Data that used to be only a few kilobytes or megabytes has begun to exponentially explode, and today it produces about 2,500 zettabytes of data every day.
Nowadays, a lot of data is generated every second from all over the world, but I didn't know what to do. In other words, we had a lot of data, but we weren't trying to draw any conclusions from it. And where data science was born is where data needs to be understood and analyzed in order to make better decisions.
Now that you know the need for data science, proceed to this data science tutorial to understand the concepts of data science.
Data Manipulation
Suppose you are using 1000 columns and 1 million rows of employee records. If you look at the dataset right now, you'll be amazed. To make matters worse, my boss asked me to find all the male employees whose salaries were exactly $100,000. It's a difficult task. So how do you find a solution? Would you like to manually inspect each of these 1 million records to see the gender and salary of your employees? Well, that would be a time-consuming and stupid idea. So what is the solution to this in
? Now, this is where data manipulation comes into play. With the help of data manipulation techniques, you can extract interesting insights from raw data with minimal effort. To better understand this, let's take a look at this example.
Now I would like to extract from this dataset only the dataset whose age is 50 years old. Now let's see how to do this using the R language.
census%>% filter (age == 50)
In other words, we needed only one line of code and could extract all the records where the person's age was. Just 50 years. Now imagine that you need to manually look at each of the 32,561 records to see the person's age. Thank you for being able to manipulate the data with a single line of code. Similarly to
, if the person's education is single and you want to extract all records where the marriage status is divorced:
Census%>% filter (education == "Bachelors" & marital
status == "Divorced")
Again, with a single line of code, I was able to get the desired result. Therefore, from these examples, it can be seen that data manipulation helps to gain insights from the data with minimal effort.
Now let's talk about data visualization, the next subfield of data science.
Data Visualization
Data Scientists are sometimes referred to as artists because they can represent their data in the form of aesthetic graphics, not because of their brush skills. As they say, photos are more eloquent than words, and if you can visualize them in beautiful charts, it's clear that you don't want to treat your Excel spreadsheet after your data Excel spreadsheet.
This record contains several species of iris. Now I would like to understand the relationship between the "Sepal length" and the "petal length" of different species. So just looking at the dataset doesn't tell you anything about the pattern. Therefore, you can visualize the data here. In
, let's go ahead and create a scatter plot between the sepals. Length "&" Petals. Length':
plot (data =g plot AES (x = Sepalasngth, Sepal. Length,gth, Petal. Length,)) + geom_point ()
This is the underlying data Isn't it data. Isn't expression't dd expression?expression? isam shows Thailand longest thaIsamepals of the flower, the longer the petals. Not only that, "sets" is the length of the petals. You can also see that the sepal length value is the lowest and "Virginia" has the highest value.
Data Science Applications
Data Science has many real-world applications. Let's take a look at some of them.
Chatbot The
chatbot is a Chatbototedathat responds to all requests. You've probably heard of Siri and Cortana. They are examples of chatbots. These chatbots are the perfect application for data science and are used in a variety of areas such as hospitality, banking, retail and publishing.
Self-driving car Another vicar. Smothering another Inge
data science iDatalf-driviniDatalf-driving talent-drivingdrivingft- the future of the automotive industry.
Image Tagging
I believe all of you have Facebook accounts! Whenever you hover over a person’s picture, Facebook automatically tags a name to that person, and this again is possible with the help of Data Science.
Types of Data Science Jobs
In this Data Science tutorial, you will not only learn Data Science but will also find out various job roles in the domain of Data Science which are listed below:
Data Analyst
A Data Analyst is entrusted with the responsibility of mining huge amounts of data, looking for patterns, relationships, trends, and so on, and coming up with compelling visualization and reporting for analyzing the data to make business decisions.
Data Engineer
A Data Engineer is entrusted with the responsibility of working with large amounts of data. He/she should be available to clear data cleansing, data extraction, and data preparation for businesses working with large amounts of data.
Machine Learning Expert
Machine Learning Expert is a person who uses various machine learning algorithms such as regression, clustering, classification, decision tree, and random forest.
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