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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SQL and Database Integration	10%	- SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - 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
Topic 2: Data Visualization and Communication	15%	- Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Choosing appropriate chart types 2. Audience-focused presentation 3. Color, layout and clarity - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices
Topic 3: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Indexing, filtering, sorting and grouping 3. Data structures: Series, DataFrame, ndarray - Data collection, integration and storage 1. Data collection methods and sources 2. Data integration and merging 3. Data formats and storage systems - Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation
Topic 4: Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Hypothesis testing and confidence intervals 3. Statistical significance and interpretation - Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Topic 5: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Overfitting, underfitting and generalization 2. Accuracy, precision, recall, F1-score 3. Hyperparameter tuning basics - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q53-Q58):

NEW QUESTION # 53
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

A. .loc[] does not allow slicing
B. .iloc[] is position-based, while .loc[] is label-based
C. .loc[] can only be used on Series
D. .iloc[] allows label-based selection

Answer: B

NEW QUESTION # 54
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.sort_values()
B. df.dropna()
C. df.replace()
D. df.rename()

Answer: C

NEW QUESTION # 55
Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

A. NumPy
B. flask
C. pandas
D. Pygame

Answer: A,C

NEW QUESTION # 56
Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

A. sort_values()
B. pivot_table()
C. var()
D. median()

Answer: C,D

NEW QUESTION # 57
What is the primary purpose of using annotations in a data visualization?

A. To enhance interactivity in static plots
B. To highlight important data points and provide contextual information
C. To replace the need for axes
D. To ensure faster rendering in large datasets

Answer: B

NEW QUESTION # 58
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