Data science is the discipline starting from data collection and ending with the processing of data. The end products of Data Science are generating predictions and improving performance by optimizing machine learning algorithms.
Machine learning is a tool to process data to generate predictions.
To understand in-depth, let’s learn what Machine learning and Data Science are, in detail:
The process of enabling computers to make autonomous decisions based on past data without external intervention is called Machine Learning.
Software, where machine learning is not involved, we program computers with standardized instructions to get outputs for inputs provided. In the case of Machine Learning, the computer analyses the intrinsic pattern within the data. And come out with the required result(s) without input explicitly being provided.
Types of Machine Learnings:
- Supervised Learning: The Supervised Learning model analyzes data organized in labels. Such data enable the system to formulate proper mapping of input-output pairs. With the abundance of such data, the Machine Learning algorithm can identify the intrinsic pattern within the data. And provide us with the required output.
- Unsupervised Learning: Unsupervised Learning models are advanced forms of the machine learning algorithm. It is currently under research. Under the Unsupervised Learning model, organized label data doesn’t get provided. So such a model first self-organizes the data, then identifies the pattern and gives the required output.
- Reinforcement learning: Reinforcement learning models generate their outcome by relying upon interaction with their environment. Their performance improves by analyzing their failures and success scenarios. These types of models get used in self-driving cars and autonomous robotics.
Data Science: Data science is a vast subject. It encompasses everything related to digital data. To understand Data Science, we should learn about steps involved in the Data Science process:
- Gathering Data: This is the starting point of the Data Science process. Every day our digital gadgets produce tons of data. All types of structured and unstructured data are needed to be gathered for processing.
- Data Preprocessing: Data processing organizes data making it analytical ready. It involves tasks like data cleaning. It also replaces missing values embedded in the data set.
- Data Analysis: This step identifies the pattern within the dataset. And by analyzing that pattern, the system comes out with related insights.
- Generating Predictions: By applying various predictor and classifier algorithms to the analyzed data, the system generates predictions forecasting future events.
- Optimizing Models: This is the final step of the Data Science process. Based on the previously mentioned steps, we can improve machine learning algorithms. Which, in its turn, enables the system to produce a more accurate outcome.
Conclusion: Both Data science and machine learning represent varied arrays of different processes. Steps of Data Science like analysis of data and predictions employ machine learning models. Generally, Supervised Learning models are used in data analysis and generating predictions. Unsupervised and Reinforcement learning models have limited use in Data Science. On the other hand, data collection and data processing hardly use Machine Learning as a tool.