1/ Book overview and review
Practical Data Science with Python (By Nathan George)
𧡠See thread below
#datascience #python
Practical Data Science with Python (By Nathan George)
𧡠See thread below
#datascience #python
2/ π€ Author
- Nathan George
- Data scientist at a fintech company
- Taught at Regis University, DataCamp and Manning LiveProject
- Mentor students at Udacity AI and Machine Learning NanoDegree
- Nathan George
- Data scientist at a fintech company
- Taught at Regis University, DataCamp and Manning LiveProject
- Mentor students at Udacity AI and Machine Learning NanoDegree
3/ π Book details
- About 600 pages
- 21 Chapters
- 6 Parts
- About 600 pages
- 21 Chapters
- 6 Parts
4/ π€ Who is this book for?
- Those wanting to break into data science
- Have beginner to intermediate level of Python coding skill
- Those wanting to break into data science
- Have beginner to intermediate level of Python coding skill
5/ π 6 Parts of the Book
1. An Introduction and the Basics
2. Dealing with Data
3. Statistics for Data Science
4. Machine Learning
5. Text Analysis and Reporting
6. Wrapping Up
1. An Introduction and the Basics
2. Dealing with Data
3. Statistics for Data Science
4. Machine Learning
5. Text Analysis and Reporting
6. Wrapping Up
6/ Part 1. An Introduction and the Basics
- Provide a brief history of the field of data science, skill sets, specializations and best practices for data science projects
- Provide tips on getting started with Python and its practical setup for data science projects
- Provide a brief history of the field of data science, skill sets, specializations and best practices for data science projects
- Provide tips on getting started with Python and its practical setup for data science projects
7/ Part 2. Dealing with Data
- How to handle various data sources (SQLite, Pandas, Numpy, MS Word, PDF, MS Excel, etc.)
- How to web scrape
- How to handle various data sources (SQLite, Pandas, Numpy, MS Word, PDF, MS Excel, etc.)
- How to web scrape
8/ Part 3. Statistics for Data Science
- Explains concisely and with practical examples on key statistical concepts (Probability, Distribution and Sampling)
- Covers hypothesis testing concepts and statistical tests using scipy.stats module
- Explains concisely and with practical examples on key statistical concepts (Probability, Distribution and Sampling)
- Covers hypothesis testing concepts and statistical tests using scipy.stats module
9/ Part 4. Machine Learning
- Preparing data prior to machine learning model building (feature engineering, feature selection, etc.)
- Model building (Classification, regression, performance evaluation, hyperparameter optimization, AutoML)
- Preparing data prior to machine learning model building (feature engineering, feature selection, etc.)
- Model building (Classification, regression, performance evaluation, hyperparameter optimization, AutoML)
10/ Part 5. Text Analysis and Reporting
- Clustering methods (k-means, hierarchical, etc.)
- Working with text (preprocessing, text analysis, sentiment analysis, etc.)
- Clustering methods (k-means, hierarchical, etc.)
- Working with text (preprocessing, text analysis, sentiment analysis, etc.)
11/ Part 6. Wrapping Up
- Making data dashboards with Streamlit
- Ethics and Privacy issues pertaining to data and machine learning
- How to stay up to date
- Making data dashboards with Streamlit
- Ethics and Privacy issues pertaining to data and machine learning
- How to stay up to date
12/ My impressions 1/2
- The author provided a thorough coverage on essential topics of using Python for practical implementation of data science projects
- Key libraries were covered: Python standard libraries, Scikit-learn, Pandas, Numpy, Scipy, Beautiful soup, etc.
- The author provided a thorough coverage on essential topics of using Python for practical implementation of data science projects
- Key libraries were covered: Python standard libraries, Scikit-learn, Pandas, Numpy, Scipy, Beautiful soup, etc.
13/ My impressions 2/2
- Data science concepts were explained alongside code examples. Thus, the book can easily serve as a handy desk reference for data practitioners
- Data science concepts were explained alongside code examples. Thus, the book can easily serve as a handy desk reference for data practitioners
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