Week 1

Day 1: Introduction to Python, installing Anaconda

Watch this video: https://www.youtube.com/watch?v=TcSAln46u9U

Resource: https://www.anaconda.com/products/distribution

Day 2: Jupyter Notebook, Variables and Data Types

Watch this video: https://www.youtube.com/watch?v=HW29067qVWk

Resource: https://jupyter-notebook.readthedocs.io/en/stable/

Day 3: Lists and Dictionaries

Watch this video: https://www.youtube.com/watch?v=R-HLU9Fl5ug

Resource: https://docs.python.org/3/tutorial/datastructures.html

Day 4: Conditional statements, loops and functions

Watch this video: https://www.youtube.com/watch?v=DZwmZ8Usvnk

Resource: https://docs.python.org/3/tutorial/controlflow.html

Day 5: Reading and writing files

Watch this video: https://www.youtube.com/watch?v=Kr0f2Sr_gxo

Resource: https://docs.python.org/3/tutorial/inputoutput.html

Week 2

Day 6: Pandas — Introduction and Data Structures (Series and DataFrames)

Watch this video: https://www.youtube.com/watch?v=dcqPhpY7tWk

Resource: https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html

Day 7: Pandas — Data cleaning and preprocessing

Watch this video: https://www.youtube.com/watch?v=0GrciaGYzV0

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/ cleaning.html

Day 8: Pandas — Data visualization with Matplotlib

Watch this video: https://www.youtube.com/watch?v=r_3lopL6vhU

Resource: https://matplotlib.org/stable/contents.html

Day 9: Pandas — Grouping and aggregation

Watch this video: https://www.youtube.com/watch?v=xPPs59pn6qU

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html

Day 10: Pandas — Merging and joining data

Watch this video: https://www.youtube.com/watch?v=h_tKLc8QvGA

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html

Week 3

Day 11: Numpy — Introduction and arrays

Watch this video: https://www.youtube.com/watch?v=GB9ByFAIAH4

Resource: https://numpy.org/doc/stable/user/quickstart.html

Day 12: Numpy — Broadcasting and advanced array operations

Watch this video: https://www.youtube.com/watch?v=YrOgIoYtl14

Resource: https://numpy.org/doc/stable/user/basics.broadcasting.html

Day 13: Matplotlib — Line plots and scatter plots

Watch this video: https://www.youtube.com/watch?v=0P7QnIQDBJY

Resource: https://matplotlib.org/stable/tutorials/introductory/sample_plots.html

Day 14: Matplotlib — Histograms, bar plots and pie charts

Watch this video: https://www.youtube.com/watch?v=_Z1eLJJ6QgU

Resource: https://matplotlib.org/stable/tutorials/introductory/pyplot.html

Week 4

Day 15: Seaborn — Introduction and basic plots

Watch this video: https://www.youtube.com/watch?v=oMpYpgE0XU0

Resource: https://seaborn.pydata.org/tutorial.html

Day 16: Seaborn — Advanced plots and visualization

Watch this video: https://www.youtube.com/watch?v=I6ypD7qv3Z8

Resource: https://seaborn.pydata.org/examples/index.html

Day 17: Data analysis with Pandas

Watch this video: https://www.youtube.com/watch?v=vmEHCJofslg

Resource: https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html

Day 18: Data visualization with Matplotlib and Seaborn

Watch this video: https://www.youtube.com/watch?v=0P7QnIQDBJY

Resource: https://matplotlib.org/stable/tutorials/introductory/sample_plots.html

Watch this video: https://www.youtube.com/watch?v=oMpYpgE0XU0

Resource: https://seaborn.pydata.org/tutorial.html

Day 19: Machine learning with scikit-learn

Watch this video: https://www.youtube.com/watch?v=HW29067qVWk

Resource: https://scikit-learn.org/stable/tutorial/basic/tutorial.html

Day 20: Working with real-world datasets

Watch this video: https://www.youtube.com/watch?v=44jq6ano5n0Resource: Kaggle (https://www.kaggle.com) — a platform for data analysis and machine learning competitions.

Day 21: Pandas — Basic operations and data cleaning

Watch this video: https://www.youtube.com/watch?v=vmEHCJofslg

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html

Day 22: Pandas — Grouping and aggregating data

Watch this video: https://www.youtube.com/watch?v=zYHvwRdhsLo

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html

Day 23: Pandas — Merging and joining data

Watch this video: https://www.youtube.com/watch?v=h_gEjK9c9UE

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html

Day 24: Pandas — Time series analysis

Watch this video: https://www.youtube.com/watch?v=W0OyjKk-zUg

Resource: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html

Week 5

Day 25: Scikit-learn — Introduction and basic operations

Watch this video: https://www.youtube.com/watch?v=HW29067qVWk

Resource: https://scikit-learn.org/stable/tutorial/basic/tutorial.html

Day 26: Scikit-learn — Linear regression and regularization

Watch this video: https://www.youtube.com/watch?v=_Z1eLJJ6QgU

Resource: https://scikit-learn.org/stable/modules/linear_model.html

Day 27: Scikit-learn — Decision trees and random forests

Watch this video: https://www.youtube.com/watch?v=7VeUPuFGJHk

Resource: https://scikit-learn.org/stable/modules/tree.html

Day 28: Deep learning with TensorFlow

Watch this video: https://www.youtube.com/watch?v=2FmcHiLCwTU

Resource: https://www.tensorflow.org/tutorials

Day 29: Natural language processing with NLTK

Watch this video: https://www.youtube.com/watch?v=FLZvOKSCkxY

Resource: http://www.nltk.org/book/

Day 30: Practice, practice, practice!

Try working on a project that combines the skills you have learned so far, such as building a machine learning model for text classification using TensorFlow and NLTK.

Here are the prerequisites for the 30-day plan to learn Python for data analysis

Basic knowledge of programming concepts: Variables, functions, loops, and basic data structures such as lists and dictionaries.

Familiarity with computer systems: Knowledge of how to install software, create files, and navigate the command line.

Familiarity with mathematical concepts: Basic understanding of statistics, probability, and linear algebra.

Access to a computer with an internet connection: In order to use the resources provided in the plan and practice coding exercises.

Willingness to dedicate time: The plan requires a daily commitment of at least 2–3 hours to complete all the activities and resources.

Install a Python development environment: You will need a Python installation and an Integrated Development Environment (IDE) to write, run, and debug your Python code.

By having these prerequisites in place, you will be better equipped to embark on the journey of learning Python for data analysis and achieve the best results from the 30-day plan.

In conclusion, learning Python for data analysis can be a challenging but rewarding experience. With the right resources and a well-structured plan, you can achieve your goal of becoming proficient in using Python for data analysis in just one month. This 30-day intensive plan provides you with all the tools and knowledge you need to succeed, including daily activities, resources, and video tutorials, all of which are completely free. So, take the first step today and commit to the plan. Embrace the challenge, and soon you will be on your way to becoming a skilled Python programmer capable of analyzing data and making informed decisions based on your insights.

Start your journey to mastering Python for data analysis today.