillna() with a constant value

Answer: D

NEW QUESTION # 21
What is the main purpose of using the bootstrapping technique in inferential statistics?

A. To increase the number of features in a model
B. To estimate the sampling distribution by resampling with replacement
C. To filter outliers from a dataset
D. To validate the syntax of SQL queries in Python

Answer: B

NEW QUESTION # 22
Which function is used to reshape a NumPy array without modifying the original data values?

A. reshape()
B. resize()
C. transpose()
D. flatten()

Answer: A

NEW QUESTION # 23
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