Essential Data Science Terms Every Analyst Should Know

Importance of Understanding Data Science Terms

Mastering data science requires an accurate interpretation of the technical terminology. It requires a clear understanding to understand the concepts and definitions correctly. It also contributes to deciding the workflow and remaining informed about the latest updates. 

Clarity also plays a critical role in communications with technical and non-technical audiences. It includes explaining the data science terms in simpler form when dealing with a non-technical audience. 

Fundamentals of Data Science

A basic understanding of any subject is essential to master it. The fundamental concepts of the subject are the right choice to begin with. Thus, some of the important fundamental concepts of data science are: 

• Machine Learning: It programs the system to carry out specific tasks. 
• Algorithms: They are a set of processes or rulers used to perform a task. 
• Statistical Models: These are mathematical concepts used to identify relations between random and non-random variables. 
• Programming: It is used to develop models.

Key Data Science Terms

Let us explore the key data science terminology that are crucial for understanding the subject.

‘A’

Accuracy Score: It is defined as the ratio between correct prediction and the overall prediction. This evaluation metric aids in estimating the performance of machine learning models.

Activation Function: It is used in artificial neural networks (ANN) to tell whether to activate neurons. This is decided on the calculation of its output to the outer layer with respect to input from the previous layer. The non-linear transformation of the neural network is due to the activation function.

Algorithm: It refers to the set of instructions for executing a particular task. It is important when working with machine learning or big data. Algorithms aid in analyzing and organizing data for making predictions and building predictive models.

API: Application Programming Interface (API) refers to the rules that enable connection between different software applications.

Artificial Intelligence (AI): It helps machines solve problems using data and computer science. Here, intelligence is a computer-based program mimicking human intelligence.

Autoregression: A time series model that uses previous input time steps to a regression equation to predict the next time step value. The model determines that the output variable linearly relies on its own previous input variable.

‘B’

Backpropagation (BP): It is an algorithm that is also called backward propagation of errors. It is designed to evaluate the errors from output to input nodes. This algorithm aids in minimizing predictive errors.

Business Intelligence (BI): It refers to data analytics that allows businesses to make informed decisions based on valuable insights from data.

Bayes’ Theorem: The theorem is applied to evaluate conditional probability. It means Bayer’s rule is used to determine the probability of an event related to another event or prior knowledge of conditions.

Big Data: It refers to the faster collection of high-volume data from a wide range of sources.

‘C’

Clustering: It is defined as an unsupervised learning problem focusing on grouping observations with respect to similarity and common points.

Changelog: It is defined as the documentation involving all steps considered and recorded that have been performed throughout the working with data.

Correlation: It refers to the strength and direction of the relationship between two or more variables. The Pearson coefficient or Correlation coefficient measures correlation. 

Covariance: The evaluation of allied variability of any two random variables is called covariance.

‘D’

Dashboard: The live data can be tracked and displayed using dashboards. Here, the databases and feature visualizations are linked with the dashboard, which provides automatic updates reflecting recent data in the database.

Data Analytics: Data analytics encircles data analysis (data-driven information process), data science (theorizing and forecasting through available data), and data engineering (generating systems of data). Data analytics thus refers to the collection, conversion, and organization of data to deliver conclusions and make predictions and data-driven informed decisions.

Database: Database (DB) refers to the collection of structured data. Here, the data are organized to allow the computer to access information easily. The database can be built and controlled using a SQL-based program.

Database Management System (DBMS): It refers to the software system for storing, accessing, and running queries on data. It works as a user database interface, enabling them to generate, read, update, and remove information or data from the dataset.

Data Mining: Examining data to find patterns and valuable insights is called data mining. It is known as the fundamental aspect of data analytics to inform business recommendations.

Dataset: The collection of data into some type of data structure is called a dataset. The dataset can be made of any data. For instance, the business datasets may have data related to the client’s name, salary, sales profit, etc.

Data Visualization: It refers to representing information through charts, graphs, maps, graphs, or other visual tools. This helps foster storytelling through which anyone can easily explain complex data in a simpler way.

Data Warehouse: It is defined as the central repository for storing processed and organized data from variable sources. Thus, a data warehouse collects combined data, i.e., current and historical data. Internal and external databases extract, modify and upload these data.

Decision Tree: A supervised learning algorithm for classification problems. It uses tree-like decision models along with their consequences, outcomes, resources, cost, and profit. This approach aids in portraying an algorithm that holds conditional control statements.

