Algorithm and Data Science
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. As unstructured data will account for more than 80% of data collected by organisations, there is a need for more complex and advanced analytical tools and algorithms for processing, analyzing, and drawing meaningful insights out of it. Data Scientists use various advanced machine learning algorithms to identify the occurrence of a specific event in the future.
Data scientist’s job is to know which of the algorithm types to apply to the business situation they are facing.
Algorithms for Data Science focus on the principles of data reduction and core algorithms for analyzing the data of data science
In Data Science there are three main algorithms used:
- Data Preparation, munging, and process algorithms
- Optimisation algorithms for parameter estimation which includes Stochastic Gradient Descent, Least Squares, Newton’s Method
- Machine Learning Algorithms: supervised learning, unsupervised learning
The top 9 data science algorithms are: Linear regression, logistic regression, K-Means clustering, PCA (Principle Component Analysis), SVM (Support Vector Machines), ANN (Artificial Neural Networks), Decision Trees, RNN (recurrent Neural Networks), Apriori.
Data science is a field of study where decisions are made based on the insights we get from the data instead of classic rule-based deterministic approaches.