Machine Learning (ML) is a subset of artificial intelligence (AI), while Data Science, as defined by Neil Lawrence, of the University of Cambridge constitutes data mining, statistics, databases, computation, machine learning, and artificial intelligence.
In this cheat sheet, we’ll learn the top 10 key distinctions between Data Science vs. Machine Learning and how the two interoperate, and what are the best practices that are still evolving.
Machine Learning models underpin much of the AI innovations we see today. Think of the fastest route suggestions on Google Maps, computer vision technology in Amazon Go retail store, and speech recognition in Alexa. Machine Learning allows data scientists to scale this process and make it more efficient and repeatable. Data Science, now a growing industry practice, involves understanding data and applying the right techniques to build data applications with tools such as Python, Apache Spark, and Kafka. Machine Learning is part of the data science toolchain.
Data Science is a broad field which uses machine learning algorithms and models to analyze and process data. Apart from ML, it involves data integration, visualization, data engineering, deployment, and business decisions. It employs mathematical algorithms, processes, and systems to extract value from rich data that are collected from various sources such as the web, text data, voice and sensors. For most organizations, Data Science is a proven industry practice, which can help reduce costs, enhance revenue, business agility, and improve customer experience.
At its core, machine learning is a form of artificial intelligence (AI) aimed at building teachable machines. Machine Learning models perform tasks by learning from data instead of being explicitly programmed. A subcategory of AI, machine learning deploys statistical techniques to drive insights from terabytes of data. And when exposed to new data, these applications learn and grow by themselves. In other words, ML applications learn from previous computations and utilize pattern recognition to improve and produce informed and reliable outcomes.
Here are the top 10 key distinctions between Data Science and Machine Learning.
2.1 Data Science vs. Machine Learning Toolchain
To begin with, the various components that form the foundation of Data Science are data collection, data pre-processing, data analysis, distributed computing, data engineering, Business Intelligence, and deployment in production mode that leads to insights and drives new business models.
Whereas, machine learning is the process of developing teachable machines that learn from data and deliver predictions. The components of machine learning include understanding problems, exploring and preparing data, model selection, and training the system. In machine learning, the problem is characterized by input data (e.g. a particular image) and a label (e.g. is there a cat in the image yes/no). The machine learning algorithm fits a mathematical function to map from the input image to the label. The parameters of the prediction function are set by minimizing an error between the function’s predictions and the true data.
2.2 Applications of Data Science vs. Machine Learning
The rise in compute power and the reduction of cost in data storage has made data science an entrenched industry practice in leading organizations. Data Science and artificial intelligence has been termed as Industrial Revolution 4.0 which is spearheading change across legacy sectors such as manufacturing, heavy industries, oil & gas, energy, and powering new innovations in healthcare, retail, finance, insurance, and more.
Machine Learning, at its core is data + model. Machine Learning is applied to accurately classify or predict the outcome for the input data by learning the system using a mathematical model. Machine Learning has become one of the go-to ways for automating low-level repetitive tasks, where input information and output is well-defined.
2.3 Hardware Specifications
Hardware specifications is another primary distinction between machine learning and data science. Data science for business requires horizontally scalable systems to handle massive volumes of data. High-end RAM and SSD are required to avoid the I/O bottleneck. On the other hand, machine learning needs GPUs (Graphic Processing Unit) for intensive vector operations. More advanced versions such as TPUs built by Google are also extensively deployed.
2.4 Data Requirements
In the case of machine learning, specific techniques are employed to pre-process the raw data. For example, feature scaling, adding polynomial features, and word embedding. In Machine Learning, data comes in structured and unstructured form such as text, video and audio. Whereas in Data Science, teams start by understanding the business problem at hand and gather and integrate raw data, which is in different formats. The next step involves building models from input data using techniques such as machine learning and data science.
2.5 Data Science vs. Machine Learning Lifecycle
Data Science lifecycle involves three critical steps — business understanding, model prototyping and model production. In addition to developing and testing models, data engineers work alongside data scientists to build data pipelines. In Data Science, businesses need to build cross-functional teams which include data engineers, data analysts and data scientists. Meanwhile, machine learning is part of the data science process and is concerned with one problem that can be described in discrete terms. By throwing massive amounts of data, the ML model can figure out what is the “correct” action, without having to code the program explicitly. The machine learning engineers need to keep evaluating the model over and over again to enhance its accuracy.