Deep Learning (DL): Deep learning is an artificial method to train computers for data processing like human intelligence. In data science, it uses large neural networks (also called deep nets) to solve complex complications like fraud detection and face recognition. 

‘E’

Exploratory Data Analysis (EDA): It is defined as a phase applicable in the data science pipeline. EDA aids in understanding data through visualization and statistical analysis.

Evaluation Metrics: It is mainly used to evaluate the quality of machine learning and statistical models.

‘F’

False Negative: When the information or values are true but have been predicted incorrectly as false, it is called false negative.

False Positive: When the values or information is false but has been predicted as true, it is called false positive.

F-Score: It combines precision and recall for evaluating the classification’s effectiveness.

‘G’

Go: It is a simple computer programming language used for building reliable and efficient software. This open-source programming language is used for garbage collection, memory safety, and structural typing.

Goodness of Fit: A model that determines how it fits the set of observations. It helps in understanding the difference between the expected values of a model and observed values.

‘H’

Hadoop: A distributed processing framework applicable to huge data. Hadoop is open-source and enables us to use parallel processing ability to manage enormous amounts of data.

Hive: To process structured data in Hadoop, a data warehouse software project is used called Hive. It helps in indexing, metadata storage, and operating compressed data.

Hypothesis: The possible outcome of any problem is called a hypothesis. It can either be true or not true.

‘I’

Imputation: It refers to the technique applied for managing the missing data values.

Iteration: It defines how many times the algorithm's parameter gets updated with model training on a dataset.

‘J’

Julia: It is a high-level, open-source computer programming language with high performance. The language is used for several purposes, such as numerical computing defining function behavior. It is designed for distributed computation and parallelism. 

‘K’

K-Means: It refers to unsupervised algorithms that aid in solving problems related to clustering.

Keras: It refers to a simple but high-level neural network library. The library is written in the programming language Python. Keras is responsible for making design and experiments easier with neural networks.

Kurtosis: The tail’s thickness of the distribution is known as Kurtosis. Kurtosis is categorized into three forms based on its value, i.e., mesokurtic (value equals 3), platykurtic (value lower than 3), and leptykurtic (value greater than 3).

‘L’

Labeled Data: If the recorded data has a tag, class, or label, the dataset is called labeled data. For instance, labeled datasets for videos may only contain only videos.

Line Chart: The visual display of a dataset representing information as a series of points linked with a line segment.

‘M’

Machine Learning (ML): ML is a subset of artificial intelligence that processes data by mimicking human intelligence. Machine learning enables algorithms to improve with time and become more accurate while making classifications or predictions. ML can design, build, and maintain AI and machine learning systems.

Mean: The arithmetic value occupied by dividing the sum of all the dataset values with the total number of values present in the dataset is called Mean.

Median: Any dataset's middle value(s), whether in descending or ascending order, is called Median. If there are two middle values, i.e., even numbers, we have to take the average of those values to get the median of the dataset.

Mode: A dataset's most occurring or frequent value(s) is called mode.

‘N’

Normalization: It is defined as the process where all data are recalled to make all the attributes at the same scale.

NoSQL: It is elaborated as ‘not only SQL’ and is a database management system. It is applied for storing and retrieving non-relational databases. 

Null Hypothesis: When the observed data opposes the alternative hypothesis and does not represent a link between two variables, it is called a null hypothesis. In this, the observation occurs only by chance.

‘O’

Open Source: It refers to the free licensed resources and software for extracting, modifying, and sharing data.

Ordinal Variable: The variables with different values but with similar order are called ordinal variables.

Outlier: The observation represented far away, which diverts from the entire sample pattern, is called an outlier.

Overfitting: When a model perfectly fits into a training dataset but cannot fit into a test set, then the model is called overfitting. This occurs when the model is sensitive and records patterns available, particularly in the training dataset.

‘P’

Pattern Recognition: It refers to the branch of ML that works mainly on recognizing regularities and patterns in the dataset.

Precision and Recall: The measurement of accurately predicted positives from the total positive cases is called precision. Recall determines the number of correct positive predictions.

Predictor Variable: These variables are used for predicting dependent variables.

Pretrained Model: Models that are developed by others to solve similar problems are called pre-trained models. Pre-trained models are preferred over building models from scratch for solving problems because they are already trained on other problems as initial points.

‘Q’

Quartile: The values that are discrete in each quarter such as Q1, Q2, Q3, Q4 are called quartiles.

