- No comments
An Algorithm is essentially a method or plan for solving a problem, based on following a sequence of specified activities. And an algorithm is something that is used to train a model, all the decisions that a model is supposed to take are based on the input given by the algorithm to produce an expected outcome. Now going further, an analytical model is a statistical model, designed to perform a specific or to predict the probability of a specific event. Now the same is applied to businesses to determine the solution when in quandary. Algorithms are generally used throughout all areas of IT. A simple example would be a search based algorithm, it takes the keyword and searches its associated database for relevance and presents the results. So basically there are a variety of algorithms that can be used for different scenarios, it is usually variable dependent, that is based on a variety of variables, a data scientist can choose which algorithm to use.
It is not recommended that a plug and play of available algorithms are used by the data scientist, as the variables like, the size of data, industry type, its application, could produce results that might not be accurate. Hence a data scientist should train themselves on the most popular and important algorithms.
Awareness of supervised v/s unsupervised learning models
On the subject of algorithms, it is interesting to understand the difference between the two. A supervised learning model is essentially a model with a clear difference between explanatory and dependent variables, which means that the model’s outputs are known in advance.
Some examples of these models which are popular would be……
- Prediction – Linear Regression
- Classification – Decision Tree
- Time Series Forecasting
Simply an opposite of this would be the unsupervised learning model, where the model’s outputs are unknown, and there is no target characteristics. These models are built with the objective of finding out the fundamental structure of data, for example,
- Association Rules
- Cluster Analysis
The top 10 algorithms used by data scientist according to a poll are as follows……
- Decision Trees
- K-Nearest Neighbours
- Random Forests
- Time Series / Sequencing
- Text Mining
Some new options to the list of most commonly used algorithms which are recently gaining popularity are….
- Neural Networks – Deep Learning
- Singular Value Decomposition
You will notice, that most of the popular algorithms are supervised learning algorithms in nature. Unsupervised clustering algorithms can be used to detect a relationship between an organisations data set. Through these algorithms, you can find different types of groupings within a customer base. At times an unsupervised clustering can offer specific advantages when compared to supervised learning models. One obvious example is a way new applications can be observed by studying, how the connections are grouped when a new cluster is formed.
As can be seen, these are only the most popular algorithms, however, here are a host of other machine learning and data mining algorithms available, which can be used to create value to any analytical program. There are specific algorithms that are designed and developed to specifically deal with business challenges. These algorithms can almost assist in doing anything, from recognising faces too, dispensing drinks from a vending machine to reminding you about your meetings.
To learn more about the algorithms join our an online data science course, which you can do anywhere and anytime This program is co-created with Genpact as Knowledge Partner. This program helps you with a deep understanding of Data Analysis and Statistics, along with business perspectives and cutting-edge practices using SAS, R, Python, Hive, Spark, and Tableau.