As our world today relies on data for almost everything, data science becomes more and more sophisticated. Every business process, every social network, every store we buy from and almost every form of entertainment we enjoy. Did you know that the word “Data” is a plural of datum, which is a Latin noun meaning “something given“.
We have smart watches to monitor our health and fitness, we listen to music via Spotify. We order cars using Uber, we receive emails and notifications about news and products we are interested in. We are targeted with online ads for things we have searched for in the past, and we use GPS to get around a new city. Data features in our life almost every minute of the day. While we are using its benefits, data analysts, scientists and engineers are working on every aspect of that data to deliver it to us in a way that we can understand it and use it to make our lives better.
There are so many terminologies that describe the work being done today in this field. Data cleaning, data wrangling, data visualization … this list goes on and on. Each of these tasks plays a crucial role in delivering data to us. Data which is used for our business, for our hobbies, for our future. Here are some of the terms that we hear and use every day:
Data science is the discipline of using data and advanced statistics to make predictions by using scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms.
Data analysis uses less complex statistics and generally tries to identify patterns that can improve a business or organization.
Data engineers build the infrastructure through which data is gathered, cleaned, stored and prepped for use by data scientists.
Data visualisation allows us to display meaningful data visually, using infographics, charts, graphs and software.
Data tagging allows us to organize information more efficiently by associating pieces of information with tags or keywords.
Data mining is the process of pulling actionable insight out of a set of data and making it useful. This can include cleaning and organizing the data or analysing it to find meaningful patterns.
Data monitoring is the process of proactively reviewing and evaluating data and its quality to ensure that it is relevant and useful.
Data Wrangling or Data Munging
Data wrangling, also known as data munging is the process of taking data in its original form and transforming it to make it more appropriate for a larger data set. Replacing or removing values that might affect analysis or performance later.
Supervised Machine Learning
Supervised machine learning refers to when a data scientist provides a computer with a well-defined set of data, so the computer knows exactly what it is searching for.
Unsupervised Machine Learning
Unsupervised machine learning means the computer builds its own understanding of patterns within data often classifying items based on shared traits.
Artificial Intelligence is the general term used for when a machine mimics cognitive functions that we associate with the human mind, such as learning and problem-solving.
If you would like to learn more about any of the above, or how your data can be analysed or transformed to work better for you, get in touch with us!