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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data structures: Series, DataFrame, ndarray 3. Data reshaping and aggregation - Data cleaning and validation 1. Quality assurance and validation techniques 2. Data standardization and transformation 3. Handling missing, duplicate and invalid values - Data collection, integration and storage 1. Data collection methods and sources 2. Data formats and storage systems 3. Data integration and merging
Topic 2: 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 - Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
Topic 3: 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. Accuracy, precision, recall, F1-score 2. Overfitting, underfitting and generalization 3. Hyperparameter tuning basics
Topic 4: 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. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals
Topic 5: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Executing queries and retrieving results 2. Connecting to SQLite, MySQL or PostgreSQL 3. Error handling and best practices

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q123-Q128):

NEW QUESTION # 123
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

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

Answer: B,D

NEW QUESTION # 124
Why might using a pie chart to represent category proportions be less effective than a bar chart?

A. Pie charts display individual data points more clearly
B. Human perception is less accurate at judging angles than lengths
C. Pie charts cannot be created using Seaborn
D. Bar charts are limited to one variable only

Answer: B

NEW QUESTION # 125
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A. Parquet files can only be used with Excel
B. Parquet offers row-based compression
C. Parquet stores data in plain text, making it human-readable
D. Parquet supports schema evolution and columnar storage

Answer: D

NEW QUESTION # 126
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

A. groupby().max()
B. groupby().count()
C. groupby().mean()
D. groupby().aggregate()

Answer: C

NEW QUESTION # 127
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A. Converting all values to integers
B. Replacing inconsistent labels with a standardized value
C. Normalizing using min-max scaling
D. Removing rows with invalid labels

Answer: B,D

NEW QUESTION # 128
......

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