Data Science

Data Science is an interdisciplinary field that uses scientific methods to extract insights from data. It combines techniques from mathematics, statistics, and computer science. Data scientists, who are part mathematician, part computer scientist, and part trend-spotter, are tasked with analyzing and visualizing data to help companies make strategic decisions and identify new opportunities. The field of Data Science has a wide range of applications and offers numerous opportunities, making it a promising and in-demand profession.

We are going to cover the fundamentals underpinnings of Python that will be used in this course. This is just part of the introduction to Python but in order to understand every concept it will help to check out introductions to Python from online sites.

This is more of a reminder that as a data scientist, you need to know databases and how to effectively retrieve relevant information. For further analysis.

Map-reduce takes advantage of distributed processing, with one task being mapped and then reduced to the required/expected outcome.

Recap on linear algebra

An Overview of Statistics

A Basic Introduction to Probability

The science part of data science frequently involves forming and testing hypotheses about our data and the processes that generate it.

In many instances during data science, our objective is to discover the optimal model for a given scenario. Typically, the term "optimal" refers to achieving objectives such as minimizing prediction errors or maximizing the likelihood of the data. Essentially, it involves finding a solution to an optimization problem.

While machine learning is an important part of data science, the focus of most data scientists' work lies elsewhere. Solving business problems and understanding data are the primary tasks in data science. Data scientists spend much of their time collecting data from various sources, analyzing it to understand its meaning and structure, and preparing it for modeling by cleaning errors and formatting it consistently. Only after this significant data work is machine learning briefly brought into the process as a tool to gain insights from the prepared data. Though not the main effort, familiarity with machine learning techniques is essential for data scientists since it allows them to leverage their data work into actionable models and solutions

Linear regression is a supervised machine learning algorithm that calculates the linear relationship between a dependent variable and one or more independent variables. It’s one of the simplest and most commonly used machine learning algorithms.

Multilinear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc. It’s widely used because it can handle situations where you need to predict an outcome based on multiple independent variables.

Logistic Regression is a popular supervised learning algorithm. It’s used for predicting the output of a categorical dependent variable. The outcome must be a categorical or discrete value, such as Yes or No, 0 or 1, true or false, etc.

Decision Trees are a type of supervised learning algorithm that is mostly used for classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.

Random Forests are a robust machine learning algorithm that can be used for a variety of tasks including regression and classification. It is an ensemble method, meaning that a random forest model is made up of a large number of small decision trees, called estimators, which each produce their own predictions.

Support Vector Machines (SVMs) are a set of supervised learning methods used for classification, regression, and outliers detection. They are effective in high dimensional spaces and are versatile as different Kernel functions can be specified for the decision function.

K-Nearest Neighbors (KNN) is one of the simplest yet most fundamental algorithms in Machine Learning. It’s a supervised learning technique, which means it learns from labeled training data.

K-Means is an unsupervised learning method used for clustering data points. The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster.

Neural networks are artificial systems that were inspired by biological neural networks. These systems learn to perform tasks by being exposed to various datasets and examples without any task-specific rules. The idea is that the system generates identifying characteristics from the data they have been passed without being programmed with a pre-programmed understanding of these datasets.

A Perceptron is an algorithm used for supervised learning of binary classifiers. It’s the simplest possible Neural Network and is the building block of Machine Learning. The name ‘Perceptron’ is derived from the word ‘perception’, meaning to grasp or understand.

A Feed-Forward Neural Network is an artificial neural network in which the connections between nodes do not form a cycle. This is the simplest form of neural network as information is only processed in one direction.

Backpropagation, short for “backward propagation of errors,” is a standard method of training artificial neural networks. It’s used to calculate the gradient of a loss function with respect to all the weights in the network.

Deep learning is a part of machine learning that uses deep neural networks to solve complex problems. It’s been very successful and will continue to grow as we get more data and better computers.

Tensors are really important in deep learning. They let you store and manipulate data across multiple dimensions, which is great for handling complex data like images, sequences, and higher-dimensional data.

Deep learning technology uses these multiple layers to represent the abstractions of data and build computational models. These layers extract features from data and transform the data into different levels of abstraction (representations).

In deep learning, an activation function is a critical part of the design of a neural network. It defines how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network. Sometimes the activation function is called a “transfer function” and if the output range of the activation function is limited, then it may be called a “squashing function”.

We use the softmax function to squash raw scores from neurons into probabilities, and then we use cross-entropy loss to compare these probabilities with the true labels. This combination allows us to effectively train our neural network.

Dropout is a technique used in deep learning models. It helps prevent overfitting, which is a common problem in deep learning where the model performs well on the training data but poorly on unseen data.

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks.

Saving model is a crucial aspect of deep learning as it allows you to save your trained models and reuse them later, which can save a lot of time and computational resources.

I am excited to introduce you to the fascinating world of Image Classification using Convolutional Neural Networks (CNNs) in TensorFlow, with a focus on the CIFAR-10 dataset.

Dimensionality Reduction is a technique that is used to reduce the number of features in a dataset while retaining as much of the important information as possible. It is a process of transforming high-dimensional data into a lower-dimensional space that still preserves the essence of the original data.

Data Science Ethics is a crucial aspect of data science that deals with the moral obligations and responsibilities when conducting data science. It’s about what is right and wrong when conducting data science. It encompasses the moral obligations of gathering, protecting, and using personally identifiable information and how it affects individuals.

This is the end, the course gives you ideas of how you can go to work on projects involving trend analysis. This doesn't cover everything as this field is dynamic . Here are my recommendations.