Would you agree that every field has its set of special words or expressions that are difficult for others to understand?
In the realm of business, phrases such as 'trim the fat', 'S.W.O.T.', 'pain point', and 'white paper' are commonly tossed around as industry jargon.
So, as you’d have guessed, Data Science just like every other field out there has its unique lexicon.
Hence, I've compiled a list of essential terms below to ensure we're all on the same page and moving towards a shared objective. Let's dive right in, shall we?
Learn Your ABC's:
Accuracy: The measure of how often a classification model correctly predicts outcomes among all instances it evaluates. For example, if a model correctly identifies 90 out of 100 instances, its accuracy is 90%.
A/B Testing: A statistical method used to compare two versions of a product, webpage, or model to determine which performs better. For instance, testing two different landing page designs to see which results in more sign-ups.
API (Application Programming Interface): A set of rules that allows one software application to interact with another. For example, a weather app using an API to fetch current weather data from a weather service.
BI (Business Intelligence): Technologies, processes, and tools that help organizations make informed business decisions. Tools like Tableau or Power BI help visualize and analyze business data.
Bias: An error in a model that causes it to consistently predict values away from the true values. For example, a model trained on biased data may favor certain outcomes.
Correlation: A statistical measure that describes the degree of association between two variables. For instance, a high correlation between hours studied and exam scores.
Covariance: A measure of how much two random variables change together. If two variables increase together, they have positive covariance.
D and beyond:
Data Cleaning: The process of identifying and correcting errors or inconsistencies in datasets. This step is crucial for ensuring data quality before analysis.
Data Mining: Extracting valuable patterns or information from large datasets. Techniques include clustering, classification, and association.
Data Visualization: Presenting data in graphical or visual formats to aid understanding. Tools like charts, graphs, and heatmaps are common.
Exploratory Data Analysis (EDA): Analyzing and visualizing data to understand its characteristics and relationships. This involves summarizing main characteristics, often with visual methods.
False Positive and False Negative: Incorrect predictions in binary classification. A false positive is when the model incorrectly predicts the positive class, while a false negative is when it incorrectly predicts the negative class.
Gaussian Distribution: A type of probability distribution often used in statistical modeling, also known as the normal distribution. It is characterized by its bell-shaped curve.
Hypothesis Testing: A statistical method to test a hypothesis about a population parameter based on sample data. It involves determining whether there is enough evidence to reject the null hypothesis.
Linear Regression: A statistical method for modeling the relationship between a dependent variable and one or more independent variables. For example, predicting house prices based on size and location.
Null Hypothesis: A statistical hypothesis that assumes there is no significant difference between observed and expected results. It is the default assumption to be tested against.
Predictive Analytics: Using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. For instance, predicting customer churn based on past behavior.
P-value: The probability of obtaining a result as extreme as, or more extreme than, the observed result during hypothesis testing. A low p-value indicates strong evidence against the null hypothesis.
Standard Deviation: A measure of the amount of variation or dispersion in a set of values. It indicates how much the values deviate from the mean.
Variance: The degree of spread or dispersion of a set of values, and also the variability of model predictions. High variance indicates a large spread around the mean.
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