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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals - 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
Data Visualization and Communication	15%	- Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Heatmaps, pair plots and correlation matrices 3. Customization and styling - Visualization principles and best practices 1. Audience-focused presentation 2. Choosing appropriate chart types 3. Color, layout and clarity
SQL and Database Integration	10%	- Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Executing queries and retrieving results 3. Error handling and best practices - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Relational database concepts 1. Tables, keys, relationships and normalization
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. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees
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. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - Data collection, integration and storage 1. Data collection methods and sources 2. Data formats and storage systems 3. Data integration and merging

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

1. Which techniques are most effective when designing reusable and testable functions in Python?
(Choose two)

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

2. What is the main purpose of validating a dataset before applying statistical analysis?

A) To generate more columns from the dataset
B) To sort the data alphabetically
C) To visualize patterns more clearly
D) To ensure the dataset meets expected quality and structure

3. Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A) Web scraping
B) Surveys
C) Sensor logs
D) Transactional data

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

A) Error
B) 15
C) (4, 5, 6)
D) 5.0

5. Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

A) DataFrame.nunique()
B) DataFrame.drop_duplicates()
C) DataFrame.remove_duplicates()
D) DataFrame.unique()

Solutions:

Question # 1
Answer: B,C

Question # 2
Answer: D

Question # 3
Answer: B

Question # 4
Answer: D

Question # 5
Answer: C