15 Common Terms Every Data Scientist NEEDS To Know

Data science is a relatively new field, and as such, there is no agreed-upon definition of what a data scientist is. However, there are certain skills and knowledge that are essential for anyone working in the field of data science. In this article, we will explore 15 common terms every data scientist should know. These terms will help you better understand the field of data science, and they will also make you more marketable to employers.

• Data mining
  In computing, data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns.

• Data wrangling
  Data wrangling is the process of cleaning and preparing data for analysis. It includes tasks such as sorting, filtering, and segmenting data, as well as filling in missing values and dealing with outliers. Data wrangling is an essential step in any data analysis project, and it is often one of the most timeconsuming tasks. But with the right tools and approach, it can be a relatively straightforward process.

• Data visualization
  Data visualization is the process of representing data in a visual format. This can be done using a variety ofchart types, including bar charts, line charts, pie charts, and scatter plots. Data visualization is an important tool for data analysis, as it can help to identify patterns and trends in data.

• Data analysis
  The term data analysis is used to refer to a variety of techniques for investigating data in order to answer questions, make decisions, or support decision making. Data analysis can be divided into two broad categories: exploratory data analysis and confirmatory data analysis. Exploratory data analysis is used to generate hypotheses about the data, while confirmatory data analysis is used to test those hypotheses. Data analysis can be further divided into several more specific types of analyses, such as statistical analysis, regression analysis, predictive analytics, machine learning, text analytics, web analytics, and so on.

• Dataset: A collection of data that can be used for training a machine learning model.
  A dataset is a collection of data that can be used for training a machine learning model. The data may be in the form of text, images, or other types of data. A dataset is typically divided into training and test sets, where the training set is used to train the machine learning model and the test set is used to evaluate the performance of the model.

• Machine learning
  Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
  Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or impractical for humans to write explicit rules.

• Deep learning
  Deep learning is a branch of machine learning based on a set of algorithms that attempt to model highlevel abstractions in data. By using a deep neural network, deep learning algorithms can learn complex tasks by progressively building up layers of abstraction.

• Predictive analytics
  Predictive analytics is the practice of extracting information from data to make predictions about future events. Predictive analytics uses a variety of techniques, including machine learning, statistical modeling, and data mining.
  Predictive analytics is used in a variety of industries, including healthcare, retail, financial services, and manufacturing. In healthcare, predictive analytics can be used to predict patient outcomes, identify at risk patients, and improve population health. In retail, predictive analytics can be used to forecast demand, optimize pricing, and prevent fraud. In financial services, predictive analytics can be used to detect financial crimes, such as money laundering and fraud. In manufacturing, predictive analytics can be used to improve quality control and prevent equipment failures.

• Big data
  Big data is a field of data science that refers to the study and applications of extremely large data sets that are too large and complex to be processed using traditional data processing methods. Big data technologies enable organizations to collect, store, and analyze large amounts of data at unprecedented speed and scale. Big data has become a critical tool for business and government organizations seeking to gain insights into customer behavior, understand market trends, and make better decisions.

• Feature
  An individual characteristic of a data point that can be used to predict a target variable.
  In machine learning, a feature is an individual characteristic of a data point that can be used to predict a target variable. Features are typically numeric, but can also be categorical or boolean. In order to train a machine learning model, data scientists must select which features will be used as inputs. The selection of features can have a significant impact on the performance of the model.

• Target Variable
  The variable that a machine learning model is trying to predict.
  A target variable is the variable that a machine learning model is trying to predict. In supervised learning, the target variable is also known as the dependent variable. The target variable can be either a continuous or a categorical variable.

• Training Set
  A subset of the dataset used to train a machine learning model.
  A training set is a subset of the data used to train a machine learning model. The purpose of the training set is to provide the model with data so that it can learn to make predictions on new data. The training set is typically divided into two parts, the first part is used to train the model and the second part is used to validate the model.

• Test Set
  A subset of the dataset used to evaluate the performance of a machine learning model.
  A test set is a subset of the data used to evaluate the performance of a machine learning model. This subset is usually different from the training set, as it is used to assess how well the model performs on data that it has not seen before. The test set should be representative of the data that the model will encounter in the real world.

• Overfitting
  When a machine learning model performs well on the training set but poorly on the test set, due to having learned too much from the training data.
  Overfitting is a common problem in machine learning, where a model performs well on the training set but poorly on the test set. This is due to the model having learned too much from the training data, and not generalizing well to new data. Overfitting can be avoided by using regularization methods, such as early stopping or Dropout.

• Underfitting When a machine learning model performs poorly on both the training set and the test set, due to not having learned enough from the training data. Underfitting is a common problem in machine learning, where a model performs poorly on both the training set and the test set. This is due to the model not having learned enough from the training data.

And that's a wrap! Data scientists, you now know 10 more jargony terms to throw around and impress your friends. Just don't forget the actual meaning behind them – otherwise you'll be the one looking foolish.

A data scientist is a person who is better at statistics than any software engineer and better at software engineering than any statistician. — Mike Driscoll

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