Pass-Sure PCAD-31-02 - Online Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Bootcamps

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Working with Data Using Python Libraries30%- NumPy Fundamentals
  • 1. NumPy arrays and operations
  • 2. Array indexing and slicing
  • 3. Basic statistical functions
  • 4. Vectorized operations
- Pandas Library
  • 1. Data selection and filtering
  • 2. DataFrame operations (merge, join, concat)
  • 3. Handling missing data
  • 4. Series and DataFrame structures
  • 5. GroupBy operations
- Data Visualization
  • 1. Customizing plots
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Matplotlib basics
  • 4. Seaborn introduction
Topic 2: Applied Data Analysis Projects20%- Data Analysis Workflow
  • 1. Data exploration and cleaning
  • 2. Analysis and modeling
  • 3. Results interpretation and presentation
  • 4. Problem definition
- Exploratory Data Analysis (EDA)
  • 1. Pattern identification
  • 2. Correlation analysis
  • 3. Descriptive statistics computation
  • 4. Data distribution analysis
Topic 3: Data Analysis Fundamentals20%- Introduction to Data Analysis
  • 1. Data analysis process lifecycle
  • 2. Data analysis concepts and terminology
  • 3. Types of data (structured, unstructured, semi-structured)
- Data Collection and Preparation
  • 1. Data sources and acquisition methods
  • 2. Data import/export operations
  • 3. Data cleaning and preprocessing basics
Topic 4: Python Programming for Data Analysis30%- Python Data Types and Structures
  • 1. Numbers, strings, booleans
  • 2. Lists, tuples, dictionaries, sets
  • 3. Data type conversions
- Control Flow and Functions
  • 1. Return values and scope
  • 2. Conditional statements (if, elif, else)
  • 3. Function definitions and parameters
  • 4. Loops (for, while)
- File Operations
  • 1. Context managers (with statement)
  • 2. Writing to files
  • 3. Reading from files (text, CSV)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q135-Q140):

NEW QUESTION # 135
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

Answer: D


NEW QUESTION # 136
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

Answer: B


NEW QUESTION # 137
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?

Answer: C


NEW QUESTION # 138
What is a key assumption of linear regression that distinguishes it from logistic regression?

Answer: C


NEW QUESTION # 139
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

Answer: A,B


NEW QUESTION # 140
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