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| Section | Weight | Objectives |
|---|
| Introduction to Data and Data Analysis Concepts | 22.5% | - Basic statistical concepts
- 1. Population, sample, variable, observation
- 2. Descriptive vs inferential statistics
- Data types and measurement scales
- 1. Qualitative vs quantitative data
- 2. Nominal, ordinal, interval, ratio scales
- Definition and classification of data
- 1. Difference between data, information, and knowledge
- 2. Process of turning raw data into insights
- 3. Role of data in decision-making and business
- Data lifecycle and ethical considerations
- 1. Privacy, security, bias, and fairness in data
- 2. Data collection, storage, processing, usage, and sharing
|
| Working with Data and Performing Simple Analysis | 25% | - Data aggregation and grouping
- 1. Summarizing and grouping datasets
- Data acquisition and loading
- 1. Reading text, CSV, and structured files
- 2. Importing data from external sources
- Data cleaning and preparation
- 1. Formatting and standardizing values
- 2. Handling missing values, duplicates, and errors
- 3. Filtering, sorting, transforming data
- Exploratory data analysis
- 1. Identifying patterns, trends, and outliers
- 2. Calculating mean, median, mode, range, variance, standard deviation
|
| Python Basics for Data Analysis | 32.5% | - Core Python syntax and data types
- 1. Variables, numbers, strings, booleans
- 2. Lists, tuples, sets, dictionaries
- Control flow and functions
- 1. Conditional statements, loops, iteration
- 2. Defining and calling functions, parameters, return values
- 3. Basic exception handling
- Built-in modules for data work
- 1. math, statistics, datetime, collections, csv
- Introduction to NumPy
- 1. Arrays, basic operations, indexing, slicing
|
| Data Visualization and Communication | 20% | - Interpreting and presenting results
- 1. Reporting findings clearly and concisely
- 2. Deriving conclusions and insights
- Creating basic visualizations
- 1. Line charts, bar charts, histograms, pie charts
- 2. Using text and simple plotting tools
- Principles of effective data visualization
- 1. Choosing appropriate chart types
- 2. Clarity, simplicity, and accuracy
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q23-Q28):
NEW QUESTION # 23
A variable is assigned using chained assignment: a = b = c = 10. The developer then changes b =
5. What will be the value of a after this operation?
Answer: C
Explanation:
Integers are immutable in Python. Reassigning b to a new value does not affect a or c, which still reference the original integer value 10.
NEW QUESTION # 24
A programmer writes a script to test operator precedence using the expression 3 + 4 * 2 ** 2. The goal is to evaluate the result correctly based on Python's operator hierarchy rules. What result should the script produce?
Answer: B
Explanation:
Exponentiation is evaluated first, so 2 ** 2 equals 4. Then multiplication 4 * 4 equals 16. Finally, addition is performed: 3 + 16 results in 19.
NEW QUESTION # 25
Below are six possible summaries for your presentation:
- Summary 1: "The data revealed a moderate positive correlation between age and recycling frequency. While 72% of students reported recycling paper regularly, only 39% reported recycling plastic."
- Summary 2: "Analysis showed notable differences in recycling habits by material type, with paper being most recycled and plastic least recycled."
- Summary 3: "Older kids recycled more than younger ones. Paper was the most recycled item- plastic got forgotten a lot."
- Summary 4: "Student responses showed positive environmental habits overall, though participation varied by waste type."
- Summary 5: "A lot of students said they recycle. Some didn't answer every question, so we couldn't figure out everything."
- Summary 6: "We asked more than 300 students about recycling. Most said they recycle paper, but way fewer recycle plastic." Which communication style is the best fit for Group A (data scientists) and Group B (12-year- olds), respectively? Select the best answer.
- A. Summary 6 for Group A, Summary 5 for Group B
- B. Summary 4 for Group A, Summary 3 for Group B
- C. Summary 2 for Group A. Summary 6 for Group B
- D. Summary 1 for Group A, Summary 6 for Group B
- E. Summary 1 for Group A, Summary 3 for Group B
- F. Summary 6 for Group A, Summary 3 for Group B
Answer: E
Explanation:
The first summary uses precise statistical language and quantitative detail appropriate for data scientists. The third summary uses simpler, more conversational language that is easier for 12- year-olds to understand.
NEW QUESTION # 26
A health researcher uses wearable devices to record physical activity and sends a survey to randomly selected participants across age groups.
Why can this approach be effective? Select the best answer.
- A. It uses unstructured interviews to validate automated tracking, improving bias detection.
- B. It relies on observational methods, which are more accurate than surveys or sensors.
- C. It limits sampling to one demographic, ensuring high-quality data within a specific group.
- D. It combines automated data collection with representative sampling, reducing the risk of bias.
Answer: D
Explanation:
Combining automated wearable data with a randomly selected survey sample integrates objective measurements with representative participant input, which helps reduce bias and improves the reliability and generalizability of the findings.
NEW QUESTION # 27
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.
- A. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
- B. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.
- C. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.
- D. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.
Answer: A
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 # 28
......
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