Today’s world heavily depends on data as the driving force behind the economy. It is estimated that we produce at least 2.5quintillion bytes of data every day, a number you probably didn’t know existed. Data helps us find essential insights about how to improve results and increase efficiency in various aspects of life, be it in manufacturing, transportation, education, or medicine. To think of this is exciting. But, like anything exciting, there are always a few facts and untold pieces of information that could surprise even the most enthusiastic big data followers.
Data science and machine learning are often used interchangeably in trying to make sense of data. However, although related, the terms mean different things and pursue different goals. Both concepts fall within the technology field and correspond with high-earning and in-demand career paths. Although data scientists in nearly every industry apply both ideas in their work, learning the differences can help define your career decision.
Data science is not to be confused with data analysis either.
Data science vs. machine learning: the differences?
When looking at a job description, trying to figure out what constitutes a data scientist and a machine learning engineer can be confusing. The two fields are similar in that squares are like rectangles, but rectangles are not squares. Data science is the rectangle, while machine learning is the square; creating something different requires a unique skill set. Data science involves researching, building, and interpreting a model you have built, while machine learning involves producing that model. Data science uses a scientific approach to obtain meaning from data, while machine learning deals with system programming to automate and improve learning from data.
What you need to keep in mind when distinguishing the two is that data is the main focus of data science, while learning is the focus of machine learning. Data science is about all the processes of collecting, cleaning, and filtering data for evaluation, then evaluating the filtered data and building a pattern or finding similar trends, then building a model for a recommendation of the same for other users, and finally optimizing. Machine learning comes into play at the data modeling phase of the data science lifecycle. It is dedicated to understanding and building data techniques to improve performance and inform predictions.
Machine learning cannot exist without data science since the data needs to be prepared before creating, training, and testing the model. While data science helps figure out new problems, machine learning already knows a current issue and applies the tools and techniques to determine an intelligent solution. Data science is in itself a complete process, while machine learning is one of the steps in data science.
What is data science?
Data science is the study of data to establish its origin, content matter, and how it can be of benefit. It is about equipping you with how to extract meaning from complex large amounts of data. The data either be structured or unstructured, and the goal is to obtain valuable insights about business or market patterns to help inform business decisions. Data scientists are specialists who work to convert raw data into meaningful business matters. They are usually trained and skilled in algorithmic coding, data mining, machine learning, and statistics. Data science also incorporates other fields like mathematics, statistics, and computation to understand and present data.
The importance of data science
Data science is buzzing in the technology field, and it’s for a good reason. By making sense of data, we can reduce uncertainty’s horrors, which is critical for helping businesses adapt and innovate.
- Data is the most important asset you can own today, and so is the science of decoding it. However, data needs to be read and analyzed for data to be valuable, and data science makes this possible.
- Data can inform companies and organizations on how to improve customer experiences. The science of data makes it possible to unveil intelligent solutions and decisions based on trends and patterns in data.
- Data science has the power to extract insights from large volumes of data within a short time, thus increasing efficiency.
- It simplifies report making and provides accurate and reliable details.
- Data science has numerous applications today. They include anomaly detection, classification, recognition, pattern detection, forecasting, optimization, and recommendation.
Data science careers
- Data scientist – Responsible for data collection, analysis, and visualization. They can also be involved in building machine learning models.
- Data analyst – Helps to collect, clean, analyze and report data. They sometimes also track and monitor web analytics.
- Business analysts – They use data to make actionable decisions for their organizations.
- Data engineers – They are responsible for building and maintaining data pipelines. They have to set up and test ecosystems used by data scientists to run algorithms.
- Machine learning engineer – Responsible for building machine learning systems.
What is machine learning?
Machine learning studies algorithms and statistical models to determine the best fit for a problem. The field focuses on equipping computers to learn new things without being manually programmed. Instead, it is a branch of artificial intelligence that relies on algorithms to extract data and inform future trends.
We interact with machine learning daily. For example, think of social media platforms like Instagram and Facebook that gather user information based on previous behavior, then attempt to predict your interests or desires and recommend products, services, or articles relevant to you.
Machine learning is part of the data science process as a tool and concept. It helps gather information from a large amount of data more quickly and assists in trend analysis. However, unlike data science, which is a complete process, machine learning is just a single part of the entire data science process.
Importance of machine learning
Machine learning is now being implemented across various industries. It helps cut costs by allowing algorithms to make decisions based on data trends and patterns. This increases efficiency and accuracy, which could be a solution to most problems experienced when it is a human making the decision.
The use cases for machine learning range from predicting customer behaviors to forming the operating systems of essential tools and devices. For example, customer relationship management and business intelligence are today influenced mainly by machine learning. These systems rely on machine learning to identify patterns in data, recommend solutions and even identify anomalies.
Machine learning careers
- Machine learning engineer – Uses programming languages like Python, Java, and Scala to run tests on machine learning libraries. Machine learning engineers design and develop scalable machine learning models by applying machine algorithms and tools.
- AI engineer – Help build artificial intelligence development and production infrastructure and assist in implementing it.
- Cloud engineer – Responsible for building and maintaining cloud infrastructure.
- Designer in human-centered machine learning – Responsible for developing human-like systems that machines can recognize and process. The goal is to significantly reduce the need for humans to design programs for every new piece of information manually.
Computational linguists help computer systems learn how to understand spoken languages while improving on the existing systems.
Skills that you need to become a professional in these fields
- Understanding of Statistics, mathematics, and probability
- Data mining and cleaning
- Understanding of SQL databases
- Ability to use data tools like Hadoop, Hive, and Pig
- In-depth knowledge of programming languages like Python, R, and SAS
- Ample knowledge of machine learning algorithms and models
- Familiarity with structured and Unstructured data management techniques
- Skills in data visualization and data wrangling
- Computer science knowledge, including data structures, algorithms, and architecture
- good understanding of statistics and probability
- programming knowledge
- skills in data modeling and analysis
- text representation techniques
These are some of the common technical skills required to pursue a career in either data science or machine learning. You will also have to invest in some soft skills, such as good communication skills, that are essential for any employee.
Choosing between data science and machine learning
When deciding on a career path, narrowing your focus and growing your skills in a single field is always the best. However, when it comes to data science and machine learning, you cannot separate the two. Machines cannot learn without data, and data can only be processed and analyzed within data science standards. It is, therefore, inevitable to learn about both fields and how they relate to each other. Even as a machine learning engineer, you still require a working understanding of data science to improve the quality of your work. In the end, you may choose to concentrate most of your skills on only one of the fields, but since the two overlap, you afford to ignore either completely.
Overall, you cannot say one is better than the other because the two have different vital applications in the big data world. For example, data science helps you make sense of data to make better decisions and anticipate the future. In contrast, machine learning enables you to automate and increase efficiency in tasks relating to analyzing, understanding, and identifying patterns in data.
While both machine learning and data science are great career paths, the end decision is determined by your interests. For example, suppose you are enthusiastic about data, automation, and algorithms. In that case, if you do not mind spending hours a day analyzing vast amounts of data, then a career in machine learning may suit you. On the other hand, suppose you are a multi-disciplinary individual who enjoys both the technical aspect and does not mind the less exciting bit like writing reports. In that case, you could thrive more in a data science career.
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