Data Science vs Machine Learning | Difference Between Machine Learning and Data Science

Index

1. Introduction of Data Science

2. Introduction of Machine Learning

3. Data Science vs. Machine Learning

4. Similarities between Data Science and Machine Learning

5. Careers for Data Science and Machine Learning

6. Skills needed for Data Science and Machine Learning

At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data.

Introduction of Data Science

The use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become a Data Scientist? What is the difference between AI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.

So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.

Predictive Casual Analytics: In predictive analytics, the goal is to predict an outcome of interest, such as the churn probability per customer. In that case, any predictive feature can be included in the model. In prescriptive analytics, the goal is to identify an action that maximizes or minimizes an outcome of interest. 

Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.

Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.

Introduction to Machine Learning

The term Machine Learning was coined by Arthur Samuel in 1959, an American pioneer in the field of computer gaming and artificial intelligence, and stated that “it gives computers the ability to learn without being explicitly programmed”.

Machine learning is a subfield of artificial intelligence (AI). Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs. Any technology user today has benefitted from machine learning.

Classification of Machine Learning

Machine learning implementations are classified into three major categories, depending on the nature of the learning “signal” or “response” available to a learning system which is as follows:-

1. Supervised Learning: When an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples comes under the category of Supervised learning.
2. Unsupervised Learning: Whereas when an algorithm learns from plain examples without any associated response, leaving for the algorithm to determine the data patterns on its own. This type of algorithm tends to restructure the data into something else, such as new features that may represent a class or a new series of un-correlated values. They are quite useful in providing humans with insights into the meaning of data and new useful inputs to supervised machine learning algorithms. As a kind of learning, it resembles the methods humans use to figure out that certain objects or events are from the same class, such as by observing the degree of similarity between objects. 
3. Reinforcement learning: When you present the algorithm with examples that lack labels, as in unsupervised learning. However, you can accompany an example with positive or negative feedback according to the solution the algorithm proposes comes under the category of Reinforcement learning, which is connected to applications for which the algorithm must make decisions (so the product is prescriptive, not just descriptive, as in unsupervised learning), and the decisions bear consequences. In the human world, it is just like learning by trial and error.
4. Semi-supervised learning: Where an incomplete training signal is given: a training set with some (often many) of the target outputs missing. There is a special case of this principle known as Transduction where the entire set of problem instances is known at learning time, except that part of the targets is missing.

Data Science vs. Machine Learning

Careers for Data Science and Machine Learning

A. Data Scientist: Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.

B. Machine Learning Engineer: Machine learning engineers create data funnels and deliver software solutions. They typically need strong statistics and programming skills, as well as a knowledge of software engineering. In addition to designing and building machine learning systems, they are also responsible for running tests and experiments to monitor the performance and functionality of such systems.

C. Applications Architect: Track the behavior of applications used within a business and how they interact with each other and with users. Applications architects are focused on designing the architecture of applications as well, including building components like user interface and infrastructure.

D. Enterprise architect: An enterprise architect is responsible for aligning an organization’s strategy with the technology needed to execute its objectives. To do so, they must have a complete understanding of the business and its technology needs in order to design the systems architecture required to meet those needs.

E. Data Engineer: Perform batch processing or real-time processing on gathered and stored data. Data engineers are also responsible for building and maintaining data pipelines which create a robust and interconnected data ecosystem within an organization, making information accessible for data scientists.

F. Business Intelligence developer: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems.

Skills Needed for Data Science and Machine Learning

Education: Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data. Explore our post graduate programs on data science here.

R-programming: In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred. R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve.

Python coding: Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists.  Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.

SQL Database/Coding: You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate, and work on data. It gives you insights when you use it to query a database. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.

Machine Learning and AI: Data science needs the application of skills in different areas of machine learning. Kaggle, in one of its surveys, revealed that a small percentage of data professionals are competent in advanced machine learning skills such as Supervised machine learning, Unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.

A career in both these domains will be equally rewarding. Check out GL academy to find free courses that will help you upskill.