AI project cycle
In this post we list down 5 applications of data science .Each application will help you visualize and understand how diversified data science /AI has become.
E Commerce websites
The most common use of AI that you can think of in today’s world is in recommendation systems. E commerce platforms like Amazon and flipkart have a feature which shows you products that are similar top the one you are viewing or may have viewed in past . Such features are based on learning algorithms which use either your history or the products internal data to suggest new similar products.
Similar is the recommendation system of Netflix.
Medical and healthcare
Recently many advancements have been made in the medical domain where intelligent softwares are being used in order to predict the presence of diseases and helping to diagnose them. Many intelligent softwares have been developed which can visualize patterns from images like CT scans , X-rays and give meaningful insights. One such example is using brain scan results to predict the presence of tumor or possible tumors.
Also many wearable products are being produced which can sense/record your heartbeat and give you warnings and insights about your physical conditions.
Over the last few years banking has majorly shifted towards the online domain. And with that has increased the chances of frauds and cyber security issues . Your bank details /personal data are recorded during transactions and their security is of peak importance . AI has helped Finance companies to come up with models that can detect and flag fraudulent transactions .
Algorithmic trading and stock price prediction is yet another field where AI/data science is being significantly being used.
Ever imagined how you get friend recommendations on Social media platforms. The idea behind the feature is to predict “what are the chances you might follow a certain person” . Platforms like facebook and instagram uses the data of its users to build models that can help them with making such decisions. Graph based algorithms are used to study the connections and build solutions
Automobile industry( AUTO-PILOT)
Various automobile companies are coming up with autopilot technologies , TESLA particularly famous. The Intelligent softwares try to learn and replicate the behaviour of a human driver. Tesla autopilot provides features like Lane changing , auto park , summoning car in parking lot and more. The idea here is to collect data from immediate surroundings and make human like sensible decisions. Various sensors are present which collect data from the surroundings.
Above we discussed 5 basic domains where Artificial intelligence is used . There are many more real life problems which are solved using intelligent and learning based programs. Feel free to explore more!
Data science: definition, existence and purpose. It is not difficult to realize how enormously the amount of data , its transfer rate and storage demands have increased over the last decade . Its amusing to see how within a span of few years we have moved from CDs, to pen drives and from pen drives to cloud storage services. The amount of data being generated and stored has led to development of multiple new technologies.
How has data Collection increased?
If we compare the online services that we use today to their offline counterparts we can easily spot the differences that has led to such enormous amounts of data being generated . Lets compare it sing a very simple example, something as simple as watching movies .
Ten years back to watch a movie, all you had to do was read a news paper , see the movie timings of the theater located nearby, drive up to that place , buy tickets using cash and the only people who could hear your reviews later were the people you talked to.
Now compare this scenario to the present situation where every step you take leaves a digital footprint , a form of data that is stored and recorded, at various places. Suppose you look up for a movie , google records that data , you use a Book my show to buy tickets , it records your details , the number of tickets you bought and had it been a streaming service like Netflix it would have recorded which genre , actor you are interested in to give future recommendations.
Your bank /UPI records the data of the transaction that took place. After watching the movie you can write reviews on multiple platforms (text data) or upload a review video on you-tube(video data). Using your search history data google refines the news section on your device.
Using the data of all its customers movie service providers calculate the profit , the market trend and so much more. Also you want movie recommendations from Netflix that suit your taste! Look at this picture below to understand how data generation has increased.
Data science is the name given to the field dedicated to extract meaningful patterns , mathematical relations from huge amounts o data.
Its purpose is to make out sensible and useful interpretations out of otherwise seemingly weird data which can be used to make business scale predictions. Sometimes its a piece of cake , sometimes it requires intuitive thinking and huge computing resources.
Nevertheless its important to realize that a lot of real life problems are based on chances rather than clear cut boundaries. Consider a problem of weather forecasting. The problem focuses on using the weather data of past few days/weeks to predict the possible weather conditions for the next two days.
Also whenever we deal with data that is on a huge scale Statistics always comes to play . Comparisons are made in terms of averages , for example ” last year the average rainfall was x mms”. And any prediction would always be in terms of chances .Look at the following statements which you may have come across;
- Its highly probable it may rain tomorrow
- You may like this product -Amazon recommendations
- You may like this song -spotify recommendations
The data science models or Artificial intelligence models always come up with a prediction that has higher chances /probability of being true . There are multiple domains in which AI has set its foot . Be it the medical domain where today where intelligent softwares are being trained using patients data records to predict whether a new patient has the chances of same disease or not. Or be it the finance domain which is using AI models to detect fraudulent transactions. The scope is ever increasing !