Data Science with Jupyter: Master Data Science skills with easy-to-follow Python examples

Gupta, Prateek


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

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, you'll 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.

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.

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
Table of 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
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.







- 獲取Python技能以進行獨立的數據科學項目
- 以Python方式學習線性代數和統計科學的基礎知識
- 了解它們在數據科學中的使用方式和時機
- 在幾個步驟中構建預測模型,調整其參數並分析性能
- 對無標籤數據集進行聚類、轉換、可視化和提取洞察
- 學習如何使用matplotlib和seaborn進行數據可視化
- 在真實世界的業務場景中實施和保存機器學習模型

1. 數據科學基礎知識
2. 軟件安裝和設置
3. 列表和字典
4. 函數和包
5. NumPy基礎知識
6. Pandas和數據框
7. 與數據庫交互
8. 在數據科學中思考統計學
9. 如何在Python中導入數據?
10. 導入數據的清理
11. 數據可視化
12. 數據預處理
13. 監督式機器學習
14. 非監督式機器學習
15. 處理時間序列數據
16. 時間序列方法
17. 案例研究-1
18. 案例研究-2
19. 案例研究-3
20. 案例研究-4