Quantitative Analysis: Quantitative analysis is the process in which measurable and verifiable data is collected and evaluated to understand the business's behavior and performance.

‘R’

Regression: A machine learning problem that predicts future outcomes using data. It relates the dependent variable with multiple independent variables to observe the changes.

Reinforcement Learning (RL): A branch of machine learning that enables algorithms to learn from the environment. Based on the learning from past experiences, RL makes decisions close to the desired goal.

Relational Database: A database that has multiple tables where information is interlinked. The user can access related data throughout multiple tables in a single query if the required data is stored in separate tables.

‘S’

Sampling Error: The statistical difference between the entire dataset and its subset is called sampling error since all the elements of a sample do not hold all the elements of the entire dataset.

Standard Deviation: The frequency of data dispersion is called standard deviation. Standard deviation is the square root of the variance of the primary data.

Standard Error: When a sample mean deviates from the standard mean of the given set, the deviation is called standard error. This helps in measuring the accuracy of the sample.

Synthetic Data: Artificially generated data is called synthetic data and reflects the statistical properties of the primary dataset. They are widely used in sectors like healthcare and banking.

‘T’

Tokenization: It is the process of dividing text string into units (tokens). Here, the tokens can be words or their groups. Tokenization is a very important step in NLP.

Training Set: It refers to the set extracted before building a model. It covers around 70% to 80% of the whole dataset, which will be used for fitting models that are further tested on the test set.

Test Set: It refers to the subset of available data extracted to build a model. It covers 20% to 30% of the data used for analyzing the model accuracy fitted on a training set.

Transfer Learning: Applying a pre-trained model to a new dataset is called transfer learning. Pre-trained models are created for solving a problem. The model aids in solving similar problems with similar data.

‘U’

Underfitting: When any model cannot identify a pattern from the training set due to its building with limited information, it is called underfitting. The model cannot perform tasks on unseen data or even on the training set.

Unstructured Data: Data that does not belong to a predefined data structure, such as row-column structure, are called unstructured data. For instance, videos, emails, and images.

‘V’

Variance: The average square difference between each value of the data and the mean of the data is called variance. It represents how values are spread. In ML, variance is the error that occurs due to the model’s sensitivity or complications in the training set.

‘W’

Web Scraping: A process of extracting particular data from a website to use them further. This can be done conveniently via programming languages like Python.

‘Z’

Z-Score: Z-score, normal score, standard score, or standardized score refers to the number of standard deviation units by which variation from the mean of the dataset occurs.

Advanced-Data Science Concepts

Advanced data science concepts involve a deeper understanding of concepts. It involves machine learning, artificial intelligence, sophisticated techniques, and processes to handle, extract, analyze, categorize, and manage data in a computer system. Therefore, advanced data science covers topics like algorithms, models, reinforced and deep learning natural language processing (NLP), and others to identify errors in the data and make informed decisions to improve business. 

How Do You Build a Career in Data Science?

To build a career in data science, you need to have a strong background in the field. You must have a degree in data science, computer science, mathematics, engineering, or related fields. However, education is not enough to land a higher-paying job. You must have the skills to bring you into the spotlight. 

Are you looking for quality certification courses? Simplilearn offers data science courses to help you learn the terms that are common yet important to the field and knowledge of data science's fundamental and advanced workings. If you’re advancing your career as a data scientist, this Post Graduate Program In Data Science will help you gain hands-on experience with the necessary languages, software, and tools. Enroll now to kickstart a bright future!

FAQs

Q1. What is the difference between AI and Data Science? 

Artificial intelligence mimics human intelligence and leads to the development of intelligent systems able to perform tasks independently. Data science uses the potential of data through handling and detailed analysis. It deals with structured and unstructured data. 

Q2. How can I transition into a data science career? 

If you are from a technical background, consider identifying the skill and knowledge gap and work to fulfill it before gaining hands-on experience. The non-technical candidates must begin from scratch by learning the basic concepts and programming language. 

Q3. What are some key concepts of data science?

The key concepts of data science are probability, algebra, calculus, statistics, regression, classification, etc. 

Q4. What are the prerequisites for learning data science? 

Familiarity or basic knowledge of previously mentioned key concepts is the prerequisite as it helps in better understanding, in-depth clarity, and increased interest. 

Q5. How is data privacy maintained in data science projects?

Establishing transparency and informed consent contributes to data privacy. Encryption, anonymization, security audits, and access control also play a role in maintaining data privacy.