The Difference Between Data Science and Machine Learning

 In Our Thinking

Machine Learning and Data Science are two terms that are being used left, right and center by organizations and individuals, often interchangeably. This is because more often than not, the terms can get confusing owing to subtle differences or overlaps. However, there is a great difference in these and forward-thinking businesses looking to build future applications using these need to get an accurate perspective.

Here are some pointers that will give more clarity:

What is Machine Learning?

Machine learning is a form of applied artificial intelligence. It is rooted in the fact that systems can change their actions as well as their responses when exposed to more data. This leads them to create efficient applications as compared to the ones created by humans.
Machine learning models include understanding the problem followed by exploring the data. The next step is to prepare the data, select the apt model based on the problems and feature sets, and clear performance measures.
In essence, machine learning is one of the ways to teach computers how to adapt to changes in data. The machine not only learns but also gets feedbacks. Based on that, it alters the algorithms and not the underlying math. Think of facial recognition on social media platforms, which is an application of machine learning. Also, getting similar recommendations such as “Pins You Like on Pinterest” is backed by this technology. In a nutshell, it can be used to produce accurate predictions.

What is Data Science?

According to research, five hours and 36 minutes is the amount of time spent by an average marketing professional to collate data and get it ready for presentation. That is a considerable amount of time and leads to 12.5 percent of a person’s average working week or 11 days. Enter Data Science.

It is the study of the source of information, what it represents and how it can be used as a valuable resource for creating business and IT strategies. It uses mathematics, statistics and multiple disciplines related to computer science and it incorporates machine learning, data mining, data visualization, cluster analysis, and similar techniques. With the increasing data volumes, it has become an important part of the big data field.

Data science also includes components for collection and profiling of the data, distributed computing, automated intelligence, data engineering, and production mode deployment. The best use of data science is made by search engines, which utilize the technology to produce the best results in within seconds.

Simply put, data science is all about gathering relevant and detailed insights from the collected data.

Benefits of data science and machine learning

The advantages of machine learning include speed, accuracy, and unbiased analysis as opposed to human analysis.

Data science brings to the table benefits such as quick decision-making driven by data. This leads to increased profitability and improved operational efficiency, workflows, and overall business performance. One of the most significant advantages for businesses is that it can help identify the right set of audiences for targeting.

For instance, streaming services such as Netflix use data and machine learning to find out what the users are more interested in. This helps them come up with shows and movies that are in sync with their target audience’s interest levels. The firm saved $1 Billion in 2017 with the help of this move.

American Express, on the other hand, relies on data analytics as well as machine learning algorithms. This helps the firm detect fraud in real-time. Given the fact that the firm must process $1 trillion in transactions and has 110 million AmEx cards in operation, it makes sense to take the help of machine learning to avoid loss of millions.

Now let’s breakdown the differences to understand both the domains in detail.

Difference between machine learning and data science

Data science creates data by dealing with complexities in the real world. It is still an emerging field as the entire process of identification, understanding the requirement and the analysis of the copious amount of structured and unstructured data can become utterly complicated, time-consuming and resource-intensive – even for the largest enterprises.
Machine learning, on the other hand, can accurately classify or predict the outcomes for new data points with the help of learning patterns. It takes into account the historical data and uses mathematical models to do so.
Let’s understand the difference between the two

The Difference in the Input Data
The output of data generated through data science is highly visual and therefore easy to read and analyze by humans. For instance, these can include visual data or tabular data that gives them a bigger picture. Machine learning transforms the input data depending upon the algorithms by using word embedding.

The Difference in the System Complexity
When it comes to data science, there is a lot of components that are needed to handle unstructured raw data. For machine learning, the complexity lies in algorithms and mathematical concepts. There are cases where more than on ML model is placed with each model contributing to the final output.

The Difference in the Skill Sets
Enterprises looking forward to hiring professionals like data scientists and ML experts need to know the specific skill sets to assess.
For data science, they need to see whether the person has a stranglehold in the domain, can conduct ETL and data profiling, have knowledge of SQL, NoSQL systems, visualization, has a grasp on statistics, knows computer programming, and so on.
For machine learning, there needs to be a strong understanding of Mathematical models, Python/R Programming and data wrangling with SQL Model specific visualization.

Where the Two Meet

As mentioned above, machine learning is often incorporated in data science. Being an AI (Artificial Intelligence) tool, it can automate the entire data-processing section that is required in data science. This is possible with advanced algorithms, which can process huge chunks of data in the shortest time possible. Data scientists collect and process the structured data using machine learning tools, later, converting and summarizing them for key decision-making.
All in all, both data science and machine learning can extract information, predict and offer insights from data in different ways. Businesses need to find out how they can make the most of these two to leverage their processes and operations for better ROIs.

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