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EIEIO / DATA SCIENCE

Introduction to Data Analysis Using Python for Beginners

Course outline

5 days
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Duration

Introduction to Data Analysis Using Python for Beginners

5 days

Overview

This course is tailored for beginners who want to acquire fundamental skills in data analysis using the Python programming language. Participants will learn to work with popular Python libraries such as Pandas, NumPy, and Matplotlib to manipulate, analyze, and visualize data. The course covers basic programming concepts, data handling, exploration, and visualization techniques.

Prerequisites

No prior programming experience is required, but basic computer literacy is recommended. The course assumes no prior knowledge of Python or data analysis.

Lesson 1: Python Basics

  • Python syntax, variables, and data types
  • Control flow (if statements, loops)

Lesson 2: Introduction to Jupyter Notebooks

  • Setting up and using Jupyter Notebooks
  • Markdown and code cells

Lesson 1: Lists, Tuples, and Sets

  • Understanding and manipulating lists, tuples, and sets
  • List comprehensions

Lesson 2: Dictionaries

  • Creating and working with dictionaries
  • Dictionary comprehensions

Lesson 1: NumPy Arrays

  • Creating and manipulating arrays
  • Array operations and broadcasting

Lesson 2: Basic Statistical Operations with NumPy

  • Calculating mean, median, and standard deviation
  • Random sampling with NumPy

Lesson 1: Introduction to Pandas

  • Series and DataFrame objects
  • Reading and writing data with Pandas

Lesson 2: Data Cleaning and Preparation

  • Handling missing data
  • Data type conversions and transformations

Lesson 1: Descriptive Statistics

  • Summary statistics and distribution measures
  • Frequency tables and histograms

Lesson 2: Data Visualization with Matplotlib

  • Creating basic plots (line, bar, scatter)
  • Customizing plots and adding labels

Lesson 1: Filtering and Sorting DataFrames

  • Selecting and sorting data
  • Conditional filtering

Lesson 2: Merging and Combining DataFrames

  • Concatenating and merging data
  • Handling duplicates and merging strategies

Lesson 1: Introduction to Seaborn

  • Seaborn vs. Matplotlib
  • Creating advanced visualizations (heatmaps, pair plots)

Lesson 2: Plotting Time Series Data

  • Visualizing trends and patterns in time series data
  • Resampling and plotting time-based data

Lesson 1: Hypothesis Testing

  • T-tests, chi-square tests, and ANOVA
  • Understanding p-values and confidence intervals

Lesson 2: Correlation and Regression

  • Correlation analysis
  • Linear regression with statsmodels

Lesson 1: Applying Data Analysis to Business Problems

  • Case studies from various industries
  • Practical applications of Python in real-world scenarios

Lesson 2: Ethical Considerations in Data Analysis

  • Responsible data handling and analysis
  • Avoiding bias and ensuring data privacy

Module 10: Final Project and Practical Application

  • Capstone project: Analyzing a real dataset using Python
  • Presenting findings and insights
  • Peer review and feedback

Programme scope, delivery arrangements and suitability are confirmed during enquiry.