Introduction to Data Analysis
Embarking on a journey to become a data analyst involves understanding various stages of data handling and interpretation. This course outline is structured to guide you from a beginner to a full-stack data analyst using Python as the primary development language. By dividing the learning process into manageable daily milestones, you can progressively build your skills.
Week 1: Getting Started with Python
Day 1: Introduction to Python and setting up the development environment.
Day 2: Learning basic syntax, variables, and data types.
Day 3: Control flow statements like loops and conditionals.
Day 4: Introduction to functions and modules.
Day 5-6: Working with lists, dictionaries, and tuples.
Day 7: Recap and exercises on basic Python concepts.
Week 2: Data Handling and Manipulation
Day 8-9: Introduction to Pandas for data manipulation.
Day 10-11: Data loading, cleaning, and preprocessing.
Day 12: Exploratory data analysis using Pandas.
Day 13-14: Advanced data manipulation techniques.
Week 3: Data Visualization
Day 15-16: Introduction to Matplotlib and Seaborn.
Day 17-18: Creating bar charts, line plots, and scatter plots.
Day 19-20: Customizing and annotating plots.
Day 21: Recap and project on data visualization.
Week 4: Introduction to SQL and Databases
Day 22-23: Basics of SQL and setting up a database.
Day 24-25: Performing SQL queries and data manipulation.
Day 26: Integrating SQL with Python.
Day 27-28: Advanced SQL queries and database operations.
Week 5: Machine Learning Fundamentals
Day 29-30: Introduction to machine learning and scikit-learn.
Day 31: Understanding supervised learning.
Day 32-33: Implementing simple linear regression and classification.
Day 34: Introduction to unsupervised learning.
Day 35: Recap and project on machine learning.
Additional Resources and Recommendations
To complement your learning, several online resources can be of great help:
- Coursera: Courses like “Python for Everybody” by the University of Michigan and “Applied Data Science with Python” by IBM.
- Alison: Basic courses on data science and analytics.
- edX: Programs like “Data Science MicroMasters” by UC San Diego.
Recommended books include “Python for Data Analysis” by Wes McKinney and “Data Science from Scratch” by Joel Grus. These resources will solidify your foundation and smooth your journey towards becoming a proficient data analyst.