What’s the difference between Data Science and Machine Learning?
Are you curious about the disparities between data science and machine learning? Although they are interconnected, these fields have distinct characteristics. In simple terms, data science involves organising vast data sets to extract meaningful insights, while machine learning concentrates on learning from the data itself. In this article, we delve deeper into the nuances of each discipline and explore their applications and challenges.
What is Data Science?
Data science is a multidisciplinary domain that harnesses the potential of today’s massive data sets. Using advanced tools, data science professionals analyse raw data, process it, and develop valuable insights. The field encompasses various areas such as data mining, statistics, data analytics, data modelling, machine learning modelling, and programming.
Data science plays a vital role in identifying business problems that can be solved using machine learning techniques and statistical analysis. By understanding the issue at hand, determining the required data, and analysing it effectively, data science helps address real-world challenges.
What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that relies on the insights derived from data science. ML employs data science tools to clean, prepare, and analyse unstructured big data. This allows machines to “learn” from the data, leading to improved performance and informed predictions.
As humans learn through experience rather than just following instructions, machines learn by applying tools to analyse data. Machine learning involves working on known problems using specific tools and techniques to create algorithms that enable machines to learn from data with minimal human intervention. With the ability to process enormous amounts of data, machine learning continues to evolve as more data is processed.
Challenges in Data Science
Data scientists often spend up to 80% of their time finding, cleaning, and preparing data for analysis. Despite being a tedious task, it is crucial to ensure accuracy and reliability.
Compiling data from various sources, often collected in different formats, can be simplified using virtual data warehouses. These platforms provide a centralised location for storing data from diverse sources.
Data science presents challenges in identifying pertinent business issues, such as declining revenue or production bottlenecks. Detecting patterns that are difficult to identify adds another layer of complexity. Communicating results to non-technical stakeholders, ensuring data security, facilitating collaboration between data scientists and engineers, and determining appropriate key performance indicators (KPIs) are additional challenges faced in the field.
The Evolution of Data Science
The emergence of big data from sources like social media, e-commerce sites, internet searches, and customer surveys led to the birth of data science as a distinct field. These vast datasets, which continue to grow, enable organisations to monitor consumer behaviour, predict trends, and make informed decisions.
However, interpreting unstructured data for decision-making purposes can take time and effort. This is where data science plays a crucial role.
The term “data science” was first used interchangeably with “computer science” in the 1960s. It gained recognition as an independent discipline in 2001. Today, professionals in various industries utilise data science and machine learning. To work as a data analyst, proficiency in Structured Query Language (SQL), mathematics, statistics, data visualisation, and data mining is essential. Knowledge of data cleaning, processing techniques, programming, and AI is also valuable, as data analysts often build machine learning models.
Applications of Data Science
Data science finds extensive applications in industries and government sectors, driving profitability, innovation, and improvements in infrastructure and public systems. Here are some notable examples:
- Banking: ML-powered credit risk models enable faster loan approvals through mobile apps.
- Manufacturing: Development of 3D-printed sensors for driverless vehicles.
- Law enforcement: Statistical incident analysis tools assist in optimising the deployment of officers for crime prevention.
- Healthcare: AI-based medical assessment platforms analyse medical records to assess stroke risk and predict treatment outcomes.
- Breast Cancer Prediction: Data science is used to develop predictive models for breast cancer detection.
- Transportation: Big data analytics aid in predicting supply and demand for ride-hailing services, optimising driver allocation.
- E-commerce: Predictive analytics enhances recommendation engines for personalised customer experiences.
- Hospitality: Data science ensures diversity in hiring practices, improves search capabilities, and offers valuable insights to optimise operations.
- Media: Personalised content development, targeted marketing, and dynamic music streaming are some data science applications.
The Evolution of Machine Learning
The concept of machine learning dates back to the 1950s when data scientist Alan Turing proposed the famous Turing Test, questioning whether machines can exhibit human-like intelligence. In 1952, IBM computer scientist Arthur Samuel coined “machine learning” and developed a checkers-playing program. The program defeated a checkers master, showcasing the potential of machine learning.
Today, machine learning has advanced significantly. Engineers specialising in machine learning require knowledge of applied mathematics, computer programming, statistical methods, probability concepts, data structures, and other computer science fundamentals. They also utilise big data tools like Hadoop and Hive, while programming languages like R, Java, SAS, and Python are commonly used for machine learning applications.
Machine learning and deep learning are subsets of AI. Deep learning specifically mimics the functioning of the human brain, allowing machines to recognise complex patterns in text, images, sounds, and other data, thereby generating accurate insights and predictions. Deep learning algorithms are designed as neural networks inspired by the structure of the human brain.
Subcategories of Machine Learning
Machine learning encompasses various algorithms, including linear regression, logistic regression, decision trees, Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN). These algorithms can fall into three categories: supervised learning, unsupervised learning, or reinforcement learning.
Machine learning engineers can specialise in subfields such as natural language processing, computer vision or become software engineers focused on machine learning.
Challenges in Machine Learning
Machine learning raises certain ethical concerns, particularly regarding privacy and the use of data. Unstructured data gathered from social media platforms without users’ knowledge or consent has become controversial. While license agreements may outline data usage, many users must know about the fine print.
Another challenge lies in the interpretability of machine learning algorithms. It is not always clear how these algorithms make decisions. One potential solution is to release machine learning programs as open-source, allowing individuals to examine the source code and ensure transparency.
The presence of biased data in machine learning models is also a concern. If biased data is used, it can impact the fairness and accuracy of the outcomes. Accountability in machine learning refers to the extent to which individuals can understand and correct the algorithm and determine responsibility in case of adverse outcomes.
Furthermore, there are concerns about the potential job displacement caused by AI and machine learning. While some jobs may be transformed or replaced, machine learning is also expected to create new opportunities. It can automate routine tasks, freeing human resources for more creative and impactful work.
Applications of Machine Learning
Various industries and sectors widely utilise machine learning.
- Social media platforms leverage machine learning to gather user data and deliver personalised recommendations.
- On-demand video subscription services rely on recommendation engines driven by machine learning algorithms.
- The development of self-driving cars heavily relies on machine learning technologies.
- Tech companies, cloud computing platforms, athletic equipment manufacturers, electric vehicle producers, space aviation enterprises, and many others also utilise machine learning in their operations.
Practising data science and machine learning comes with its own set of challenges.
Fragmented data, a shortage of skilled professionals, and the need to choose appropriate tools, practices, and frameworks can pose difficulties. Operationalising machine learning models, ensuring accuracy, and maintaining auditable predictions are additional challenges.
Understanding the distinction between data science and machine learning is crucial for navigating the world of advanced analytics. While data science focuses on extracting insights from big data, machine learning harnesses those insights to learn and make predictions. Both fields have unique applications, challenges, and impacts on various industries. By leveraging the power of data science and machine learning, organisations can unlock new opportunities and drive innovation in an increasingly data-driven world.