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.

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
48. How to install H2O.ai web UI or flow for Machine Learning and Deep Learning
49. Introduction to Ensemble Modeling and working example on Random Forest
50. Face Recognition using Python Open Source Libraries
51. Tweets Extraction and Sentiment Analysis using Tweepy and NLTK

Cheers!