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.

- Setup Python environment
- How to start jupyter notebook
- Open Jupyter Notebook in Browser of your Choice
- Install and check Packages
- Arithmetic operations
- Comparison or logical operations
- Assignment and augmented assignment in Python
- Variables naming conventions
- Types of variables in Python and typecasting
- Python Functions
- Exception handling in Python
- String manipulation and indexing
- Conditional and loops in Python
- Python data structure and containers
- Introduction to Python Numpy
- Introduction to Python SciPy
- Conduct One Sample and Two Sample Equality of Means T Test in Python
- Introduction to Python Pandas
- Python pivot tables
- Pandas join tables
- Missing value treatment
- Dummy coding of categorical variables
- Exploratory Data Analysis using Pandas-Profiling Package
- Basic statistics and visualization
- Data standardization or normalization
- Linear Regression with scikit- learn (Machine Learning library)
- Lasso, Ridge and Elasticnet Regularization in GLM
- Classification Algorithm Evaluation Metrics
- Logistic Regression with scikit- learn (Machine Learning library)
- Hierarchical clustering with Python
- K-means clustering with Scikit Python
- Decision trees using Scikit Python
- Regression Decision Trees with Scikit Python
- Support Vector Machine using Scikit Python
- Hyperparameters Optimization using Gridsearch and Cross Validations
- Principal Component Analysis (PCA) using Scikit Python- Dimension Reduction
- Linear Discriminant Analysis (LDA) using Scikit Python- Dimension Reduction and Classification
- Market Basket Analysis or Association Rules or Affinity Analysis or Apriori Algorithm
- Recommendation Engines using Scikit-Surprise
- Price Elasticity of Demand using Log-Log Ordinary Least Square (OLS) Model
- Timeseries Forecasting using Facebook Prophet Package
- Timeseries Forecasting using Pyramid ARIMA Package
- Model Persistence and Productionalization Using Python Pickle
- Deep Learning- Introduction to deep learning and environment setup
- Deep Learning- Multilayer perceptron (MLP) in Python
- Deep Learning- Convolution Neural Network (CNN) in Python
- Wordcloud using Python nltk library
- How to install H2O.ai web UI or flow for Machine Learning and Deep Learning
- Introduction to Ensemble Modeling and working example on Random Forest
- Face Recognition using Python Open Source Libraries
- Tweets Extraction and Sentiment Analysis using Tweepy and NLTK

Cheers!

You must be logged in to post a comment.