In the last few years, data science and machine learning have dramatically exploded into the mainstream and become a hot topic. On a technical level, many industries are increasingly using ML to solve data driven challenges and improve the level of service, product quality and customer insight available.
It’s not just businesses that benefit from DS/ML though – it’s already a huge part of our daily lives, and becoming increasingly ubiquitous with modern life. But why is DS/ML so omnipresent, and specifically what issues can it solve?
Data science combines the scientific method, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.
Read AI ML Insights: What is Quora’s “Platform for Open Exploration,” Poe All About?
What can data scientists do?
Data Scientists can:
- Apply mathematics, statistics, and scientific methods
- Use a wide range of tools and techniques for evaluating and preparing data; everything from SQL to data mining to data integration methods.
- Extract insights from data using predictive analytics and AI, including ML and DL models.
- Write applications that automate data processing and calculations.
- Tell stories which convey the meaning of results to decision-makers and stakeholders.
- Explain how results can be used to solve business problem.
Application of DS/ML in our daily life
DS/ML has entered and impacted our daily lives.
Fashion
Walking down the street and spotting someone in a dress you like, but don’t know where it’s from – while you might previously have been resigned to giving up on it, you may now open your phone, Google a description and be offered several alternatives.
Food
Searching for restaurant recommendations in a new city, or even for a new cuisine closer to home, is one of the most common ways we all use DL/ML in our daily lives without even realizing.
Housing
Mortgage applications, prior to approval, require swathes of personal and property information. This is the bank utilising machine learning models, using the information you provide to estimate what monthly repayments you can afford and if you are likely to default.
Entertainment
Your Netflix and YouTube recommendations get more intuitive over time, based on what you watch and what you might like that’s similar.
Pets
There are millions of stray animals around the world, but if suitable homes can be found for them, happy families will be created as a result. Animal shelters which utilize ML models can predict how popular or how quick an animal will be adopted. In turn, they can pre-plan their spaces and save more lives.
All these applications above are based on either recommendation, classification, or regression ML models.
One of the most famous recommendation competitions might be the Netflix P****, which took place in 2006 and offered $1million p**** to whoever could utilize AI to creative a personalized service. The winner went on to enhance the model by an enormous 10%. The competition was one of the first of its kind, and in the years to follow, it inspired teams all over the world to develop these algorithms and pioneer this technology. Fast forward 15 years, and Netflix has one of the most sophisticated systems of its kind on the market.
Since then, competitions of this nature have increased in popularity, spanning several sectors. H&M recently ran one which attracted global interest, and everything from house prices to pet adoption rates have beget their own competitions. The winners’ solutions often signify incredible progress for these brands.
This list is far from comprehensive – in general, DS/ML is almost omnipresent in our daily lives, with countless iterations impacting almost everything we do. The popularity of DS/ML models shows no sign of slowing, and inevitably will become increasingly integrated into our daily lives until we barely remember life before it.
[To share your insights with us, please write to sghosh@martechseries.com]
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