Python for Analysis

Python Road-map for Data Engineers

3-month road-map & some resources for learning Python for data analysis

Month 1: Python Basics

Week 1

• Introduction to Python and setting up your development environment.

• Learn basic syntax, data types, and control structures in Python.

• Resources:

  • Codecademy's Learn Python 3 (https://www.codecademy.com/learn/learn-python-3)

  • Introduction to Python Programming for Data Science: https://www.youtube.com/watch?v=eykoKxsYz5U

  • Python for Data Science Tutorial (Full Course): https://www.youtube.com/watch?v=_P7X8tMplsw

  • Python for Everybody by Charles Severance (https://www.py4e.com/book.php)

Week 2

• Learn how to work with data in Python using NumPy and Pandas.

• Understand data structures, indexing, and slicing.

• Resources:

  • NumPy User Guide (https://numpy.org/doc/stable/user/index.html)

  • Pandas User Guide (https://pandas.pydata.org/docs/user_guide/index.html)

  • Python Pandas Tutorial (Part 1): Getting Started with Data Analysis - https://www.youtube.com/watch?v=vmEHCJofslg

  • Python Pandas Tutorial (Part 2): Data Indexing and Selection - https://www.youtube.com/watch?v=ZyhVh-qRZPA

Week 3

• Understand how to manipulate and clean data using Pandas.

• Learn techniques for data exploration and visualization using Matplotlib.

• Resources:

  • Data Cleaning in Python by Kevin Markham (https://www.datacamp.com/courses/cleaning-data-with-python)

  • Data Visualization with Matplotlib by Kevin Markham (https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib)

  • Matplotlib Crash Course: Python Data Visualization - https://www.youtube.com/watch?v=ZmYPzESC5YY

  • Seaborn Tutorial: Python Data Visualization for Beginners - https://www.youtube.com/watch?v=6GUZXDef2U0

Week 4

• Practice data manipulation and visualization techniques by working on a small project.

• Use what you've learned to analyze a real-world dataset.

• Resources:

  • Kaggle Datasets (https://www.kaggle.com/datasets)

  • DataCamp Projects (https://www.datacamp.com/projects)

Month 2: Data Analysis with Pandas

Week 5

• Learn how to perform statistical analysis using Python.

• Understand probability distributions, hypothesis testing, and regression analysis.

• Resources:

  • Introduction to Statistical Learning by Gareth James et al. (https://www.statlearning.com/)

  • Statistics with Python Specialization by University of Michigan on Coursera (https://www.coursera.org/specializations/statistics-with-python)

Week 6

• Learn how to work with time series data in Python.

• Understand time series decomposition, forecasting, and visualization.

• Resources:

  • Time Series Analysis in Python by Kevin Markham (https://www.datacamp.com/courses/time-series-analysis-in-python)

  • Introduction to Time Series Forecasting with Python by Jason Brownlee (https://machinelearningmastery.com/time-series-forecasting-with-python/)

Week 7

• Understand machine learning algorithms and how they work.

• Learn about supervised and unsupervised learning.

• Resources:

  • Introduction to Machine Learning with Python by Andreas Müller and Sarah Guido (https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/)

  • Machine Learning Crash Course by Google (https://developers.google.com/machine-learning/crash-course)

Week 8

• Learn how to apply machine learning algorithms to real-world problems.

• Understand feature engineering, model selection, and evaluation.

• Resources:

  • Applied Machine Learning in Python by Kelleher, Tierney, and Tierney (https://www.amazon.com/Applied-Machine-Learning-Python-Kelleher/dp/1491960471)

  • Machine Learning Mastery by Jason Brownlee (https://machinelearningmastery.com/)

Month 3: Machine Learning with Scikit-Learn

Week 9

• Learn how to work with big data using PySpark.

• Understand Spark architecture, RDDs, and DataFrames.

• Resources:

  • Learning PySpark by Tomasz Drabas and Denny Lee (https://www.amazon.com/Learning

Week 10

• Linear regression

• Logistic regression

• k-Nearest Neighbours

Resources:

• Scikit-Learn official documentation: https://scikit-learn.org/stable/modules/classes.html

• DataCamp's Machine Learning with Tree-Based Models in

Generic Roadmap to learn Python for data analysis

1. Learn the Basics of Python: Before you dive into data analysis, you should first become comfortable with the basics of the Python programming language. This includes learning the syntax, data types, control structures, functions, and modules.

2. Learn NumPy: NumPy is a Python library that provides support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. NumPy is essential for data analysis in Python, so it's important to become familiar with this library.

3. Learn Pandas: Pandas is another important Python library for data analysis that provides support for data manipulation and analysis. Pandas provides a data structure called a DataFrame that is similar to a table in a relational database, and provides a lot of powerful methods for data manipulation.

4. Learn Data Visualization with Matplotlib: Matplotlib is a Python library for creating visualizations, including line charts, scatter plots, and histograms. Matplotlib is essential for data analysis and is often used in conjunction with Pandas.

5. Learn Scikit-Learn: Scikit-Learn is a Python library for machine learning that provides a wide range of algorithms for classification, regression, clustering, and more. Scikit-Learn is a powerful tool for data analysis and is often used in conjunction with Pandas and NumPy.

6. Learn Deep Learning with TensorFlow or PyTorch: If you want to get into deep learning, you should consider learning TensorFlow or PyTorch, which are popular Python libraries for building and training neural networks.

7. Practice on Real-World Data: Once you have learned the basics of Python and data analysis, it's important to practice your skills on real-world data sets. There are many publicly available data sets that you can use to practice your data analysis skills.

8. Attend Hackathons and Competitions: Finally, attending hackathons and competitions can be a great way to practice your data analysis skills and learn from others. There are many online platforms that host data science competitions, such as Kaggle and DataHack.