Python for Data Analysis

Data Analysis in Python provides a concise overview of essential Python syntax, data structures, and the primary libraries used for data manipulation (NumPy, Pandas) and basic visualization (Matplotlib). It establishes the fundamental skills required to manage varied data scenarios—ranging from array and DataFrame operations to generating exploratory plots—and lays the groundwork for more advanced analytical procedures.

Introduction to Python

Learning Objectives

Explain Python’s significance in data analysis and summarize its key features. Compare popular Python IDEs (Spyder, Jupyter Notebook) to determine their suitability for various tasks.

Indicative Content

• Python’s history, extensive standard library, and active community.

• A comparison of IDEs like Spyder and Jupyter Notebook for coding and data analysis.

Python Basics, Writing Your First Python Program

Learning Objectives

Demonstrate how to set up Python and configure the environment in Spyder. Construct and run basic scripts with effective code documentation.

Indicative Content

• Performing arithmetic operations and displaying output using print().

• Adding comments with # for code documentation.

Understanding Data Types in Python

Learning Objectives

Identify Python’s built-in data types and demonstrate how to convert between them for seamless data operations.

Indicative Content

• Working with integers (int), floats (float), and string manipulation.

• Using lists, tuples, and dictionaries for indexing, updating, and retrieving values.

Topic: Functions and Operators

Learning Objectives

Apply numeric functions (e.g., round(), math.log()) and arithmetic operators to perform calculations. Use relational and membership operators to evaluate data comparisons.

Indicative Content

• Understanding operator precedence and common numeric functions.

• Examples of membership testing in collections using in.

String Manipulation

Learning Objectives

Execute string operations (split, replace, join) and apply formatting techniques for clarity and improved presentation.

Indicative Content

• String slicing, indexing, and methods such as split(), replace(), join(), format(), and f-strings.

Conditional Statements

Learning Objectives

Implement conditional logic using if, if-else, and if-elif-else constructs, and design nested conditions to handle complex decision-making.

Indicative Content

• Syntax of conditional statements and the use of logical operators (and, or, not).

Python Libraries for Data Analysis

Learning Objectives

Identify and import key data analysis libraries (NumPy, Pandas, Matplotlib) and explain their roles in data manipulation, visualization, and scientific computing.

Indicative Content

• Importing libraries with import numpy as np, import pandas as pd, and import matplotlib.pyplot as plt.

• Overview of their applications in data tasks.

Data Structures in Python

Learning Objectives

Manipulate Pandas Series and DataFrames for data analysis, and execute indexing, slicing, and filtering operations on datasets.

Indicative Content

• Creating DataFrames and using .loc[] and .iloc[] for data access.

• Converting dictionaries to Series and understanding their structure.

File and Directory Management

Learning Objectives

Manage file paths and directories with the os module, and organize, rename, and delete files programmatically.

Indicative Content

• Using os.getcwd(), os.chdir(), os.mkdir(), os.remove(), and os.rename() for file and directory operations.

Types of Loops and Their Applications

Learning Objectives

Employ for and while loops for iterative tasks, and control loop execution with break and continue statements.

Indicative Content

• Iterating over lists and dictionaries with for loops.

• Using while loops for condition-based repetition and nesting loops for complex iterations.

Introduction to Date and Time Handling

Learning Objectives

Convert string data into date/time objects and format them effectively, and extract specific components (year, month, day) from date objects.

Indicative Content

• Using the datetime library with functions like datetime.strptime() and strftime().

• Parsing dates in Pandas with to_datetime().

Merging and Formatting Dates

Learning Objectives

Merge separate year, month, and day columns into a single date field, and resolve inconsistencies in date formats.

Indicative Content

• Using to_datetime() with multiple columns.

• Managing mixed formats such as MM-DD-YYYY and YYYY-MM-DD.

Tools and Methodologies

Tools & Methodologies

• Software: Python (3.x), IDEs like Spyder or Jupyter Notebook.

• Key Libraries: NumPy, Pandas, Matplotlib for initial visualization; optional introduction to Seaborn.

• Methodologies:

  • Employ vectorized operations with NumPy and Pandas for efficiency.

  • Validate data shapes and manage directories systematically.