PCAD-31-02 Prep Training - PCAD-31-02 Study Guide & PCAD-31-02 Test Pdf

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Handling missing, duplicate and invalid values 2. Data standardization and transformation 3. Quality assurance and validation techniques - Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray
Topic 2: Data Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Audience-focused presentation 3. Choosing appropriate chart types - 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. Customization and styling 3. Line, bar, scatter, histogram, box plots
Topic 3: Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Descriptive statistics 1. Correlation and covariance analysis 2. Frequency distributions and percentiles 3. Measures of central tendency and dispersion - Inferential statistics 1. Probability concepts and distributions 2. Hypothesis testing and confidence intervals 3. Statistical significance and interpretation
Topic 4: 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 5: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q12-Q17):

NEW QUESTION # 12
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A. WHERE
B. GROUP BY
C. ORDER BY
D. HAVING

Answer: A

NEW QUESTION # 13
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A. Boolean feature encoding
B. Features with different units and scales
C. Distance-based modeling algorithms
D. Presence of duplicate data

Answer: B,C

NEW QUESTION # 14
Which techniques are most effective when designing reusable and testable functions in Python?
(Choose two)

A. Use of default parameters
B. Creating large monolithic functions
C. Returning print statements instead of values
D. Avoiding external state or side effects

Answer: A,D

NEW QUESTION # 15
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

A. Adding descriptive axis labels
B. Avoiding color entirely
C. Disabling grid lines in all cases
D. Customizing tick labels

Answer: A,D

NEW QUESTION # 16
Which method is typically used in Pandas to check if a column contains only values within an expected range?

A. df.sort_values()
B. df.sample()
C. df.clip()
D. df.isin()

Answer: D

NEW QUESTION # 17
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