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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

A. Index-based iteration
B. Append
C. Key-value mapping
D. Slicing

Answer: B,D

NEW QUESTION # 112
What distinguishes structured data from unstructured data in the context of data modeling and storage?

A. Structured data cannot be stored in databases
B. Unstructured data cannot be stored or analyzed
C. Structured data follows a consistent schema or format
D. Unstructured data contains only numeric values

Answer: C

NEW QUESTION # 113
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

A. .iloc[] allows label-based selection
B. .iloc[] is position-based, while .loc[] is label-based
C. .loc[] can only be used on Series
D. .loc[] does not allow slicing

Answer: B

NEW QUESTION # 114
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.xlabel()
B. plt.legend()
C. plt.annotate()
D. plt.title()

Answer: C

NEW QUESTION # 115
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

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

Answer: D

NEW QUESTION # 116
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