Data science/ML/AI

Importance of Theory in Data Science While there are many resources covering the theoretical foundations of data science concepts, few demonstrate why having these foundations is practically important. This article gives four examples illustrating why it’s crucial for a data scientist to know what they’re doing Link: https://towardsdatascience.com/the-importance-of-theory-in-data-science-3487b4e93953

How To Use Tableau and Python TabPy (the Tableau Python Server) is an Analytics Extension implementation that expands Tableau’s capabilities by allowing users to execute Python scripts and saved functions via Tableau’s table calculations. You can learn more about it in this article Link: https://medium.datadriveninvestor.com/introducing-tabpy-tableau-python-e812bf3f2632

Silhouette coefficient: A score from -1 to 1 describing the clusters found during modeling. A score near zero indicates overlapping clusters, and scores less than zero indicate data points assigned to incorrect clusters. A Stop words: A list of words removed by natural language processing tools when building your dataset. There is no single universal list of stop words used by all-natural language processing tools. In supervised learning, every training sample from the dataset has a corresponding label or output value associated with it. As a result, the algorithm learns to predict labels or output values. Test dataset: The data withheld from the model during training, which is used to test how well your model will generalize to new data. Training dataset: The data on which the model will be trained. Most of your data will be here. Transformer: A more modern replacement for RNN/LSTMs, the transformer architecture enables training over larger datasets involving sequences of data. In unlabeled data, you don't need to provide the model with any kind of label or solution while the model is being trained. In unsupervised learning, there are no labels for the training data. A machine learning algorithm tries to learn the underlying patterns or distributions that govern the data.

Interesting Terminologies to Understand in Machine Learning Bag of words: A technique used to extract features from the text. It counts how many times a word appears in a document (corpus), and then transforms that information into a dataset. A categorical label has a discrete set of possible values, such as "is a cat" and "is not a cat." Clustering. Unsupervised learning task that helps to determine if there are any naturally occurring groupings in the data. CNN: Convolutional Neural Networks (CNN) represent nested filters over grid-organized data. They are by far the most commonly used type of model when processing images. A continuous (regression) label does not have a discrete set of possible values, which means possibly an unlimited number of possibilities. Data vectorization: A process that converts non-numeric data into a numerical format so that it can be used by a machine learning model. Discrete: A term taken from statistics referring to an outcome taking on only a finite number of values (such as days of the week). FFNN: The most straightforward way of structuring a neural network, the Feed Forward Neural Network (FFNN) structures neurons in a series of layers, with each neuron in a layer containing weights to all neurons in the previous layer. Hyperparameters are settings on the model which are not changed during training but can affect how quickly or how reliably the model trains, such as the number of clusters the model should identify. Log loss is used to calculate how uncertain your model is about the predictions it is generating. Hyperplane: A mathematical term for a surface that contains more than two planes. Impute is a common term referring to different statistical tools which can be used to calculate missing values from your dataset. Label refers to data that already contains the solution. Loss function is used to codify the model’s distance from this goal Machine learning, or ML, is a modern software development technique that enables computers to solve problems by using examples of real-world data. Model accuracy is the fraction of predictions a model gets right. Discrete: A term taken from statistics referring to an outcome taking on only a finite number of values (such as days of the week). Continuous: Floating-point values with an infinite range of possible values. The opposite of categorical or discrete values, which take on a limited number of possible values. Model inference is when the trained model is used to generate predictions. Model is an extremely generic program, made specific by the data used to train it. Model parameters are settings or configurations the training algorithm can update to change how the model behaves. Model training algorithms work through an interactive process where the current model iteration is analyzed to determine what changes can be made to get closer to the goal. Those changes are made and the iteration continues until the model is evaluated to meet the goals. Neural networks: a collection of very simple models connected together. These simple models are called neurons. The connections between these models are trainable model parameters called weights. Outliers are data points that are significantly different from others in the same sample. Plane: A mathematical term for a flat surface (like a piece of paper) on which two points can be joined by a straight line. Regression: A common task in supervised machine learning. In reinforcement learning, the algorithm figures out which actions to take in a situation to maximize a reward (in the form of a number) on the way to reaching a specific goal. RNN/LSTM: Recurrent Neural Networks (RNN) and the related Long Short-Term Memory (LSTM) model types are structured to effectively represent for loops in traditional computing, collecting state while iterating over some object. They can be used for processing sequences of data.

Different Data Sources and How They Are Collected 1) Company Data Sources: Web Events, Survey Data, Customer Data, Logistics Data and Financial Transactions. 2) Open Data Sources: Public Data APIs, Public Records APIs request data over the internet. Interesting API's include: Twitter, Wikipedia, Yahoo Finance, Google Maps etc Public records data can be collected by international organisations like World Bank, UN, WTO 3) National Statistical Offices: Censuses Surveys 4) Government Agencies: Weather Data Environment Data Population Data

A Guide to Understanding Different Types of Data Hey There😃!! Do you know the different formats your data can be in and how to identify them?😌 Here's a guide that can help you😉 Structured Data : It is in a standardized format, has a well-defined structure, complies to a data model, follows a persistent order, and is easily accessed by humans and programs. This data type is generally stored in a database. Normally in a table or number of tables. Examples: Data from surveys, different sensors, point-of-sale details, and financial information Unstructured Data: It does not conform to any other model and has no easily identifiable structure. There is no organization to it and it cannot be stored in any logical way. Unstructured data does not fit into any database structure, has no rules or format, and it cannot be easily used by programs. Examples: raw videos from surveillance cameras, reports, file shared with corporate documents, images, and memos. Semi Structured Data: It is not in a relational database, does not conform to a data model, but has some elements of structure. It cannot be stored in rows and columns or databases. This data contains metadata and tags which helps it to be grouped appropriately and describes the way it is stored. Semi-structured data is organized hierarchically, although the entities within that group may not have the same properties or attributes. It is difficult to automate and manage and is hard for programs to access. Examples: wikipedia pages with links, collection of scientific papers in JSON format with authors, emails, zipped files, web files, and binary executables.

A Guide to Understanding Different Types of Data Hey There😃!! Do you know the different formats your data can be in and how to identify them?😌 Here's a guide that can help you😉 **Structured Data :**It is in a standardized format, has a well-defined structure, complies to a data model, follows a persistent order, and is easily accessed by humans and programs. This data type is generally stored in a database. Normally in a table or number of tables. Examples: Data from surveys, different sensors, , web logs, point-of-sale details, and financial information Unstructured Data: **It does not conform to any other model and has no easily identifiable structure. There is no organization to it and it cannot be stored in any logical way. Unstructured data does not fit into any database structure, has no rules or format, and it cannot be easily used by programs. **Semi Structured Data: It is not in a relational database, does not conform to a data model, but has some elements of structure. It cannot be stored in rows and columns or databases. This data contains metadata and tags which helps it to be grouped appropriately and describes the way it is stored. Semi-structured data is organized hierarchically, although the entities within that group may not have the same properties or attributes. It is difficult to automate and manage and is hard for programs to access.

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Just wanted to share this here as well in case somebody is interested.

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The Scikit-Learn Guide Looking to improve your knowledge on machine Learning ALgorithms, there's no better place to start from than to check the sklearn documentation There is alot of interesting information you can gain there https://scikit-learn.org/stable/

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