Apr 21, 2023

How would I learn Python for Data Science in 2023?

Python is a popular programming language known for its simplicity and versatility. If you're interested in learning how to code in Python, you might be wondering how long it takes to master the language. While becoming an expert in any programming language requires a lot of time and dedication, you can learn Python in just three months if you follow a structured approach.

Here's a 3-Month Plan for Beginner to Advance level learning plan for Python with daily topics -

Beginner Level ( 1st Month )

Week 1: Introduction to Python and Data Science

  • Day 1: Installing Python and basic Python syntax
  • Day 2: Variables, data types, and operators
  • Day 3: Control statements and loops
  • Day 4: Functions and libraries
  • Day 5: Introduction to data science and its applications

Course : Python for Everybody by Dr. Charles Severance on edX - Free Course

Book : Python Crash Course by Eric Matthes - Free Book

YouTube Channel : Corey Schafer

Week 2: Data Analysis with Pandas

  • Day 1: Introduction to Pandas and its data structures
  • Day 2: Reading and writing data from various sources
  • Day 3: Data cleaning and preprocessing
  • Day 4: Data wrangling and transformation
  • Day 5: Data aggregation and group by operations

Course: Easier data analysis in Python with pandas by Kevin Markham on Data School - Free Course

Book : Python for Data Analysis by Wes McKinney - Free Book

YouTube Channel : Data School

Week 3: Data Visualization with Matplotlib and Seaborn

  • Day 1: Introduction to data visualization and Matplotlib
  • Day 2: Basic plots and charts with Matplotlib
  • Day 3: Advanced plots and charts with Matplotlib
  • Day 4: Introduction to Seaborn and its plotting functions
  • Day 5: Creating advanced visualizations with Seaborn

Course: Visualizing Data with Python By IBM on edX - Free Course

Book: Python Data Science Handbook by Jake VanderPlas - Free Book

YouTube Channel: PyData

Week 4: Probability and Statistics

  • Day 1: Introduction to probability and its concepts
  • Day 2: Descriptive statistics and summary metrics
  • Day 3: Inferential statistics and hypothesis testing
  • Day 4: Probability distributions and their applications
  • Day 5: Bayesian statistics and its applications

Course: 1. Intro to Statistics By Udacity - Free Course

               2. Intro to Descriptive statistics By Udacity - Free Course

               3. Intro to Inferential Statistics By Udacity - Free Course

               4. Bayesian Statistics: From Concept to Data Analysis By Coursera - Free Audit

Book: Think Stats by Allen B. Downey - Free Book

YouTube Channel: StatQuest with Josh Starmer

Intermediate Level ( 2nd Month )

Week 5: Machine Learning with Scikit-Learn

  • Day 1: Introduction to machine learning
  • Day 2: Supervised learning algorithms in Scikit-Learn
  • Day 3: Unsupervised learning algorithms in Scikit-Learn
  • Day 4: Model selection and validation techniques
  • Day 5: Hyperparameter tuning and optimization techniques

Course: Applied Machine Learning in Python On Coursera - Free Audit

Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron - Free Book

YouTube Channel: Sentdex

Week 6: Linear Algebra and Calculus for Data Science

  • Day 1: Introduction to linear algebra and its concepts
  • Day 2: Vectors, matrices, and their operations
  • Day 3: Linear transformations and their applications
  • Day 4: Introduction to calculus and its concepts
  • Day 5: Applications of calculus in data science

Course: Linear Algebra by Gilbert Strang on MIT OpenCourseWare - Free Course

Book: Linear Algebra and Its Applications by Gilbert Strang- Free Book

YouTube Channel: 3Blue1Brown

Week 7: Deep Learning with TensorFlow or PyTorch

  • Day 1: Introduction to deep learning and neural networks
  • Day 2: Building and training simple neural networks with TensorFlow or PyTorch
  • Day 3: Convolutional neural networks for image classification
  • Day 4: Recurrent neural networks for sequence modeling
  • Day 5: Advanced topics in deep learning, such as transfer learning and reinforcement learning

Course: Deep Learning Specialization by Andrew Ng on Coursera - Free Audit

Book: Deep Learning with Python by François Chollet - Free Book

YouTube Channel: TensorFlow

Week 8: Natural Language Processing (NLP) with NLTK

  • Day 1: Introduction to NLP and NLTK
  • Day 2: Text preprocessing and normalization with NLTK
  • Day 3: Part-of-speech tagging and named entity recognition with NLTK
  • Day 4: Sentiment analysis and text classification with NLTK
  • Day 5: Advanced topics in NLP, such as text summarization and machine translation

Book: Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper - Free Book

YouTube Channel: Codebasics

Advance Level ( 3rd Month )

Week 9: Big Data Processing with Apache Spark

  • Day 1: Introduction to big data processing and Apache Spark
  • Day 2: Working with Spark DataFrames and SQL
  • Day 3: Distributed computing with Spark RDDs
  • Day 4: Machine learning with Spark MLlib
  • Day 5: Streaming and real-time processing with Spark Streaming

Course: Big Data Analytics Using Spark on edX - Free Audit

Book: Learning Spark by Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia - Free Book

YouTube Channel: Databricks

Week 10: Advanced Topics in Data Science

  • Day 1: Dimensionality reduction and feature selection
  • Day 2: Ensemble methods and model stacking
  • Day 3: Time Series Analysis and Forecasting
  • Day 4: Clustering and unsupervised learning techniques
  • Day 5: Model interpretation and explainability techniques

Course: Machine Learning Specialization by Andrew NG on Coursera - Free Audit

Book: Machine Learning Yearning by Andrew Ng - Free Book

YouTube Channel: StatQuest with Josh Starmer

Week 11: Data Engineering and Pipeline Development

  • Day 1: Introduction to data engineering and pipeline development
  • Day 2: Data ingestion and processing with Apache Kafka and Apache NiFi
  • Day 3: ETL (extract, transform, load) techniques with Apache Airflow
  • Day 4: Data warehousing and storage with Apache Hadoop and Hive
  • Day 5: Building scalable data pipelines with cloud services, such as AWS and GCP

Course: Data Engineering Nanodegree Program by Udacity - Paid

Book: Designing Data-Intensive Applications by Martin Kleppmann - Free Book

Week 12: Final Project and Review

  • Day 1: Designing and implementing a data science project
  • Day 2-4: Working on the final project and incorporating all the skills learned throughout the program
  • Day 5: Final presentation and review of the project, along with a review of the entire program

Course: Applied Data Science Capstone by IBM on Coursera - Free Audit

Book: Data Science Projects with Python by Stephen Klosterman

YouTube Channel: Kaggle

Note : The duration of each week can be adjusted based on the learner's pace and schedule. Additionally, the order of the topics can be modified based on the learner's interests and priorities. This plan provides a comprehensive roadmap for learning Python for data science beginners, starting from the basics and progressing to advanced topics.

Good Luck!

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