Step-by-step guide to practising data science techniques with Jupyter notebooks

Key Features

Acquire Python skills to do independent data science projects

Learn the basics of linear algebra and statistical science in Python way

Understand how and when they're used in data science

Build predictive models, tune their parameters and analyze performance in few steps

Cluster, transform, visualize, and extract insights from unlabelled datasets

Learn how to use matplotlib and seaborn for data visualization

Implement and save machine learning models for real-world business scenarios

Description
Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist.

The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, youll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models.

By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques.

Audience
The book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience.

Table of Contents

Data Science Fundamentals

Installing Software and Setting up

Lists and Dictionaries

Function and Packages

NumPy Foundation

Pandas and Dataframe

Interacting with Databases

Thinking Statistically in Data Science

How to import data in Python?

Cleaning of imported data

Data Visualization

Data Pre-processing

Supervised Machine Learning

Unsupervised Machine Learning

Handling Time-Series Data

Time-Series Methods

Case Study 1

Case Study 2

Case Study 3

Case Study 4

About the Author
Prateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.

 

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Spis treści

Data Science with Jupyter eBook -- spis treści

• Cover
• Data Science with Jupyter
• Copyright
• About the Author
• Preface
• Acknowledgements
• Erratta
• Contents
• 1. Data Science Fundamentals
• 2. Installing Software and Setting up
• 3. Lists and Dictionaries
• 4. Function and packages
• 5. NumPy Foundation
• 6. Pandas and Dataframe
• 7. Interacting with Databases
• 8. Thinking Statistically in Data Science
• 9. How to import data in Python?
• 10. Cleaning of imported data
• 11. Data Visualization
• 12. Data Pre-processing
• 13. Supervised Machine Learning
• 14. unsupervised Machine Learning
• 15. Handling time-Series Data
• 16. Time-Series Methods
• 17. Case Study-1
• 18. Case Study-2
• 19. case Study-3
• 20. Case Study-4
• Index