Data Science For Beginners
Data science for beginners. In this post, we going to see about what is data science? And how to start a data science carrier? And let’s take a look at some top companies using data science. So you can translate this blog into Tamil and now let’s jump into our content.
Data Scientist Carrier,
We hear a lot about how artificial intelligence and machine learning are going to 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.
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. (Tamil)This is where data science comes in. Words data science is the process of using data to find Solutions or to predict outcomes for a problem statement to better understand data science.
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, obviously because Monday mornings are Peek hours and everyone is 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 find out which neighborhoods will be the busiest so that it can activate search pricing to get more drivers on the road in this manner over maximized. The number of rides it can provide and hence benefit from this Uber surge pricing algorithm uses data science.
7 Steps on data scientists carrier,
1. Business requirement :
Let’s see how a data science process always begins with understanding the business requirement or the problem. You’re trying to solve this in this case. The business requirement is to build a dynamic pricing model that takes effect. When a lot of people in the same area are requesting rides at the same time.
2. Data collection :
This is followed by data collection Uber collects data such as the weather. Oracle data holidays time traffic pick up and drop location and it keeps a track of all of this.
3. Data cleaning :
The next stage is data cleaning while sometimes unnecessary data is collected such data only increases the complexity of the problem an example is boober might collect information like the location of restaurants and cafes nearby now such data is not needed to analyze Uber surge pricing there for such data has to be removed at this step data planning is followed by the date.
4. Data Exploration and Analysis :
Exploration and Analysis. The data exploration stage is like the brainstorming of data analysis. This is where you understand the patterns in your data.
5. Data Modelling :
This is followed 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 it thousands of customer records so that it can Learn to predict the outcome more precisely.
6. Data Validation :
Next is the data validation stage nowhere the model is tested when new customer books arrive the data of the new booking is compared with the historic data to check if there are any anomalies in the search prices or any false predictions if any such anomalies are detected a notification is immediately sent to the data scientists at Uber who fix the issue. This is how Uber predicts a surge price for a given location and time.
7. Deployment & Optimization :
The final stage of The 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.
Data Science progress :
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’s recommendation 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.
Amazon :
Let’s consider an example suppose you look for shoes on Amazon, but you do not buy them then in there. Now the next day you’re watching videos on YouTube and suddenly you see an ad for the same item you switch to Facebook there. Also, you see the same ad so how does this happen? Well, this Happens because Google Tracks your search history and recommends ads based on your search history. (Tamil)This is one of the coolest applications of data science. 35% of Amazon’s revenue is generated by product recommendations. And the logic behind product recommendation is data science.
Apple :
Let me tell you another sad story Scott killed in never imagined his Apple watch might save his life, but that’s exactly 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 an individual’s Health this watch collects data such as the person’s heart rate sleep cycle breathing rate activity level blood pressure Etc and 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. So these were a few hours Locations.
Data Scientist Job trends :
Now the question is how and why you should become a data scientist according to Linkedin’s March 2019 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 that are 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 job Trends show the vacancy of jobs 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.
Data scientist skills :
Now, remember that your salary varies on your skills your level of experience your geography, and 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 our and python is a Just you’re required to have a good understanding of processes, like data extraction processing wrangling and exploration. You must also be 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 become 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, make sure you follow my blog until next time happy learning.
Originally published at https://bloggingtamilzhanda.blogspot.com.