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.
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 data will be used by a data scientist at the back-end to understand your behavior and push you re-targeted advertisements and deals to get you purchase what you browsed.
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.
A data scientist is responsible for being as inquisitive as possible with the data set in hand to make the weirdest of business connection.
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 – discovery of data insight
- Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.
- Target identifies what are major customer segments within it’s base and the unique shopping behaviors within those segments, which helps to guide messaging to different market audiences.
- Proctor & Gamble utilizes time series models to more clearly understand future demand, which help plan for production levels more optimally.
How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity.
Then as needed, data scientists may apply quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.
This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.
Data science – development of data product
A “data product” is a technical asset that: (1) utilizes data as input, and (2) processes that data to return algorithmically-generated results. The classic example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data. Here are some 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.
- Gmail’s spam filter is data product – an algorithm behind the scenes processes incoming mail and determines if a message is junk or not.
- Computer vision used for self-driving cars is also data product – machine learning algorithms are able to recognize traffic lights, other cars on the road, pedestrians, etc.
This is different from the “data insights” section above, where the outcome to that is to perhaps provide advice to an executive to make a smarter business decision.
In contrast, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications.
Respective examples of applications that incorporate data product behind the scenes: Amazon’s homepage, Gmail’s inbox, and autonomous driving software.Data scientists play a central role in developing data product.
This involves building out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at wide scale.
What is data science – the requisite skill set
Data science is a blend of skills in three major areas:
At the heart of mining data insight and building data product is the ability to view the data through a quantitative lens.
There are textures, dimensions, and correlations in data that can be expressed mathematically. Finding solutions utilizing data becomes a brain teaser of heuristics and quantitative technique.
Solutions to many business problems involve building analytic models grounded in the hard math, where being able to understand the underlying mechanics of those models is key to success in building them.
Also, a misconception is that data science all about statistics. While statistics is important, it is not the only type of math utilized. First, there are two branches of statistics – classical statistics and Bayesian statistics.
When most people refer to stats they are generally referring to classical stats, but knowledge of both types is helpful. Furthermore, many inferential techniques and machine learning algorithms lean on 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 on 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 ability important? Because data scientists utilize technology in order to wrangle enormous data sets and work with complex algorithms, and it requires tools far more sophisticated than Excel.
Data scientists need to be able to code — prototype quick solutions, as well as 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. A hacker is a technical ninja, able to creatively navigate their way through technical challenges in order to make their code work.
Strong Business Acumen.
It is important for a data scientist to be a tactical business consultant. Working so closely with data, data scientists are positioned to learn from data in ways no one else can.
That creates the responsibility to translate observations to shared knowledge, and contribute to strategy on how to solve core business problems.
This means a core competency of data science is using data to cogently tell a story. No data-puking – rather, present a cohesive narrative of problem and solution, using data insights as supporting pillars, that lead to guidance.
Having this business acumen is just as important as having acumen for tech and algorithms. There needs to be clear alignment between data science projects and business goals.
Ultimately, the value doesn’t come from data, math, and tech itself. It comes from leveraging all of the above to build valuable capabilities and have strong business influence.
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 data-set, 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 data-sets 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
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 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.
To become a machine learning expert, you need to possess knowledge of statistics and probability, technical skills like programming languages and coding, data evaluation and modelling skills and more.
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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