Data Science Tutorial For Beginners

Data Science Tutorial

Welcome to the world of data science for beginners! This tutorial is here to help you understand the basics of data science, even if you’re just starting. Whether you’re curious, a student wanting to learn something new, or a professional looking to improve your skills, we’ll explore the fundamentals of data science together.

We’ll break down what data is, look at different types of data, and learn about the tools and techniques that make data analysis possible. This tutorial aims to make the essentials of data science easy to grasp, so you can use data to make better decisions in our data-driven world. Let’s start this exciting journey together!

What is Data Science

Imagine Data Science like magic, where a Data Scientist is the wizard performing tricks with data. Just as magic has different parts, Data Science combines various things like playing with data, creating visual charts, doing math with statistics, and using Machine Learning.

Each of these parts is important, and in this tutorial, we’ll go through each one to help you understand the wonders of Data Science. Get ready for a journey where we uncover the secrets behind the magic of working with data!

Why would you choose Data Science?

Through the Data Science tutorial for beginners, we can understand what exactly data is! This entity is called data that is present everywhere. In Simple terms, data is just a collection of facts.

A bunch of numbers such as -0.879 and 348 are data. For example, statements like ‘My name is Sam’ or ‘I love Pizza’, are also considered as data. A mathematical formula such as ‘A = …’ is nothing but data, when it reaches the computers, data is nothing but the binary code, i.e., 0s and 1s.

Whereas, data has changed from scarce to super-abundant in the last 2 decades and will keep on increasing exponentially for the next 2 decades. In the previous 2 decades, the data we had that belonged with us was small, structured, and most of a single format and the analytics performed on it was quite simple.

However, with the advance of technology, this data will explode; multiple sources have begun to create huge amounts of unstructured data in different formats. The data, which was just a few kilobytes or megabytes earlier, will blow up exponentially, and today, we produce around 2,500 zettabytes of data every single day!

Now, a vast amount of data is being produced every second from every corner of the world, but we do not know what to do with it. In other words, A lot of data with us, but we don’t put effort into finding any insights from it. Therefore, the need to understand and analyze data to make better decisions has brought Data Science into place.

we will learn about the Data Scientist tutorial for beginners and understand the concepts of it.

What is Data Manipulation

Suppose that you are working with an employee dataset that will consist of 1000 columns and 1 million rows. By just analyzing the dataset, you will be exhausted. Boss has asked you to analyze all the male employees whose salary is exactly US$100,000. This is a difficult task, isn’t it? So, how do we find the solution?

Hence, this is where data manipulation comes in. With the help of data manipulation techniques, you can find interesting insights from the raw data with minimal effort. Let’s take the below example to understand this better.

The census dataset comprises 15 columns and 32,561 rows.

Now, from this dataset, we need only those records of the people in the age group of 50. So, let’s see how can we do this with the R language:

census %>% filter(age==50)

So, all it takes is one line of code, and we can extract all those records where the age of the person is exactly 50.

Now, if we want to extract all those records where the education of the person is ‘Bachelor’ and the marital status is ‘Divorced.’ Here, we can get the result using the below line of code.

census %>% filter(education==" Bachelors" & marital.status==" Divorced")

Again, just a single line of code, and we can get our desired result. So, with these examples, we can understand that data manipulation will enable us to find insights from the data with the smallest amount of effort.

What is Data Visualisation?

Data Scientists are the so-called artists, not due to their skills with the paintbrush, since they can represent the data in the form of aesthetic graphs. We know that pictures will speak much louder than words, there’s no need to deal with numerous Excel data when you can visualize it with beautiful graphs.

Check this Iris dataset below to know more on data visualization

This dataset consists of different species of the iris flower: ‘Setosa’, ‘Versicolor’ and ‘Virginia, along with their ‘sepal length’, ‘sepal width’, ‘petal length’, and ‘petal width.’ Now, we need to learn what is the relationship between the ‘sepal length’ and ‘petal length’ of different species. Through this dataset, we would not get to know any patterns. This is where we can visualize the data.

