Some useful PYTHON libraries for data science
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of denoscriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of denoscriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
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"🔍 Data Integrity Alert: Always double-check your data sources for accuracy and consistency. Inaccurate or inconsistent data can lead to faulty insights. #DataQualityMatters"
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"📊 Clear Objectives: Define clear objectives for your analysis. Knowing what you're looking for helps you focus on relevant data and prevents getting lost in the numbers. #AnalyticalClarity"
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📈 Context is Key: Interpret your findings in the context of your industry or domain. A seemingly significant trend might be trivial if it doesn't align with what's happening in your field. #ContextMatters"
Encyclopedia of Data Science & Machine Learning-J. Wang.pdf
261.8 MB
Encyclopedia of Data Science and Machine Learning
John Wang, 2023
John Wang, 2023
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"💡 Start Simple: Don't overcomplicate your analysis. Begin with simple approaches and gradually explore more complex techniques as needed. Simplicity often leads to clarity. #StartSimple"
"🔗 Data Relationships: Understand the relationships between variables. Correlation doesn't always imply causation. Dig deeper to uncover the underlying reasons behind observed patterns. #DataConnections"
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🔍 Missing Data Handling: Handle missing data wisely. Ignoring it or filling it with random values can distort results. Choose appropriate methods like imputation based on context. #MissingData"
"📈 Visual Storytelling: Use data visualization to tell a compelling story. Visuals make complex data accessible and engaging, enabling better communication of insights. #VisualStorytelling"
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"💬 Collaboration Matters: Collaborate with domain experts and stakeholders. Their insights can guide your analysis and help you uncover relevant trends and patterns. #CollaborativeInsights"
Generative AI is a multi-billion dollar opportunity!
There will be some winners and losers emerging directly or indirectly impacted by Gen AI 🚀 💹
But, how to leverage it for the business impact? What are the right steps?
✔️Clearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
👀 Uses public models with minimal customization at a lower cost.
🤖 Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
🚀Develops a unique foundation model for a specific business case, which requires substantial investment.
✔️Develop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
✔️Quickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
✔️Integrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
✔️Create a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
✔️Use existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
✔️Upgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
✔️Develop a data architecture that can process both structured and unstructured data.
✔️Establish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
✔️Upskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
✔️Assess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
✔️For many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
There will be some winners and losers emerging directly or indirectly impacted by Gen AI 🚀 💹
But, how to leverage it for the business impact? What are the right steps?
✔️Clearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
👀 Uses public models with minimal customization at a lower cost.
🤖 Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
🚀Develops a unique foundation model for a specific business case, which requires substantial investment.
✔️Develop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
✔️Quickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
✔️Integrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
✔️Create a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
✔️Use existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
✔️Upgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
✔️Develop a data architecture that can process both structured and unstructured data.
✔️Establish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
✔️Upskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
✔️Assess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
✔️For many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
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8 AI Tools Just for Fun:
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
Tattoosai
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