Learn Data Science using Python Step by Step

Here is how you can learn Data Science using Python step by step. Please feel free to reach out to me on my personal email id rpdatascience@gmail.com if you have any question or comments related to any topics.

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  1. Setup Python environment
  2. How to start jupyter notebook
  3. Open Jupyter Notebook in Browser of your Choice
  4. Install and check Packages
  5. Arithmetic operations
  6. Comparison or logical operations
  7. Assignment and augmented assignment in Python
  8. Variables naming conventions
  9. Types of variables in Python and typecasting
  10. Python Functions
  11. Exception handling in Python
  12. String manipulation and indexing
  13. Conditional and loops in Python
  14. Python data structure and containers
  15. Introduction to Python Numpy
  16. Introduction to Python SciPy
  17. Conduct One Sample and Two Sample Equality of Means T Test in Python
  18. Introduction to Python Pandas
  19. Python pivot tables
  20. Pandas join tables
  21. Missing value treatment
  22. Dummy coding of categorical variables 
  23. Exploratory Data Analysis using Pandas-Profiling Package
  24. Basic statistics and visualization
  25. Data standardization or normalization
  26. Linear Regression with scikit- learn (Machine Learning library)
  27. Lasso, Ridge and Elasticnet Regularization in GLM
  28. Classification Algorithm Evaluation Metrics
  29. Logistic Regression with scikit- learn (Machine Learning library)
  30. Hierarchical clustering with Python
  31. K-means clustering with Scikit Python
  32. Decision trees using Scikit Python
  33. Regression Decision Trees with Scikit Python
  34. Support Vector Machine using Scikit Python
  35. Hyperparameters Optimization using Gridsearch and Cross Validations
  36. Principal Component Analysis (PCA) using Scikit Python- Dimension Reduction
  37. Linear Discriminant Analysis (LDA) using Scikit Python- Dimension Reduction and Classification
  38. Market Basket Analysis or Association Rules or Affinity Analysis or Apriori Algorithm
  39. Recommendation Engines using Scikit-Surprise
  40. Price Elasticity of Demand using Log-Log Ordinary Least Square (OLS) Model
  41. Timeseries Forecasting using Facebook Prophet Package
  42. Timeseries Forecasting using Pyramid ARIMA Package
  43. Model Persistence and Productionalization Using Python Pickle
  44. Deep Learning- Introduction to deep learning and environment setup
  45. Deep Learning- Multilayer perceptron (MLP) in Python
  46. Deep Learning- Convolution Neural Network (CNN) in Python
  47. Wordcloud using Python nltk library

Cheers!

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