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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which operations are valid when working with Pandas Series?
(Choose two)

A. Arithmetic vector operations
B. Using .columns to rename
C. Merging by index using merge()
D. Applying NumPy universal functions

Answer: A,D

NEW QUESTION # 24
What result will the following Pandas expression return: df['Age'].notnull().all()?

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

Answer: D

NEW QUESTION # 25
When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

A. Using fillna() to replace missing product prices
B. Applying agg() to calculate multiple metrics like mean and max
C. Using rename() to change column headers
D. Using groupby() to compute total sales per region

Answer: B,D

NEW QUESTION # 26
Which element is essential to justify a conclusion drawn from a dataset?

A. The use of color in plots
B. File format of the source data
C. Logical reasoning and supported metrics
D. The origin of the dataset

Answer: C

NEW QUESTION # 27
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.title()
D. plt.annotate()

Answer: D

NEW QUESTION # 28
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