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. Outliers, Missing Values, Dummy Coding
  24. Exploratory Data Analysis using Pandas-Profiling Package
  25. Basic statistics and visualization
  26. Data standardization or normalization
  27. Machine Learning & Data Science Intro and FAQs
  28. Linear Regression with scikit- learn (Machine Learning library)
  29. Lasso, Ridge and Elasticnet Regularization in GLM
  30. Classification Algorithm Evaluation Metrics
  31. Logistic Regression with scikit- learn (Machine Learning library)
  32. Hierarchical clustering with Python
  33. K-means clustering with Scikit Python
  34. Decision trees using Scikit Python
  35. Decision Trees Basics and Modeling
  36. Regression Decision Trees with Scikit Python
  37. Support Vector Machine using Scikit Python
  38. Hyperparameters Optimization using Gridsearch and Cross Validations
  39. Principal Component Analysis (PCA) using Scikit Python- Dimension Reduction
  40. Linear Discriminant Analysis (LDA) using Scikit Python- Dimension Reduction and Classification
  41. Market Basket Analysis or Association Rules or Affinity Analysis or Apriori Algorithm
  42. Recommendation Engines using Scikit-Surprise
  43. Price Elasticity of Demand using Log-Log Ordinary Least Square (OLS) Model
  44. Timeseries Forecasting using Facebook Prophet Package
  45. Timeseries Forecasting using Pyramid ARIMA Package
  46. Model Persistence and Productionalization Using Python Pickle
  47. Deep Learning- Introduction to deep learning and environment setup
  48. Deep Learning- Multilayer perceptron (MLP) in Python
  49. Deep Learning- Convolution Neural Network (CNN) in Python
  50. Wordcloud using Python nltk library
  51. How to install H2O.ai web UI or flow for Machine Learning and Deep Learning
  52. Introduction to Ensemble Modeling and working example on Random Forest
  53. Face Recognition using Python Open Source Libraries
  54. Tweets Extraction and Sentiment Analysis using Tweepy and NLTK

Cheers!

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