2.6 Data Science vs. Machine Learning Workflow
Machine Learning involves model building, testing, tuning and deployment. There are five stages in this process:
- Importing data
- Data cleansing
- Model building
- Training and testing
- Fine-tuning the model
On the other hand, Data Science is used to handle big data. Data Scientists spend a considerable amount of time gathering and processing data, a process known as data wrangling. Next, they apply numerous techniques to extract information from the data set. The workflow of Data Science involves various stages which includes – understanding business problem, data acquisition, data processing, data exploration, modeling and Deployment.
Machine Learning helps Data Science by providing the algorithms for data exploration.
2.7 Coding Languages
In order to solve Data Science problems, SQL and SQL syntax such as Spark SQL and Hive QL are commonly employed. In addition, Perl, awk and sed can be used as data processing scripting languages. Also, a framework of supported languages i.e. Scala for Spark, Java for Hadoop are widely leveraged to code Data Science problems.
Machine Learning involves the study of algorithms that lets a machine learn and take action on its own. Amongst the numerous machine learning programming languages available, R and Python are the most popular programming languages commonly used. The other programming languages that can be used are Java, Scala, MATLAB, C, C++, etc.
2.8 Data Visualization
In the case of Data Science, visualization plays a key role and BI analysts use tools such as Tableau, Qlik, and Looker to visualize and interpret results. However, in machine learning, visualization is leveraged to express insights from training data. For instance, in a multi-class classification problem, visualization of confusion matrix is used to find false negatives and positives.
2.9 Measuring Output
Performance measure is an indicator of the system’s capability of performing its task accurately. It is amongst the most crucial factors to differentiate between machine learning and data science. In the case of data science, performance measures are not standard and vary from problem to problem. It is often an indication of querying, data quality, user-friendly visualization, effectiveness of data access and more.
However, in terms of machine learning, the performance measure is standard. Each algorithm has a measure indicator which can describe if the model is fit for the given training data and error rate.
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A Data Scientist collects and manipulates huge amounts of raw data. One of the most important factors is to gain critical skills in business analytics, programming, and domain knowledge. Additionally, to create a niche as a data scientist it’s essential to have strong knowledge of Python, R, SAS, Scala and hands on experience in SQL coding. The other skills include understanding of multiple analytical functions, machine learning and ability to work with unstructured data from various sources.
Whereas, for machine learning, the skill-set includes strong mathematical and statistical operations understanding, expertise in computer fundamentals, in-depth programming skills, and data modeling and evaluation skills.
While we have extensively discussed the key differences between machine learning and data science, the two fields also interoperate. It’s essential to remember that data is the main focus in case of Data Science and learning is the main focus for machine learning.
Let’s explain this with a use case. Let us assume that you wish to purchase headphones on a website. This is the first time you are visiting the website and you are browsing through headphones of all ranges. You use the filters to narrow down your search and finally zero down on a couple of options and compare them. Once you choose a particular model, a recommendation will be displayed below the product which may be of a similar product with same or different configuration and price.
How does the website recommend these products though it has no history about you. That’s through the data from several other people who may have tried to purchase the same headphones. This makes the system automatically recommend products to you. Now, Data Science is the end-to-end process of gathering data, cleansing and filtering it out for evaluation, then evaluating the filtered data to build patterns, find trends, and build models to recommend the same thing to other users and optimization.
So, where is machine learning involved in this process? Machine learning algorithms are used to build the model. On the basis of data collected and trends generated, machine understands that these are the particular products that are usually bought by other users along with a particular headset. Thus, it recommends the same item based on previous patterns.
Data Science is a swiftly growing field with massive potential and machine learning is one of the most exciting technologies in modern data science. It lets computers learn autonomously from the wealth of data available. The application of machine learning and data science for business is vast, but not unlimited. Though these technologies are powerful, they work only when there’s quality data.