Machine Learning Vs Data Science – Which is Better for your Next Project?
Machine Learning and Data Science are two of the most sought-after terminologies of this era in the tech world. Computer Science students along with big brands like Amazon and Netflix have been after these two technologies. The age of Big Data emerged in the data space when businesses dealt with exabytes and petabytes of data. Until 2010, industries found it hard to store large data. The emergence of frameworks like Hadoop and a few others solved the data storage. Now, the focus has shifted to the processing of data.
Of lately, machine learning and data science have become the core of our routine life, and have affected our lives in several ways. However, many people still do not understand their significance even when they are familiar with artificial intelligence and other terminologies.
A majority of data engineers and skilled data architects are familiar with machine learning and data science. However, many of them are not clear about the differences between these two even after using them as their tools of the trade. If someone plans to deploy machine learning in business, it is essential to know which area to focus on. For this purpose, we shall take a look at these topics in brief.
What is Machine Learning?
Machine Learning is a part of Artificial Intelligence that emerged with the formation of algorithms, which can process data and be self-learned. It means that you can efficiently teach a computer how to learn rather than integrating it with a program to perform every task. Machine Learning is based on this approach of self-learning through algorithms.
Today, you can find numerous applications of machine learning. The popular one to rise among others is the virtual assistant, which has examples like Amazon Alexa or Apple Siri. The core of all these is learning algorithms, which provide results as per individual requests of users. These systems have incorporated learning algorithms that study the user’s habits and provide better outputs.
Face recognition is another application that uses a still picture to identify the person portrayed in it. Facebook is one of the social media services that is capable of examining pictures and tagging friends in a photo. These social media services use algorithms to perform functions such as finding and suggesting people that you may know.
Significance of Machine Learning
The world of machine learning keeps evolving. As a result, its demand and significance have increased. Machine learning offers high-value forecasts that can redirect towards smart decisions and smart behavior in real-time without human intrusion. It is one of the main reasons why data scientists need machine learning.
In recent times, machine learning has gained immense popularity as a tool to interpret huge data and automate data scientists’ activities. Machine learning has emerged as the way to extract data and operate visualization through conventional mathematical tools.
With machine learning, organizations go with data-driven decisions, which help them determine whether they are up to date with the market or lag behind. Machine learning emphasizes customer data and corporate and helps businesses to make choices that enable them to survive the competition. It efficiently contributes to the original market outcomes with its realistic uses.
Machine learning automates those activities that involve a live agent’s assistance like verifying an account balance or updating a password. Thus, it saves time for agents, which they can spend on consumer interaction.
Limitations of Machine Learning
Artificial Intelligence Algorithms do not Work Together
Artificial Intelligence models are not capable of transferring their information from one group of settings to another. It means that when a model accomplishes a use case, it is relevant to that specific use case only. As a result, organizations have to dedicate their resources to training new models continuously even when the use cases are similar.
Machine Learning Algorithms require Huge Storage for Training Data
AI programs are not programmed but educated. As a result, they require to store huge data to execute complex tasks at a human level. However, large data sets are hard to generate for particular use cases despite data being produced quickly. Therefore, you get access to robust processing capability.
Artificial Intelligence is based on the supervised machine learning framework. Labeling is a significant level of data processing while performing controlled learning. This training style uses predefined goal features from historical data. Moreover, the data marking technique is used to clean and sort raw data for neural structures to consume.
Additionally, deep learning requires a huge amount of labeled information, and labeling is a difficult task. Thus, AI is expected to learn how to make choices, understand, and function in the most compatible manner.
What is Data Science?
Data science involves a deep analysis of large volumes of data consisted in the archive of an organization. It involves tracking the data’s location, quality evaluation, and ensuring whether it can be used for business expansion in the future.
A company’s data can be maintained in two ways; organized or unorganized. The data procured can be used to have helpful insights into the customer dynamics or the industry, or take a tactical benefit over the rivals through data collection patterns.
The main aim of Data Scientists is to transform the raw data into essential business information. They are familiar with data processing, statistics, algorithmic coding, and artificial learning. Big names like Netflix and Amazon also broadly utilize data analytics.
