The word “data science” is often confused with “machine learning.” However, these two fields are actually quite different. In this guide, we will be taking a closer look at the differences between data science and machine learning. We will also be exploring the different career paths that these two fields offer.
What is data science?
Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge from large amounts of unstructured and structured data. It is a field of study that combines elements from computer science, statistics, math, and visualization to uncover patterns and insights in different types of data.
Data science can answer complex business questions and create predictive models that allow organizations to make informed decisions. Data scientists use a mix of techniques ranging from natural language processing, computer vision, and programming to analyze vast amounts of data.
They also have the ability to build complex machine learning models that can be used to predict outcomes. Additionally, data science involves the use of data visualizations to communicate insights and present them in a way that is understandable to stakeholders.
What is machine learning?
Machine learning is a subset of Artificial Intelligence (AI) and data science that focuses on algorithms that learn from data and make predictions based on that data. It enables machines to ‘learn’ without being explicitly programmed. This means that machines can take in data and start making predictions without needing any help from a human being.
Machine learning algorithms use supervised and unsupervised learning techniques to learn from data. Supervised learning algorithms are trained on labeled data, where the output labels are known. Unsupervised learning algorithms are used when the data is not labeled. In this case, the algorithm has to figure out the clusters in the data by itself and then make predictions on the basis of these clusters.
Data science vs machine learning
Machine learning and data science are related fields, but there are some key differences between them. I’d like to highlight in a table some of the major differences. We compare aspects such as career paths, focus, and data variety.
|Aspect||Data Science||Machine Learning|
|Focus||Broad field encompassing data analysis, statistical modeling, machine learning, and domain expertise.||Subset of data science that focuses specifically on creating algorithms that can learn from and make predictions or decisions based on data.|
|Goals||Extract insights, patterns, and trends from data to support business decisions and solve complex problems.||Create models and algorithms that can learn patterns from data and make predictions or decisions without explicit programming.|
|Components||Data preprocessing, exploratory data analysis, statistical analysis, data visualization, domain knowledge.||Algorithm design, model training, evaluation, tuning, deployment.|
|Data Variety||Analyzes various types of data, including structured, unstructured, text, images, and more.||Relies on structured and labeled data for training models.|
|Techniques||Uses a range of techniques including machine learning, statistical analysis, data mining, and domain expertise.||Focuses on designing and implementing machine learning algorithms.|
|Applications||Business analytics, data visualization, predictive modeling, recommendation systems, fraud detection, and more.||Speech recognition, image classification, natural language processing, autonomous vehicles, and more.|
|Skill Set||Requires programming, statistical analysis, domain knowledge, data manipulation, data visualization.||Requires strong programming skills, understanding of algorithms, model evaluation, and domain expertise.|
|Toolkits||Utilizes a variety of tools such as Python, R, SQL, data visualization libraries, and more.||Employs machine learning libraries and frameworks like scikit-learn, TensorFlow, and PyTorch.|
|Data Volume||Handles various data sizes, from small datasets to big data scenarios.||Can handle both small and large datasets, depending on the algorithm and infrastructure.|
|Deployment||Focuses on insights and decision support, often communicating findings to non-technical stakeholders.||Focuses on creating models that can be deployed for real-time predictions or decisions.|
|Career Paths||Data Analyst, Data Scientist, Business Analyst, Data Engineer.||Machine Learning Engineer, Data Scientist (with ML specialization), AI Engineer.|
Which career should you pick?
Both fields offer exciting opportunities, but they have different requirements and applications.
If you enjoy working with data, statistics, and programming and have a strong interest in developing algorithms that can automatically learn from data, then a career in machine learning may be a good fit for you. Machine learning is a rapidly growing field with applications in a wide range of industries, including healthcare, finance, and e-commerce.
If you are interested in using data to gain insights and solve real-world problems and enjoy working with a variety of tools and techniques to extract meaning from data, then a career in data science may be a good fit for you. Data science is a broad field that includes many different roles, such as data analyst, data engineer, and data scientist.
To sum things up
In summary, data science and machine learning are related fields that have both similarities and differences. If you are interested in deriving insights from data and working across the entire data life cycle, then data science may be the best choice for you. However, if you are looking for a career that focuses on building deep learning models, then machine learning may be the better choice.