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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Introduction to Data and Data Analysis Concepts22.5%- Definition and classification of data
  • 1. Role of data in decision-making and business
  • 2. Difference between data, information, and knowledge
  • 3. Process of turning raw data into insights
- Data types and measurement scales
  • 1. Qualitative vs quantitative data
  • 2. Nominal, ordinal, interval, ratio scales
- Data lifecycle and ethical considerations
  • 1. Privacy, security, bias, and fairness in data
  • 2. Data collection, storage, processing, usage, and sharing
- Basic statistical concepts
  • 1. Population, sample, variable, observation
  • 2. Descriptive vs inferential statistics
Topic 2: Working with Data and Performing Simple Analysis25%- Data cleaning and preparation
  • 1. Handling missing values, duplicates, and errors
  • 2. Filtering, sorting, transforming data
  • 3. Formatting and standardizing values
- Data acquisition and loading
  • 1. Reading text, CSV, and structured files
  • 2. Importing data from external sources
- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Exploratory data analysis
  • 1. Identifying patterns, trends, and outliers
  • 2. Calculating mean, median, mode, range, variance, standard deviation
Topic 3: Data Visualization and Communication20%- Creating basic visualizations
  • 1. Line charts, bar charts, histograms, pie charts
  • 2. Using text and simple plotting tools
- Interpreting and presenting results
  • 1. Deriving conclusions and insights
  • 2. Reporting findings clearly and concisely
- Principles of effective data visualization
  • 1. Choosing appropriate chart types
  • 2. Clarity, simplicity, and accuracy
Topic 4: Python Basics for Data Analysis32.5%- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Core Python syntax and data types
  • 1. Variables, numbers, strings, booleans
  • 2. Lists, tuples, sets, dictionaries
- Control flow and functions
  • 1. Basic exception handling
  • 2. Conditional statements, loops, iteration
  • 3. Defining and calling functions, parameters, return values

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q24-Q29):

NEW QUESTION # 24
You are given two lists representing daily page views and sign-ups on a website:
views = [120, 130, 128, 700, 115, 123, 119, 680, 122]
signups = [12, 15, 13, 50, 11, 14, 10, 55, 13]
You want to:
- remove outliers (views > 600), and
- calculate correlation between the cleaned lists.
Which code accomplishes this correctly? Select the best answer.

Answer: A

Explanation:
It converts the lists to NumPy arrays, builds a boolean mask based on the views threshold to remove outliers, applies the same mask to both arrays so they stay aligned, and then computes the correlation on the cleaned pairs using np.corrcoef.


NEW QUESTION # 25
Consider a Python program that assigns a string value "5" to a variable and then attempts to multiply it by an integer 3. The programmer expects numeric multiplication. What will actually happen when the code is executed?

Answer: B

Explanation:
In Python, multiplying a string by an integer repeats the string. Therefore, "5" * 3 produces "555", not a numeric result. This behavior differs from arithmetic multiplication and often confuses beginners.


NEW QUESTION # 26
A Python script checks membership using "a" in "apple". The developer wants to understand how membership works for strings. What result will this expression produce?

Answer: C

Explanation:
The in operator checks for substring presence. Since the character "a" exists in "apple", the expression evaluates to True, demonstrating how membership works for strings in Python.


NEW QUESTION # 27
You have a list of test scores, where each entry includes a student name and a score. Some students appear more than once. You want to compute the average score for each student and store the results in a dictionary. Here's the partial code block:

Which code correctly replaces the # MISSING CODE comment to calculate the average score for each student? Select the best answer.

Answer: B

Explanation:
It iterates through each record, extracts the name and score, and uses dictionary .get() to accumulate total scores and counts per student safely, initializing missing keys to zero. This correctly enables calculation of each student's average afterward.


NEW QUESTION # 28
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

Answer: C

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.


NEW QUESTION # 29
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