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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. 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
Topic 2: Data Exploration and Statistical Analysis	25%	- 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 - Inferential statistics 1. Statistical significance and interpretation 2. Hypothesis testing and confidence intervals 3. Probability concepts and distributions
Topic 3: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 3. Accuracy, precision, recall, F1-score
Topic 4: Data Visualization and Communication	15%	- 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 storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions
Topic 5: 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 collection methods and sources 2. Data integration and merging 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q36-Q41):

NEW QUESTION # 36
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 at least one value in 'Age' is not NULL
C. Returns True if all values in 'Age' are not NULL
D. Returns the number of NULL values in 'Age'

Answer: C

NEW QUESTION # 37
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

A. It assumes a normal distribution
B. It uses random sampling with replacement
C. It requires a large population sample
D. It enables confidence interval estimation

Answer: B,D

NEW QUESTION # 38
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A. Use Python's None and SQL's IS NULL clause
B. Use == None directly in SQL queries
C. Convert all NULL to string before comparison
D. Replace NULL with zero before importing

Answer: A

NEW QUESTION # 39
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

A. TSV
B. CSV
C. XML
D. JSON

Answer: C,D

NEW QUESTION # 40
Which of the following are limitations of manual data collection methods?
(Choose two)

A. High scalability
B. Time-consuming process
C. Increased chance of human error
D. Seamless integration with big data tools

Answer: B,C

NEW QUESTION # 41
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