Data Science vs Machine Learning
In this page, We will learn about Data Science vs Machine Learning | What is Data Science? | What is Machine Learning? | Where is Machine Learning used in Data Science? | Comparison Between Data Science and Machine Learning"
Machine learning is a branch of AI and a subfield of data science. Data science is the study of data purification, preparation, and analysis. The two most popular modern technologies are data science and machine learning, both of which are exploding in popularity. However, these two buzzwords, along with artificial intelligence and deep learning, are all fairly perplexing terms, so it's crucial to know how they vary. We will only learn about the differences between Data Science and Machine Learning in this topic, as well as how they relate to one another.
Machine Learning and Data Science are closely related, but they serve different purposes and have different goals. At a glance, Data Science is a field to study the approaches to find insights from the raw data. Whereas, Machine Learning is a technique used by the group of data scientists to enable the machines to learn automatically from the past data. To understand the difference in-depth, let's first have a brief introduction to these two technologies.
[ Note: while Data Science and Machine Learning are closely connected, they are not synonymous. ]
What is Data Science?
Data science, as its name suggests, is all about the data. Hence, we can define it as, "A field of deep study of data that includes extracting useful insights from the data, and processing that information using different tools, statistical models, and Machine learning algorithms." It is a concept that covers data cleansing, data preparation, data analysis, and data visualization and is used to handle large data.
A data scientist gathers raw data from a variety of sources, prepares and pre-processes it, and then uses machine learning techniques and predictive analysis to derive usable insights from the data.
Netflix, for example, mines its users' data and viewing patterns to determine their interests using data science approaches.
To become a Data Scientist, you'll need the following skills.
- Python, R, SAS, or Scala programming skills are required.
- SQL database coding experience is a plus.
- Algorithms for Machine Learning are well-versed.
- Deep understanding of statistical concepts.
- Skills in data mining, cleansing, and visualization.
- Skills in the usage of big data platforms like Hadoop.
What is Machine Learning?
Machine learning is a subfield of Data Science and a branch of artificial intelligence. It's a rapidly evolving technology that allows machines to learn from previous data and complete tasks on their own. It can be summed up as follows:
“Machine Leaning allows the computers to learn from the past experiences by its own, it uses statistical methods to improve the performance and predict the output without being explicitly programmed.”
Email spam filtering, product suggestions, and online fraud detection are just a few of the common applications of machine learning.
Machine Learning Engineer Skills Required:
- Understanding and implementation of Machine Learning Algorithms.
- Natural Language Processing is a term that refers to the process of
- Python or R programming skills are required.
- Statistics and probability principles are understood.
- Data modeling and data evaluation expertise
Where is Machine Learning used in Data Science?
The development process or life cycle of data science will help you understand how machine learning is used in data science. The following are the many steps in the data science lifecycle:
- Business Requirements: We strive to grasp the requirements for the business challenge we want to use it for in this step. Let's say we want to build a recommendation system, and the goal is to improve sales.
- Data Acquisition: In this step, data is collected in order to answer the problem. We can gather the user's ratings for different products, comments, purchase history, and other information for the recommendation system.
- Data Processing: In this stage, the raw data collected in the previous step is processed into a format that can be used by the subsequent steps.
- Data Exploration: This is the process in which we try to analyze the patterns in the data and extract relevant insights from it.
- Data modeling: It is a process in which machine learning methods are employed. As a result, this step encompasses the entire machine learning process. Importing data, cleaning data, developing a model, training the model, testing the model, and improving the model's efficiency are all part of the machine learning process.
- Deployment & Optimization: This is the final step, in which the model is put on a real-world project and its performance is evaluated.
Comparison Between Data Science and Machine Learning
The below table describes the basic differences between Data Science and ML:
|Data Science||Machine learning|
|It is concerned with deciphering and obtaining hidden patterns or helpful insights from data in order to make better business decisions.||It is an area of data science that allows a machine to autonomously learn from previous data and experiences|
|It is used to extract information from data.||For fresh data points, it's utilized to make predictions and classify the results.|
|It's a wide term that refers to the process of developing and deploying a model for a specific problem.||It is employed in the data modeling stage of the data science process as a whole.|
|A data scientist should be able to use big data tools like Hadoop, Hive, and Pig, as well as statistics and Python, R, or Scala programming.||Computer science principles, Python or R programming abilities, statistics and probability ideas, and other skills are required of a Machine Learning Engineer.|
|It can operate with unstructured, organized, and raw data.||To work on, it largely necessitates structured data.|
|Data scientists spent a significant amount of time managing the data, cleaning it, and deciphering its patterns.||Engineers who work in machine learning spend a lot of time dealing with the complexity that arise during the implementation of algorithms and the mathematical ideas that underpin them.|
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