Learn python for data analysis in 30 days

A girl learning python for data analysis

Python is a highly versatile programming language that has gained immense popularity for its applications in data analysis, machine learning, and artificial intelligence. In this comprehensive guide, I will outline a rigorous 30-day plan to learn Python for data analysis. This plan is designed to provide you with an intensive learning experience and is completely free of cost, with no subscription or fees required. With a curated set of daily activities, resources, and video tutorials, you will have access to all the tools and knowledge necessary to become a proficient Python programmer, capable of utilizing this language for data analysis in just a matter of one month.

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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/

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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

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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

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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.

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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.

Thank you for taking the time to read my post. If you have any questions or would like to reach out to me, feel free to email me at sheriffjbabu@gmail.com. I would love to hear from you and will do my best to respond promptly. Thank you again for your time, and have a great day!

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