Data science VS Machine learning

One of the most common confusions arises among the modern technologies such as AI, ML, BD, DS, DL and more. While they are all closely interconnected, each has a distinct purpose and functionality.

Over the past few years, popularity of these technologies has risen, several companies have now woken up to their importance. On massive levels and are increasingly looking to implement them for their business growth.

Maybe a bit complicated in the beginning, but we assure you everything will be clear after we walk through it.

Data Science

In simple words, data science is the processing and analysis of data that you generate for various insights that will serve a myriad of business purposes.

For instance, when you have logged in on Amazon and browsing through a few products or categories, you are generating data.

This is one of the simplest implementations of data science and it keeps getting more complex in terms of concepts like cart abandonment and more.

Tons of insights lie unnoticed in massive chunks of data and it is data science that sheds new light on areas like customer behavior, operational shortcomings, supply-chain cycles, predictive analysis and more. Data science is crucial for companies to retain their customers and stay in the market.

Data science – The discovery of data insight
  •  Netflix data mining movies to understand what drives users’ interest and use it to decide which original Netflix series to create.
  • Target identifies the main customer segments at its base and the unique buying behavior in these segments, helping to guide messages to different audiences in the market.
  • Procter & Gamble uses time series models to better understand future needs, helping to plan production levels more optimally.

How do data scientists leverage knowledge? When faced with a challenging question, the data scientist becomes the tester. They investigate clues and try to understand patterns or characteristics in the data. This requires a lot of analytical creativity.

If necessary, data scientists can use quantitative techniques to obtain deeper layers: inference models, segmentation analysis, time series predictions, synthetic control experiments, and much more.

This data-driven information is critical to providing strategic guidance. In this regard, data scientists act as advisers to guide business stakeholders on how to act on findings.

Data science – development of data product

A “Data Product” is a technical asset that: (1) uses data as input, and (2) process data returns the results generated by the algorithm. A typical example of a data product is a recommendation engine that collects user data and makes custom recommendations based on that data. Examples of data products:

  • Amazon’s recommendation engines suggest items for you to buy, determined by their algorithms. Netflix recommends movies to you. Spotify recommends music to you.
  •  The Gmail spam filter is a data product: the background algorithm processes incoming messages and determines if the messages are spam.
  •  Computer vision for autonomous cars is also a data product — machine learning algorithms recognize traffic lights, other cars on the road, pedestrians, etc.

This involves building algorithms, as well as testing, improving and implementing technologies in production systems. In this regard, data scientists act as technology developers, building assets that can be widely used.

What is data science – the requisite skillset

Data Science is the convergence of competencies in three main areas:

Mathematics Expertise.

There are textures, dimensions, and correlations in the data, which can be expressed mathematically. Using data to find solutions becomes a brain warning for heuristic and quantitative technologies.

In addition, a misunderstanding is that data science all about statistics. First, statistics have two branches: classical statistics and Bayesian statistics.

When most people refer to statistics, they usually refer to classical statistics, but both types of knowledge are useful. In addition, many reasoning techniques and machine learning algorithms depend on the knowledge of linear algebra.

For example, a popular method to discover hidden characteristics in a data set is SVD, which is grounded in matrix math and has much less to do with classical stats.

Technology and Hacking.

First, let’s clarify that we are not talking about hacking as in breaking into computers. We’re referring to the tech programmer subculture meaning of hacking – i.e., creativity and ingenuity in using technical skills to build things and find clever solutions to problems.

Why is hacking important? Because data scientists use technology to fight for large data sets and use complex algorithms, it needs a lot more tools than Excel.

Data scientists need to be able to codify: prototype fast solutions and integrate with complex data systems.

Core languages associated with data science include SQL, Python, R, and SAS. On the periphery are Java, Scala, Julia, and others. But it is not just knowing language fundamentals

Strong Business Acumen.

It is important that data scientists be a tactical business advisor. Working closely with data, data scientists can learn from data in ways no one else can understand.

This approach creates responsibility for translating observations into knowledge sharing and contributes to strategies on how to address key business issues.

This means that the primary competence of data science is the use of data to tell the story. No data boost; instead, ask for a consistent description of problems and solutions, using data information as a support pillar, leading to orientation.

Having this commercial insight is as important as being for technology and algorithms. Data science projects and business objectives must be clearly aligned.

Ultimately, the value does not come from data, mathematics, or the technology itself. It’s about leveraging all of the above to build valuable capabilities with a strong impact on the business.

Machine Learning

For simple comprehension, understand that machine learning is part of data science. It draws aspects from statistics and algorithms to work on the data generated and extracted from multiple resources.

What happens most often is data gets generated in massive volumes and it becomes totally tedious for a data scientist to work on it. That is when machine learning comes into action.

Machine learning is the ability given to a system to learn and process data sets autonomously without human intervention. This is achieved through complex algorithms and techniques like regression, supervised clustering, naive Bayes and more.

  • Supervised machine learning algorithms 

can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.

The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

  • In contrast, unsupervised machine learning algorithms

used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

  • Semi-supervised machine learning algorithms

fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.

Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring-unlabeled data generally doesn’t require additional resources.

  • Reinforcement machine learning algorithms

a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.

Machine learning enables the analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly.

Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Data science is an all-encompassing term that includes aspects of machine learning for functionality. Machine learning is also part of artificial intelligence, where a distinct set of purpose is met on a whole new level.

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