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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 descriptive 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.

Logistic regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1).

Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable.

The k-Nearest Neighbor (kNN) method makes predictions by locating similar cases to a given data instance (using a similarity function) and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression.

Classification and Regression Trees (CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression.

Support Vector Machines (SVM) are a method that uses points in a transformed problem space that best separate classes into two groups. Classification for multiple classes is supported by a one-vs-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error.

Additional Resources To Assist Research

https://www.reddit.com/r/MachineLearning/

• https://www.reddit.com/r/deeplearning/

• https://paperswithcode.com/

• https://papers.nips.cc/

• https://icml.cc/

• https://iclr.cc/

• https://www.researchgate.net/

Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Microsoft is now fueling its AI ambitions with nuclear power, signing a 20-year deal with Constellation Energy.

AI uses a lot of energy, but nuclear is a promising, sustainable power source that could support the expansion of AI capabilities.

Evaluating fairness in ChatGPT

We've analyzed how ChatGPT responds to users based on their name, using language model research assistants to protect privacy.

Creating our models takes more than data—we also carefully design the training process to reduce harmful outputs and improve usefulness. Research has shown that language models can still sometimes absorb and repeat social biases from training data, such as gender or racial stereotypes.

In this study, we explored how subtle cues about a user's identity—like their name—can influence ChatGPT's responses. This matters because people use chatbots like ChatGPT in a variety of ways, from helping them draft a resume to asking for entertainment tips, which differ from the scenarios typically studied in AI fairness research.

While previous research has focused on third-person fairness, where institutions use AI to make decisions about others, this study examines first-person fairness, or how biases affect users directly in ChatGPT.

🚨 ChatGPT has a Windows app now

OpenAI is testing a ChatGPT app for Windows— but it’s only available to paid users for now. You can download an early version of the app from then Microsoft Store. Just like the Mac version of the app. ChatGPT on Windows lets you ask the AI-powered chatbot questions in a dedicated window that you can keep open alongside your apps. You can quickly access the app by using the Alt + Space shortcut.

It also lets you upload files and photos to ChatGPT and comes with access to a preview of OpenAI’s o1 model capable of “reasoning.” The app is still missing some capabilities, however, such as advanced voice mode. Shortly after OpenAI launched its ChatGPT app on Mac in June, a developer spotted a security, vulnerability that stored conversations in plain text. OpenAI has since fixed this issue and now encrypts locally stored data.

INSTAGRAM is instant MONEY!

10 ChatGPT / Gemini Prompts to grow and monetize on instagram.

(Just copy paste these prompts)

1. "Engaging Content Calendar"
Create a content calendar for the next 3 months for my Instagram account, [account_name], focused on [niche]. It should include a mix of [content_type] (e.g., Reels, posts, stories) with a variety of formats, like [examples]. Each post should be aligned with my target audience ([target_audience]) and their pain points, including [pain_points].

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How to enter into Data Science

👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.

👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.

👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.