Now , lets build a scatter plot between ‘Sepal.Length’ and ‘Petal.Length’:

ggplot(data = iris,aes(x=Sepal.Length,y=Petal.Length,col=Species)) + geom_point()

This scatter-plot will provide us with the information that as the sepal length of the flower increases, petal length will also increase. Not just this, we can also see that ‘Setosa’ has the lowest values of petal length and septal length, and ‘Virginica’ has the highest values.

What is Machine Learning

Machine Learning is a place where the real magic happens. This is referred to as the field of Data Science where machines are provided with data and that will allow them to make insightful decisions. 

Think back to when you were a kid and learned about cars. Someone probably showed you a picture and pointed out that cars have four wheels, a steering wheel, windows, and more. Your brain learned to recognize a car when it saw these features.

Now, think about how a machine would learn. If you show a computer lots of pictures of cars and label them as “car,” the machine learns by itself. It keeps seeing the features in those pictures, like the wheels and the steering wheel, and figures out what makes a car. That’s what Machine Learning is all about—teaching machines to understand and recognize things, just like our brains do!

Simply put, in Machine Learning, we provide a machine with raw data or training data to help it learn the features associated with that data. After it learns, we give it new data or test data to see how well it has grasped the information. This is the core idea behind Machine Learning.

A familiar example is the captchas you encounter online. You might see a set of images with various items and cars, and the system asks you to pick the ones with cars. Essentially, you’re helping build a training dataset for the system to recognize cars from a different set of pictures. It’s like teaching the machine to identify things based on what it has learned from your selections.

Data Science And Its Life Cycle

The Data Science journey is like a step-by-step adventure, aiming to uncover insights and predictions to meet business goals. This process, known as the Data Science life cycle, involves several key steps given below

• Business understanding: Before processing data, it is important to understand what the query is or the goals the business needs to achieve. For example, if a business aims to lessen credit loss, then it needs to analyze the factors that affect it. For this, we need to learn our data by its structure, sources, relevance, and its type.
• Data preparation: It is the most essential step in the Data Science life cycle that consists of data extraction, merging different data sources, cleaning, and dealing with missing values. Although it requires more time to clean and transform the data, it is a crucial step to create a good model.
• Exploratory data analysis: Before creating an actual model, we have to collect information about those possible solutions and the affecting factors. We have to figure out a possible solution that gives suitable results after processing the data.
• Data modeling: The prepared data is fed to the data model, which provides the desired output. After choosing the model, we have to select the algorithm that gives the perfect results. To achieve the desired results, we can use hyperparameters while maintaining a balance between generalization and performance.
• After training and adjusting the model according to the requirements, the next step is testing it with new, unused datasets using evaluation metrics. If the model doesn’t produce the desired results, we need to go back and tweak it until it performs well.
• Model Deployment, which is the last stage in the Data Science life cycle. Once the model has gone through thorough evaluation and necessary modifications, it’s ready to be deployed in the chosen channel and format. This marks the point where the data model becomes operational and ready to fulfill its intended purpose. It’s like releasing a well-tested product into the market, ensuring that it performs effectively in real-world scenarios.

Use Of Data Science In Daily Life

Chatbots, those helpful automated bots we encounter, are designed to respond to our queries. Familiar names like Siri and Alexa are prime examples of chatbots. These applications find use in various sectors such as hospitality, banking, retail, and publishing, making our interactions more seamless. Another exciting application is self-driving cars, representing the future of the automotive industry.

If you’ve ever experienced Facebook’s automatic tagging feature when hovering over a person’s picture, you’ve witnessed another remarkable application of Data Science at play. It’s these innovations that showcase the real-world impact of technology in our daily lives.

Conclusion

In summary, for beginners diving into data science, this tutorial highlights the importance of exploring key concepts, staying motivated, and gradually building skills. Remember, the journey is about continuous learning and application. Good luck on your data science adventure!

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