If you are looking to become a data scientist or just need to brush up on some techniques, here is a list of 22 must-know terms for aspiring data scientists.
(This may or may not expand as time goes by.)
Data Science Glossary
Machine Learning is an area of study that enables computers to learn without requiring manual programming. With the use of Algorithms, Machine Learning is able to process data and get trained for predicting future outcomes autonomously.
Data Exploration is the process of discovering patterns and trends in a data set. It is an important step in the Data Analysis process, as it provides valuable insights into the structure and content of the data.
Data Visualization is the process of representing large amounts of structured or unstructured data in graphical or pictorial form to provide a better understanding of the data. Visualization tools such as graphs, charts, and maps can help to make complex data easier to understand.
Data Wrangling is the process of cleaning and transforming raw data into a usable format for downstream analysis. It involves selecting relevant features, dealing with missing values, and reformatting the data into a more suitable form.
Data Mining is the process of discovering meaningful patterns in large datasets by applying algorithms to identify hidden correlations and trends. Data Mining can be used for predicting customer behavior, detecting fraud, and identifying new business opportunities.
Predictive modeling is the process of creating a model which is used to make predictions about future events or outcomes. Predictive models analyze past data and apply it to future data, using various techniques such as linear regression, machine learning algorithms, and artificial neural networks. The goal of predictive modeling is to gain insights into relationships between variables and use them to forecast future outcomes.
Regression is a statistical technique used to determine relationships between variables. It can be used to predict future outcomes based on past data and draw conclusions from observed trends. Regression is generally utilized for forecasting, prediction, and causality analysis by establishing a mathematical equation that explains the relationship between two or more variables.
Classification is an approach to machine learning where data is grouped or classified into categories or classes. It enables computers to identify patterns, group similar inputs together, and predict outcomes based on this data.
Clustering is the process of grouping a set of data points together into clusters based on their similarity or distance from each other. It is an unsupervised learning technique that uses algorithms to identify patterns within data and group similar objects together. Clustering can help to classify data or identify outliers in datasets.
Deep learning is a subfield of machine learning that uses multiple layers of artificial neural networks to process and analyze data more accurately than traditional machine learning algorithms. It is used to solve complex problems such as natural language processing, computer vision and speech recognition. Deep learning can also be used for predictive analytics.
Neural networks are a type of artificial intelligence (AI) inspired by biological neural systems. They use multiple layers of neurons which are interconnected and each layer is responsible for a different task within the network. Neural networks can be used to classify data, generate predictions, and detect patterns in data that would otherwise be impossible with traditional methods.
Convolutional Neural Networks (CNNs)
Convolutional neural networks (CNNs) are a form of sophisticated deep learning systems that have proven tremendously successful in accurately recognizing images. CNNs use convolutions, which are mathematical operations applied to the input data, allowing the network to detect patterns and features within the data. CNNs are designed to be able to process complex data such as images and video.
Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) are a type of deep learning algorithm used to identify patterns in temporal data – such as audio or text-based data. Unlike traditional machine learning algorithms which make predictions based on static data, RNNs process data sequentially and use the output of previous sequences to make new predictions. As such, they are better suited for tasks that require understanding sequential data.
Natural Language Processing (NLP)
Natural language processing (NLP) is a branch of artificial intelligence that focuses on teaching machines how to understand and interact with human language. NLP algorithms are used to extract meaning from data sources such as text, audio, images, or videos. NLP is used in many areas such as machine translation, dialogue systems, automated summarization, automatic speech recognition, question answering systems, and more.
Feature engineering is when you transform raw data into features that better represent the underlying problem to a predictive algorithm. You select, create, and transform variables in order to make them more suitable for machine learning algorithms. Feature engineering involves combining existing features, transforming existing features, deriving new features from existing ones, and deleting irrelevant features. This helps improve model accuracy and prevent overfitting.
Hyperparameter tuning is the process of finding the best set of hyperparameters for a machine learning model. Hyperparameters are values that control the model’s behavior and performance, such as learning rate and regularization. Tuning these hyperparameters can help improve the accuracy of a model by allowing it to better fit the training data. Hyperparameter tuning is an iterative process that involves testing different combinations of parameters to find the optimal configuration for a given problem.
Overfitting occurs when a machine learning model is too complex for the data it is trained on. The model learns to recognize patterns from the training data, which do not generalize to real-world data. As a result, the model performs noticably worse when presented with new data that it has not seen before. Overfitting often leads to decreased accuracy and can prevent a model from achieving its optimal performance.
The bias-variance tradeoff is a concept in machine learning which states that increasing model complexity can lead to both increased bias and increased variance. Bias refers to the inaccuracy of a model’s predictions due to its over-simplification, while variance refers to the variability of a model’s predictions due to its sensitivity to small changes in data. When choosing an appropriate model, it is important to find a balance between these two properties in order to minimize prediction error.
Cross-validation is a technique used to evaluate the performance of a machine-learning model. The data is split into multiple subsets, one subset is training the model and the other is testing it. This process repeats until all subsets have been used for both training and testing. The final performance of the model is evaluated by averaging the results from all iterations of cross-validation. Cross-validation helps to reduce overfitting and ensures that the model is generalizable to unseen data.
A/B testing is a method of experimentation that involves comparing two versions of the same product or website to determine which one performs better. In the context of data science, A/B testing can involve splitting a dataset into two groups and testing various variables to discover their effect on the outcome, such as whether a change in design will lead to an increase in sales. A/B testing helps optimize a product based on empirical evidence.
Find out about the best A/B testing tools here.
Model selection is the process of choosing the most appropriate machine learning model for a given data science problem. It involves exploring various algorithms, evaluating model performance based on different metrics, and selecting the best-performing model. Model selection also includes assessing potential tradeoffs between the accuracy, complexity, and generalizability of a model.
Deployment is the process of taking a data science or machine learning model from development and making it available in production. It typically involves packaging the model into an executable format, like a Docker container or an Android application, and deploying it on a server or cloud platform. Deployment also requires testing and verifying that the model is working correctly in production.
These terms provide a broad overview of the field of data science and machine learning, and aspiring data scientists should be familiar with each of these concepts to build a solid foundation for their careers.
Did I miss any terms? Any other terms that you think belong here? Shoot me a comment below!
About the Author
Dani Lehmer is the Commander in Chief of Dani Digs In. She is a Quality Assurance Administrator by day, an aspiring blog star by night.