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What is data science with examples?

 We hear a lot about how artificial intelligence and machine learning will change the world and how the internet of things will make everyone's life easier. But what's the one thing that underpins all of these revolutionary Technologies?

The answer is data. From social media to the IoT devices for generating. Bill amount of data consider The cab service provider Uber. I'm sure all of you have used Uber. What are you think makes Uber a multi-billion-dollar worth company. Is it the availability of cabs, or is it their service?

 Well, the answer is data makes them very rich, but wait, is there enough to grow a business? Of course, it isn't you must know how to use the data to draw useful insights and solve problems. Here is where data science comes in.

 In Words, data science is the process of using data to find Solutions or to predict outcomes for a problem statement to understand data science better. Let's see how it affects our day-to-day activities. It's a Monday morning, and I have to get to the office before my meeting starts. 

 So I quickly open up Uber and look for cabs, but there's something unusual the gab reads A comparatively higher at this hour of the day. Why does this happen?

 Well, because Monday mornings are P cars and everyone rushing off to work. Work the high demand for cams leads to an increase in the cab fares. We all know this, but how is all of this implemented? Data science is at the heart of Uber's pricing algorithm. The Surge pricing algorithm ensures that their passengers always get a ride when they need one.

 Even if it comes at the cost of inflated prices, Uber implements data science to determine which neighborhoods will be the busiest to activate search pricing to get more drivers on the road in this manner over maximized.

 The number of rides it can provide and benefit from this Uber surge pricing algorithm uses data science. Let's see how a data science process always begins with understanding the business requirement or the problem. You're trying to solve this case—the business requirement is to build a dynamic pricing model that takes effect.

 When many people in the same area are requesting rides at the same time. Uber collects data such as the weather. Oracle data holidays time traffic pick up and drop location, and it keeps track of all of this.

 The next stage is data cleaning, while sometimes unnecessary data is collected. Such data only increases the complexity of the problem. An example is that uber might collect information like the location of restaurants and cafes nearby. Such data is not needed to analyze user surge pricing there for such data has to be removed at this step; the date follows data planning.

 Exploration and Analysis. The data exploration stage is like the brainstorming of data analysis. Here is where you understand the patterns in your data. that is called by data modeling. The data modeling stage includes building a machine learning model that predicts the Uber surge at a given time and location.

 This model is built by using all the insights and Trends collected in the exploration stage. The model is trained by feeding thousands of customer records to learn to predict the outcome more precisely. Next is the data validation stage nowhere the model is tested when a new customer

 books arrive, the data of the original booking is compared with the historical data to check if there are any anomalies in the search prices or any false predictions if any such abnormalities are detected, a notification is immediately sent to the data scientists at Uber who fix the issue.

 This is how Uberpredicts a surge price for a given location and time. The final stage of science is deployment and optimization. So after testing the model and improving its efficiency, 

it is deployed on all the users. At this stage, customer feedback is received, and if there are any issues, they are fixed here.

 So that was the entire data science process. Now, let's look at a few other applications of data science is implemented in e-commerce platforms, like Amazon and Flipkart. It is also the logic behind Netflix'srecommendation system now in all actuality. Quality data science has made remarkable changes in today's market.

 Its applications range from credit card fraud detection to self-driving cars and virtual assistants such as City and Alexa. Let's consider an example. Suppose you look for shoes on Amazon, but you do not buy it then in there.

 Now the next day, you're watching videos on YouTube, and suddenly you see an ad the same item you switch to Facebook there. Also, you see the same how does this happen?

 Well, this Happens because Google Tracksyour searches history and recommends ads based on your search history. This is one of the coolest applications of data science. 35% of Amazon'srevenue is generated by product recommendation.

 And the logic behind product recommendations is data science. Let me tell you another sad story Scott killed. In never imagined his Apple watch might save his life, but that's precisely what happened a few months ago when he had a heart attack in the middle of the night. But how could a watch detect a heart attack any guesses?

 Well, it's data science again. Apple used data science to build a watch that monitors and individuals Health. This watch collects data such as the person's heart rate, sleep cycle, breathing rate, activity level blood pressure Etcand keeps a record of these measures 24 bars seven.

 This collected data is then processed and analyzed to build a model that predicts the risk of a heart attack. These were a few hours Locations now; the question is how and why you should become a data scientist. According to Linkedin'sMarch 2019 the survey, a data scientist is the most promising job role in the US, and it stands at number one on glass doors' best jobs of 2019.

 Here are a couple of job trends collected from LinkedIn. Top companies like Microsoft, IBM, Facebook and Google have over a thousand job vacancies, and this number is only going to grow. 

 Hurley these jobs Trends show the vacancy of employment concerning jog defame coming to the salary of a data scientist, the average salary ranges between a hundred thousand dollars two hundred and eighty-two thousand dollars.

 Now, remember that your salary varies on your skills. Your level of experience and your geography also the company you're working for.

 Here are the skills that are needed to become a data scientist. You must be skilled in statistics expertise in programming languages like ours and python. You're required to understand processes, like data extraction, processing ranging, and exploration. 

It would be best if you were well-versed with the different types of machine learning algorithms and how they work. Advanced machine learning Concepts like deep learning is also needed you must also possess a good understanding of the different big data processing Frameworks,

 like Hadoop and Spark, and finally, you must know how to visualize the data by using tools like Tableau and power bi now that you know what it takes to becomes a data scientist. It's time to buckle up and kick start your career as a data scientist. That's all from my side, guys. If you wish to learn more about such trending Technologies,

 

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