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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Data standardization and transformation 3. Quality assurance and validation techniques - 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. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray
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
Data Visualization and Communication	15%	- Visualization principles and best practices 1. Audience-focused presentation 2. Choosing appropriate chart types 3. Color, layout and clarity - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices
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. Statistical significance and interpretation 2. Probability concepts and distributions 3. Hypothesis testing and confidence intervals - Descriptive statistics 1. Frequency distributions and percentiles 2. Measures of central tendency and dispersion 3. Correlation and covariance analysis
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Overfitting, underfitting and generalization 3. Hyperparameter tuning basics - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Classification: logistic regression, k-NN, decision trees 3. Model training, testing and evaluation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A. cursor.execute("SELECT * FROM users WHERE id = " + user_id)
B. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
C. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})
D. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)

Answer: B

NEW QUESTION # 44
Which method in the pandas library allows efficient loading of large datasets in chunks to conserve memory?

A. load_dataset()
B. fetch_chunks()
C. import_csv()
D. read_csv(chunksize=...)

Answer: D

NEW QUESTION # 45
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

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

Answer: B

NEW QUESTION # 46
What result will the following Pandas expression return: df['Age'].notnull().all()?

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

Answer: C

NEW QUESTION # 47
What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

A. To improve data visualization aesthetics
B. To reduce memory usage in the dataset
C. To avoid treating the same category as different due to case differences
D. To make string comparison operations slower

Answer: C

NEW QUESTION # 48
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