Significance of Data Science
Data science helps organizations to address their customers’ requirements in a better way. We know that clients are the foundation of a product, as they help determine its failure or performance. It also helps businesses to communicate with their consumers in various ways, which ensures the quality and strength of the product.
Apart from this, data science also allows products to share their stories mandatorily in an innovative way. It is one of the main reasons why it is popular among data scientists. Brands can communicate with their clients using this collected information, which helps them have stronger product relations.
One of the biggest advantages of data science is that it can be virtually utilized across a wide variety of fields such as schooling, healthcare, tourism, and more. Moreover, data analytics helps organizations assess their troubles and address them effectively.
Enterprises should utilize data analytics to communicate with their customers, which will help them have better products and services. Thus, data science is gaining momentum day by day in every market, as it plays a crucial role in product development. As a result, the demand for data scientists has also increased, as they are engaged with data management and addressing complex troubles.
Moreover, data science has played a vital role in shaping the retail industry with the rise and expansion of supermarkets. It has efficiently contributed to bringing buyers and customers to one platform and smoothening their communication.
Limitations of Data Science
Difficult to expertise
Along with the benefits, data science also has its limitations. For example, it has various corrections that include mathematics, data science, and statistics. It is hard to master all this at the same time and be an expert in each of them.
Data Privacy is at Risk
Businesses highly depend on data to take their significant decisions, and data scientists help create data-driven choices. However, the data procured can upset consumers’ privacy, which may lead to data breaches if the security of the sensitive information is compromised.
Need for Domain Awareness
Among other limitations, data science also has the drawback of being dependent on domain knowledge. Therefore, if an individual is not well-versed with the knowledge of Computer Science and Statistics, he or she is likely to find it hard to solve Data Science issues.
In what ways Machine Learning is used in Data Science?
You can easily understand the use of machine learning with the help of the Data Science lifecycle. Let us take a look at them as follows.
The step of data procurement involves acquiring data to resolve the addressed issue. Moreover, you can get ratings and reviews given by customers on various products. For this purpose, you can create a recommendation system, wherein you get comments, purchase history, and more.
This stage determines business requirements related to solving problems. For example, the business requirement is to maximize sales, and we need to build a recommendation system.
At this stage, the raw data obtained from the previous stage is converted into an appropriate format. Thus, it can be effectively utilized in the steps ahead.
It is also known as the modeling stage, wherein uses machine learning algorithms are used. The entire machine learning process is included in this step. The machine learning procedure performs testing and training the model, data cleaning, and improves the model’s efficiency.
The data examination stage helps us understand the patterns of the data. Also, it allows you to have helpful insights from the data.
Deployment & Optimization
It is the last stage, wherein you need to deploy the model on a real project. In this step, you will be able to examine its performance.
Machine Learning Vs Data Science
1. Machine learning is a part of data science that helps the machine learn automatically from past information and experiences.
Data science understands and finds helpful perceptions and hidden patterns from the data. As a result, you can easily take smart business decisions.
2. Machine learning is used to make forecasts and categorize the output for new data points.
Data science helps to discover understandings from the data.
3. Machine learning is utilized in the data modeling process of data science as a complete method.
On the other hand, data science is a broad term that involves several stages to develop a model for the mentioned issue and deploy the model.
4. A machine learning professional is required to know Python or R, probability concepts, computer science fundamentals, and more.
On the other hand, a data scientist should know data tools such as statistics, Hive, Hadoop, Pig, programming in Python, R, or Scala.
5. Machine learning involves working with structured data.
Data science involves working with raw, structured as well unstructured data.
6. Machine learning professionals are completely occupied with the management of complexities that arise while implementing algorithms and the relevant mathematical perceptions.
On the other hand, data scientists are busy with data handling and cleaning, and studying the patterns.
Data science is a wide field that connects a broad array of data and processing skills to achieve insights. Machine learning is one of the most amazing and thrilling technologies in modern data science. It allows computers to learn unconventionally from the data available.
The applications of both these technologies are widespread, however, they have their limitations. Even though data science is powerful, you can use it only if you have highly skilled and experienced employees in this field.
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