Decoding the Hidden Language of Data Science: A Beginner’s Guide to Industry Terminology
Here’s a challenge,
Think back and tell me the number of people who told you data science would be easy. I bet the count is zero because you’ve checked EdX, 365 Data Science, Udacity, Udemy and Datacamp and you’re excited, exhausted and overwhelmed by all the new terms you’re seeing.
Why is Industry Terminology Important?
As with any industry, data science has its own set of jargon and technical terms that are used to describe specific concepts, methodologies, and tools.
Learning and mastering these terms is essential if you want to communicate effectively with your peers, understand technical documentation and research papers, and stay up-to-date with the latest trends and developments in the field.
So I decided to use Storytelling, a Data Science technique to ensure the terms are digestible. So today we’ve made a character called Chanel, a data science student at the University of the West Indies who will be guiding you through the terminology matrix.
“Hey, guys! I’m Chanel, a University of the West Indies student pursuing a degree in data science. I know how challenging it can be to keep up with the ever-evolving industry-specific jargon. That’s why I’m here to share my journey of learning the hidden language of data science with you. So, let’s dive in!
Fundamental terms:
Data mining: The process of discovering patterns and extracting useful information from large data sets.
Predictive modelling: The process of using data to make predictions about future events or behaviour.
Regression analysis: A statistical method used to identify relationships between variables and make predictions based on those relationships.
Time series analysis: A statistical method used to analyze time-dependent data and make predictions based on historical patterns.
Cluster analysis: A method of grouping data points together based on their similarities.
Principal component analysis (PCA): A method of reducing the complexity of large data sets by identifying the most important variables.
Machine Learning:
Artificial intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans.
Deep learning: A subset of machine learning that uses neural networks with multiple layers to analyze and extract complex patterns from data.
Convolutional neural networks (CNNs): A type of neural network commonly used in image and video processing.
Recurrent neural networks (RNNs): A type of neural network commonly used in natural language processing and speech recognition.
Support vector machines (SVMs): A type of machine learning algorithm used for classification and regression analysis.
Random forests: A machine learning technique that combines multiple decision trees to improve the accuracy of predictions.
Natural Language Processing (NLP):
Sentiment analysis: The process of identifying and categorizing opinions expressed in text as positive, negative, or neutral.
Named entity recognition (NER): The process of identifying and categorizing named entities, such as people, organizations, and locations, in text.
Part-of-speech (POS) tagging: The process of labelling words in text with their corresponding parts of speech, such as nouns, verbs, or adjectives.
Word embeddings: A technique for representing words as vectors in a high-dimensional space, commonly used in natural language processing.
Topic modelling: A method of identifying the underlying themes or topics in a large collection of text documents.
Text classification: The process of categorizing text into predefined categories, such as spam or non-spam emails.
Data Visualization:
Data storytelling: The use of data and visualization techniques to tell a compelling story or convey a message.
Infographics: A visual representation of data or information, often used to present complex information clearly and concisely.
Heatmaps: A visualization technique used to represent the distribution of values across a two-dimensional space.
Scatterplots: A type of chart used to display the relationship between two variables.
Boxplots: A type of chart used to display the distribution of a dataset and identify outliers.
Line graphs: A type of chart used to display trends in data over time.
So there you have it, the most important data science terminology you should know as a beginner. By learning and understanding these key concepts and technical terms used in the field, you can communicate more effectively with your peers, understand technical documentation and research papers etc.”
Thank Chanel!
Let me know how you guys feel about the storytelling method I’ve developed.
Follow for more and tell a friend.
See you next week! Same time! Same place!