Data Science with Jupyter

Preface

Data Science Fundamentals

• What is Data?
• What is Data Science?
• What a Data Scientist actually do? 
• Real world use cases of Data Science?
• Why Python for Data Science?
• Conclusion

Installing Software and Setting Up

• System Requirements
• Downloading the Anaconda
• Installing the Anaconda in 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 list?
• How to create a list?
• Different list Manipulation operations
• Difference between lists and tuples
• What is dictionary?
• How to create a dictionary?
• Some operations with dictionary
• Conclusion

Function and Packages

• 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?
• Global and Local variable in a function
• What Is Lambda Function?
• Understanding Main in Python
• Conclusion

NumPy Foundation

• Importing a NumPy package
• Why NumPy array over List?
• NumPy array Attributes
• Creating NumPy arrays
• Accessing 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 Database
• Inserting Data In a Table
• Update a record
• How to join two tables
• How to join two tables
• Conclusion

Thinking Statistically in Data Science

• Statistics in Data Science
• Types of Statistical data/variables?
• Mean, Median and Mode
• Basics of Probability
• Statistical Distributions
• Pearson Correlation Coefficient
• Probability Density Function (PDF)
• Real World Example
• Statistical Inference and Hypothesis Testing
• Conclusion

How to import data in Python?

• Importing txt data
• Importing csv data
• Importing Excel data
• Importing JSON data
• Importing pickled data
•  Importing a compressed data
•  Conclusion

Cleaning of Imported Data

•  Know your data
•  Analysing Missing Values
•  Dropping Missing Values
• Automatically Fill Missing Values
•  How to scale and normalize data?
•  How to Parse Dates?
• How to apply character encoding?
• 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 & Pre-processing
•  Feature Engineering
•  Conclusion

Supervised Machine Learning

• Some common ML Terms
• Introduction to Machine Learning (ML)
• 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 variable in sklearn?
• Advanced technique to handle missing data
• Conclusion

Unsupervised Machine Learning

•  Why Unsupervised Learning?
• Unsupervised Learning Techniques
• Clustering
• 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
• Forecast future traffic to a Web page
• Conclusion

Case Study-1

• Case Study 1: Predict whether or not an applicant will be able to repay a loan
• Conclusion

Case Study-2

• Conclusion

Case Study-3

Case Study-4