Python Roadmap

Complete roadmap to learn Python for data analysis

Step 1: Fundamentals of Python

1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)

2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions

3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions

4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)

Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)

2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully

3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation

Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations

2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data

3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn

Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering

2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers

3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions

Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models

2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models

3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)

Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects

2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects

👨‍💻 FREE Resources to Learn & Practice Python 

1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33

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ENJOY LEARNING 👍👍

When you join a company and their database is an Excel workbook 😂

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Useful websites to practice and enhance your Data Analytics skills
👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.iss.one/sqlspecialist/232?single

2. Python

https://www.learnpython.org/

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://www.datacamp.com/courses/free-introduction-to-r

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://www.w3schools.com/EXCEL/index.php

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ENJOY LEARNING 👍👍

I’m a data analyst.

I clean and prepare data daily for my job.

This is how I would learn data cleaning for 2025:
✅Learn how to handle missing values
✅Learn data normalization and standardization
✅Learn to remove duplicates
✅Learn how to handle outliers
✅Learn how to merge and join datasets
✅Learn to identify and correct data inconsistencies

Data cleaning is an essential step to make your analysis meaningful.

Skills a data analyst needs:

1. Technical skills
📍 SQL
📍 Excel
📍 Data viz (Power BI/Tableau)

2. Soft skills
📍 Problem solving
📍 Communication
📍 Thinking (critical + analytical)

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Hope it helps :)