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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Customization and styling 3. Line, bar, scatter, histogram, box plots - Visualization principles and best practices 1. Audience-focused presentation 2. Color, layout and clarity 3. Choosing appropriate chart types - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals - Descriptive statistics 1. Frequency distributions and percentiles 2. Correlation and covariance analysis 3. Measures of central tendency and dispersion - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data cleaning and validation 1. Data standardization and transformation 2. Quality assurance and validation techniques 3. Handling missing, duplicate and invalid values - 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%	- Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results - Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
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. Model training, testing and evaluation 2. Classification: logistic regression, k-NN, decision trees 3. Regression: linear, multiple, polynomial

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q142-Q147):

NEW QUESTION # 142
Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

A. INSERT
B. ROLLBACK
C. UPDATE
D. SELECT

Answer: A,D

NEW QUESTION # 143
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

A. Tuple unpacking
B. Global variables
C. Custom functions
D. Lambda functions

Answer: C

NEW QUESTION # 144
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

A. tuple
B. dictionary
C. set
D. list

Answer: B

NEW QUESTION # 145
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

A. Commit → Connect → Execute → Close
B. Execute → Connect → Commit → Close
C. Connect → Commit → Execute → Close
D. Connect → Execute → Commit → Close

Answer: D

NEW QUESTION # 146
Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

A. append()
B. join(..., how='outer')
C. concat()
D. merge(..., how='inner')

Answer: D

NEW QUESTION # 147
......

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