If there’s something all businesses are after today no matter which sector they operate in, it’s data. The global Big Data market is expected to grow to $103 billion by 2027.
Data science, data analytics, Big Data, machine learning are buzzwords everyone seems to be talking about these days. But what exactly do they all mean, and how do they differ from one another? Knowing that is essential to understanding the current technology trends and choosing the best solutions for your organization.
Read this article to see the key differences between data science and machine learning, and learn when it makes sense to invest in a machine learning-powered solution for your business.
If you have any questions, don’t hesitate to reach out to us. At Codete, we have been supporting organizations across different sectors with data science and machine learning expertise.
What is Data Science?
Data science is an umbrella term that includes areas such as data mining, machine learning, data analytics, and other related disciplines. Data science is a collection of approaches that aim to generate meaningful insights from large data sets, including steps such as data cleansing, preparation, and analysis.
A data scientist puts together data from multiple sources and then applies techniques such as predictive analytics or sentiment analysis to extract information from the data sets. The idea here is unlocking the business value of data and providing accurate predictions that can be used by executives in their decision-making process.
Applications of Data Science
- Search engines – they use data science algorithms to deliver the most relevant results to user search queries.
- Digital ads – whether its display banners or digital billboards, you can be sure that they take advantage of data science to show users the most relevant messages. That’s why they have a much higher CTR than traditional ads.
- Recommendation systems – they help users find the most relevant products from billions of available options and improve the overall user experience through personalization. Many companies use recommendation systems to offer product suggestions that are in line with the user’s profile and their previous search results.
How does Data Science differ from data analysis?
Data science and data analysis are often used interchangeably, but in reality, they’re different. Data analysis is a subfield of data science, and it focuses on generating actionable insights companies can apply instantly on the basis of existing queries.
Data science doesn’t aim to answer specific queries. Instead, it parses through huge datasets, sometimes in unstructured ways, to generate broader insights (like initial observations or future trends). Data science indicates which questions should be asked instead of finding specific answers to them: a data scientist creates questions worth asking, and a data analyst finds answers to them.
Applications of Data Analytics
- Retail – retailers use data analytics insights to optimize the buying experience on their web and mobile platforms, offer recommendations, and personalize the user experience to increase browse-to-buy conversions.
- Smart cities – data analytics is often used in smart city projects in areas such as energy management. For example, that includes energy optimization and distribution, smart-grid management, and improving automation in utility companies. Data analytics helps to control and monitor network devices, manage outages, and dispatch crews for handling incidents.
- Entertainment – another application of data analytics is in sectors such as gaming. Game companies can use it to learn more about the behaviors of users, their likes and dislikes, and relationships – and then use these insights to optimize their experience and spending within the game.
What is the difference between Data Science and Big Data?
Here’s how Gartner defines Big Data: “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
Big Data is a term that refers to massive volumes of data that traditional applications can’t process effectively. The processing of Big Data starts with raw data, which isn’t aggregated and too large to be stored in the memory of one machine.
Applications of Big Data
- Financial services – retail banks, credit card companies, insurance firms, venture funds, and many others generate Big Data; massive amounts of multi-structured data stored in many disparate systems.
- Telecom– telecommunication service providers have masses of both customer- and machine-generated data at their disposal and use Big Data analysis technique to attract new subscribers, retain customers, and expand their business.
- Retail – understanding customers is a priority for both brick-and-mortar and online stores. They usually collect customer data from various sources such as social media, loyalty programs, customer transaction data, and more. They need Big Data solutions to make sense of it all and generate actionable insights for personalizing the customer experience.
What is Machine Learning?
Machine learning (ML) uses algorithms to extract data, learn from it, and predict future trends. ML solutions combine statistical analysis and predictive analysis to identify patterns and hidden insights based on the perceived data.
For example, Facebook’s machine learning algorithms gather data about user behaviors and then predict their interests to recommend articles, shape their news feed, or send them notifications.
Applications of Machine Learning
- Virtual assistants – Siri, Alexa, Google Now help users to find information and complete tasks with the help of voice commands. These solutions collect and refine the information they provide based on their previous interactions with users and generate results that are tailored to individual preferences.
- Smart cities – municipalities struggling with high volumes of traffic take advantage of machine learning solutions for building a map of the current traffic and adjust the city infrastructure (for instance, traffic lights) on the basis of congestion predictions.
- Email spam and malware filtering – to make sure that spam filters are constantly updated to eliminate the most recent threats, companies invest in machine learning solutions able to understand coding patterns and detect spam or malware easily.
When to use Machine Learning?
Is machine learning a universal solution? Considering the popularity of this technology, many people seem to think so.
But the truth is that machine learning isn’t a silver bullet, and implementing it is not always necessary to get great results. Companies can develop robust data science solutions without using any machine learning techniques. For example, if you want to determine value by using simple rules or predetermined steps, you don’t need data-driven learning.
It makes sense to use machine learning in the following scenarios:
- When it’s impossible to scale the solution – analyzing a few hundred emails to determine whether they might be spam is an option. But if you want to scale your solution, you simply need machine learning.
- When it’s impossible to code the rules – companies can automate many manual tasks by using a simpler rule-based solution. But when the application of rules depends on many different factors or they may overlap, coding such rules will become too difficult. That’s when machine learning can help fine-tune your solution.
Machine Learning vs. Data Science – key differences
Here are the most important differences between machine learning and data science you should know to pick the best approach for your project:
Data science has a much broader scope. It generates insights from data by handling real-world complexities like understanding the requirements, data extraction, and others. Machine learning, on the other hand, focuses on classifying or predicting the outcome for a data point accurately by using historical data to learn patterns and create mathematical models.
Data science can take advantage of manual methods, even if they’re not as efficient as machine algorithms. Machine learning needs input from data science because its models are trained on data that needs to be prepared first.
Data science helps define the problems that can be solved using different approaches, among which are machine learning techniques and statistical analysis. In machine learning, the problem is already clear, and engineers use different tools to find the best solution.
In practical terms, a data scientist needs to know SQL to perform operations on data. A machine learning engineer, on the other hand, rely more on languages like Python, Java, and R.
Data science and machine learning both play an important role in today’s tech landscape, where data is the single most valuable commodity organizations have. To make the most of data for your business, you need to team up with an experienced team that has ample knowledge of data science, data analytics, and machine learning techniques.
At Codete, we have been helping companies in sectors such as fintech, insurtech, and automotive to accomplish data science projects.
Get in touch with us if you’re not sure which approach would work best for your business; our data science consultants will design the best solution for your company.