Practical Data Science With Jupyter

Preface

Data Science Fundamentals

• What is data?
• What is data science?
• What does a data scientist do?
• Real-world use cases of data science
• Why Python for data science?
• Conclusion

Installing Software and System Setup

• System requirements
• Downloading Anaconda
• Installing the Anaconda on Windows
• Installing the Anaconda in Linux
• How to install a new Python library in Anaconda?
• Open your notebook - Jupyter
• Know your notebook
• Conclusion

Lists and Dictionaries

• What is a list?
• How to create a list?
• Different list manipulation operations
• Difference between Lists and Tuples
• What is a Dictionary?
• How to create a dictionary?
• Some operations with dictionary
• Conclusion

Package, Function, and Loop

• The help() function in Python
• How to import a Python package?
• How to create and call a function?
• Passing parameter in a function
• Default parameter in a function
• How to use unknown parameters in a function?
• A global and local variable in a function
• What is a Lambda function?
• Understanding main in Python
• while and for loop in Python
• Conclusion

NumPy Foundation

• Importing a NumPy package
• Why use NumPy array over list?
• NumPy array attributes
• Creating NumPy arrays
• Accessing an element of a NumPy array
• Slicing in NumPy array
• Array concatenation
• Conclusion

Pandas and DataFrame

• Importing Pandas
• Pandas data structures
• .loc[] and .iloc[]
• Some Useful DataFrame Functions
• Handling missing values in DataFrame
• Conclusion

Interacting with Databases

• What is SQLAlchemy?
• Installing SQLAlchemy package
• How to use SQLAlchemy?
• SQLAlchemy engine configuration
• Creating a table in a database
• Inserting data in a table
• Update a record
• How to join two tables
• Conclusion

Thinking Statistically in Data Science

• Statistics in data science
• Types of statistical data/variables
• Basics of probability
• Statistical distributions
• Pearson correlation coefficient
• Probability Density Function (PDF)
• Real-world example
• Statistical inference and hypothesis testing
• Conclusion

Cleaning of Imported Data

• Know your data
• Analyzing missing values
• Dropping missing values
• Automatically fill missing values
• How to scale and normalize data?
• How to parse dates?
• How to apply character encoding?
• Cleaning inconsistent data
• Conclusion

Data Visualization

• Bar chart
• Line chart
• Histograms
• Scatter plot
• Stacked plot
• Box plot
• Conclusion

Data Pre-processing

• About the case-study
• Importing the dataset
• Exploratory data analysis
• Data cleaning and pre-processing
• Feature Engineering
• Conclusion

Supervised Machine Learning

• Some common ML terms
• Introduction to machine learning (ML)
• Unsupervised learning
• List of common ML algorithms
• Supervised ML fundamentals
• Solving a classification ML problem
• Solving a regression ML problem
• How to tune your ML model?
• How to handle categorical variables in sklearn?
• The advanced technique to handle missing data
• Conclusion

Unsupervised Machine Learning

• Why unsupervised learning?
• Unsupervised learning techniques
• Principal Component Analysis (PCA)
• Case study
• Validation of unsupervised ML
• Conclusion

Handling Time-Series Data

• Why time-series is important?
• How to handle date and time?
• Transforming a time-series data
• Manipulating a time-series data
• Comparing time-series growth rates
• How to change time-series frequency?
• Conclusion

Time-Series Methods

• What is time-series forecasting?
• Basic steps in forecasting
• Time-series forecasting techniques
• Autoregression (AR)
• Moving Average (MA)
• Forecast future traffic to a web page
• Conclusion

Case Study-1

• Predict whether or not an applicant will be able to repay a loan
• Conclusion

Case Study-2

• Build a prediction model that will accurately classify which text messages are spam
• Conclusion

Case Study-3

• Build a film recommendation engine
• Conclusion

Case Study-4

• Predict house sales in King County, Washington State, USA, using regression
• Conclusion

Python Virtual Environment

Introduction to An Advanced Algorithm - CatBoost

• What is a Gradient Boosting algorithm?
• Introduction to CatBoost
• Install CatBoost in Python virtual environment
• How to solve a classification problem with CatBoost?
• Push your notebook in your GitHub repository
• Conclusion

Revision of All Lessons' Learning

• Conclusion

How to Import Data in Python?

• Importing text data
• Importing CSV data
• Importing Excel data
• Importing JSON data
• Importing pickled data
• Importing a compressed data
• Conclusion