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| Section | Weight | Objectives |
|---|
| Topic 1: Introduction to Data and Data Analysis Concepts | 22.5% | - Basic statistical concepts
- 1. Population, sample, variable, observation
- 2. Descriptive vs inferential statistics
- Definition and classification of data
- 1. Process of turning raw data into insights
- 2. Role of data in decision-making and business
- 3. Difference between data, information, and knowledge
- Data types and measurement scales
- 1. Nominal, ordinal, interval, ratio scales
- 2. Qualitative vs quantitative data
- Data lifecycle and ethical considerations
- 1. Data collection, storage, processing, usage, and sharing
- 2. Privacy, security, bias, and fairness in data
|
| Topic 2: Data Visualization and Communication | 20% | - Principles of effective data visualization
- 1. Choosing appropriate chart types
- 2. Clarity, simplicity, and accuracy
- Creating basic visualizations
- 1. Using text and simple plotting tools
- 2. Line charts, bar charts, histograms, pie charts
- Interpreting and presenting results
- 1. Deriving conclusions and insights
- 2. Reporting findings clearly and concisely
|
| Topic 3: Working with Data and Performing Simple Analysis | 25% | - Exploratory data analysis
- 1. Identifying patterns, trends, and outliers
- 2. Calculating mean, median, mode, range, variance, standard deviation
- Data acquisition and loading
- 1. Importing data from external sources
- 2. Reading text, CSV, and structured files
- Data cleaning and preparation
- 1. Filtering, sorting, transforming data
- 2. Handling missing values, duplicates, and errors
- 3. Formatting and standardizing values
- Data aggregation and grouping
- 1. Summarizing and grouping datasets
|
| Topic 4: Python Basics for Data Analysis | 32.5% | - 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
- Built-in modules for data work
- 1. math, statistics, datetime, collections, csv
- 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 (Q19-Q24):
NEW QUESTION # 19
A dictionary is defined as d = {"a":1, "b":2}. The programmer accesses d["c"]. What happens when this line executes?
- A. Returns None
- B. Adds key automatically
- C. Returns 0
- D. Raises KeyError
Answer: D
Explanation:
Accessing a non-existent key using square brackets raises a KeyError. Python does not automatically create missing keys, so the program will terminate unless the exception is handled.
NEW QUESTION # 20
You are given a short Python script that uses both arithmetic and assignment operators. The variable x is initialized as 4, then updated using x *= 2 + 3. What will be printed when the final value of x is displayed?
Answer: C
Explanation:
Python evaluates the addition first because of operator precedence. The expression 2 + 3 equals
5, then x *= 5 multiplies the current value of x (4) by 5, resulting in a final value of 20.
NEW QUESTION # 21
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.
- A. Developing machine learning algorithms to predict user behavior.
- B. Creating summary tables and charts to describe patterns in historical data.
- C. Recommending pricing strategies using predictive modeling and A/B testing.
- D. Designing a data pipeline to ingest and process real-time sensor data.
Answer: B
Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.
NEW QUESTION # 22
How do the analysis and visualization stages of the data lifecycle typically work together?
- A. Analysis summarizes archived data, and visualization transforms it into structured tables for storage.
- B. Analysis identifies patterns and insights in processed data, which are then communicated through visualizations for informed decision-making.
- C. Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
- D. Analysis retrieves raw inputs from dashboards, while visualization uses that input to clean the data.
Answer: B
Explanation:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.
NEW QUESTION # 23
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?
- A. tho
- B. ytho
- C. yth
- D. ythn
Answer: C
Explanation:
Slicing includes the start index but excludes the end index. Characters at indices 1, 2, and 3 are selected, producing the substring "yth".
NEW QUESTION # 24
......
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