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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - 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
Topic 2: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation - 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 collection methods and sources 2. Data integration and merging 3. Data formats and storage systems
Topic 3: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Hyperparameter tuning basics 2. Accuracy, precision, recall, F1-score 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Model training, testing and evaluation 2. Classification: logistic regression, k-NN, decision trees 3. Regression: linear, multiple, polynomial
Topic 4: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Frequency distributions and percentiles 2. Correlation and covariance analysis 3. Measures of central tendency and dispersion - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Inferential statistics 1. Probability concepts and distributions 2. Hypothesis testing and confidence intervals 3. Statistical significance and interpretation
Topic 5: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices - 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 Questions and Answers: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) & PCAD-31-02 Practice Test

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which SQL command should a data analyst use to modify existing records in a database table based on specific conditions?

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

Answer: A

NEW QUESTION # 44
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

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

Answer: A

NEW QUESTION # 45
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A. Convert all NULL to string before comparison
B. Use Python's None and SQL's IS NULL clause
C. Replace NULL with zero before importing
D. Use == None directly in SQL queries

Answer: B

NEW QUESTION # 46
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 contains only numeric values
C. Structured data follows a consistent schema or format
D. Unstructured data cannot be stored or analyzed

Answer: C

NEW QUESTION # 47
Which practices are typically part of the data integration process?
(Choose two)

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

Answer: A,D

NEW QUESTION # 48
......

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