| Section | Weight | Objectives |
|---|
| Topic 1: Python Basics for Data Analysis | 32.5% | - Control flow and functions
- 1. Basic exception handling
- 2. Defining and calling functions, parameters, return values
- 3. Conditional statements, loops, iteration
- 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
|
| Topic 2: Working with Data and Performing Simple Analysis | 25% | - Exploratory data analysis
- 1. Calculating mean, median, mode, range, variance, standard deviation
- 2. Identifying patterns, trends, and outliers
- Data aggregation and grouping
- 1. Summarizing and grouping datasets
- Data cleaning and preparation
- 1. Filtering, sorting, transforming data
- 2. Handling missing values, duplicates, and errors
- 3. Formatting and standardizing values
- Data acquisition and loading
- 1. Reading text, CSV, and structured files
- 2. Importing data from external sources
|
| Topic 3: Data Visualization and Communication | 20% | - Principles of effective data visualization
- 1. Choosing appropriate chart types
- 2. Clarity, simplicity, and accuracy
- Interpreting and presenting results
- 1. Reporting findings clearly and concisely
- 2. Deriving conclusions and insights
- Creating basic visualizations
- 1. Using text and simple plotting tools
- 2. Line charts, bar charts, histograms, pie charts
|
| Topic 4: Introduction to Data and Data Analysis Concepts | 22.5% | - Basic statistical concepts
- 1. Descriptive vs inferential statistics
- 2. Population, sample, variable, observation
- Data types and measurement scales
- 1. Qualitative vs quantitative data
- 2. Nominal, ordinal, interval, ratio scales
- Definition and classification of data
- 1. Role of data in decision-making and business
- 2. Process of turning raw data into insights
- 3. Difference between data, information, and knowledge
- Data lifecycle and ethical considerations
- 1. Data collection, storage, processing, usage, and sharing
- 2. Privacy, security, bias, and fairness in data
|
