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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions - Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers
Topic 2: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Quality assurance and validation techniques 2. Data standardization and transformation 3. Handling missing, duplicate and invalid values - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Data reshaping and aggregation 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources
Topic 3: SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Python-database connectivity 1. Executing queries and retrieving results 2. Error handling and best practices 3. Connecting to SQLite, MySQL or PostgreSQL - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 4: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Overfitting, underfitting and generalization 2. Accuracy, precision, recall, F1-score 3. Hyperparameter tuning basics
Topic 5: 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 - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation

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PCAD-31-02 Sample Questions Pdf - Exam PCAD-31-02 PDF

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q44-Q49):

NEW QUESTION # 44
In supervised learning, what is the primary goal when training a model on labeled data?

A. To discover hidden patterns without outputs
B. To cluster data points into similar groups
C. To analyze unstructured data formats like text
D. To learn a function that maps inputs to known outputs

Answer: D

NEW QUESTION # 45
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They require labeled training data
B. They work only with numerical features
C. They are useful for regression and classification tasks
D. They automatically detect outliers

Answer: A,C

NEW QUESTION # 46
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

A. Dropping all columns
B. Filling with random values
C. Replacing with NULL
D. Imputation using mean or median

Answer: D

NEW QUESTION # 47
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

A. 0
B. Error
C. (4, 5, 6)
D. 5.0

Answer: D

NEW QUESTION # 48
Which practices are typically part of the data integration process?
(Choose two)

A. Schema alignment
B. Neural network training
C. Data encryption
D. Format standardization

Answer: A,D

NEW QUESTION # 49
......

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