Data Science Tutorial for Beginners
IBM predicts that the demand for Data Science will scale by 28% by the end of 2020. Today, LinkedIn’s fastest-growing jobs belong to Data Science Skills. According to Forbes, Data Scientist’s job is named as the best job in America for the 3 consistent years. As per Glassdoor, there are 16,706 job postings for Data Scientists and the median salary of a Data Science professional in the US is $110,000 per annum. Forbes predicts that by 2020, the Data Science and Analytics job postings are expected to grow by 15% with an additional listing of 364,000 jobs.
Watch this Data Science Tutorial video
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Let’s look at the agenda of this Data Science tutorial:
- Need of Data Science
- Applications of Data Science
- Types of Data Science Jobs
- Qualities of a Data Scientist
- How to Learn Data Science from Scratch
- Comparison of Data Science with Data Analytics
Need of Data Science
In this Data Science tutorial for beginners, we will start off by understanding what exactly data is! This entity called data is present all around us; it’s omnipresent like God! Simply put, data is just a collection of facts.
A bunch of numbers like -0.879 and 348 is data. When we say statements like ‘My name is Sam’ or ‘I love Pizza’, this again is data. A mathematical formula such as ‘A = ’ is nothing but data, and well, when it comes to computers, data is nothing but the binary code, i.e., 0s and 1s.
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Now, why is this necessary?
Because this data has gone from scarce to super-abundant in the past two decades and will keep on increasing exponentially for the next two decades. Around two or three decades back, the data which we had with us was small, structured, and mostly of a single format and then the analytics performed was quite simple.
But with the advent of technology, this data started to explode; multiple sources started to generate huge amounts of unstructured data of different formats. The data, which was of just a few kilobytes or megabytes earlier, started blowing up exponentially and, today, we generate around 2,500 zettabytes of data every single day!
Lately, huge amounts of data were being generated every second from every corner of the world, but we did not know what to do with it. In other words, we had a lot of data with us, but we were not trying to find out any insights from it. And this need to understand and analyze data to make better decisions is what gave birth to Data Science.
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Now that we are aware of the need for Data Science, we will move ahead in this Data Science tutorial to look at some of the applications of Data Science.
Applications of Data Science
Data Science has a lot of real-world applications. Let’s have a look at some of those:
Chatbots are basically automated bots which respond to all our queries. I believe all of you must have heard of Siri and Cortana! They are examples of chatbots. These chatbots are perfect applications of Data Science and are used across different sectors like hospitality, banking, retail, and publishing.
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Types of Data Science Jobs
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 take business decisions.
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 for working with large amounts of data.
Machine Learning Expert
A Machine Learning expert is the one who is working with various Machine Learning algorithms like regression, clustering, classification, decision tree, random forest, and so on.
A Data Scientist is the one who works with huge amounts of data to come up with compelling business insights through the deployment of various tools, techniques, methodologies, algorithms, and so on.
Qualities of a Data Scientist
If you want to learn Data Science, you should be aware of the various strengths of a Data Scientist. In this Data Science tutorial, you will also see that there are a lot of skills that you need to master in order to become a successful Data Scientist.
Some of the skills that an accomplished Data Scientist possesses include technical acumen, statistical thinking, analytical bent of mind, curiosity, problem-solving approach, big data analytical skills, and so on.
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How to Learn Data Science to Be an Expert?
If you want to be an expert Data Scientist, then you need to practice the following things:
- Familiarize yourself about the real-world Data Science problems
Like one famous person once said that the whole world is one big data problem. So, as a Data Scientist, it is your job to learn more and more about various Data Science problems in the real world. This way, you will have an inside understanding of this domain.
- Participate in Data Science forums and competitions
There are a lot of forums that are regularly hosting Data Science contests and competitions for Data Scientists. You would do well not only learn Data Science but also participate in these highly exciting contests. That way, the knowledge that you get from this Data Science tutorial can be built up and put into practical use.
- Regularly work on huge datasets
There is a huge amount of data that is available on the Internet. It could be real data or just a practice dataset. But, whatever be the nature of this data, it will be beneficial to work on it to implement your knowledge and get hands-on practice in the domain of Data Science.
- Have a collaborative and interactive approach
Since Data Science is a very vast field, in the initial days, it would be good to have a collaborative approach to learn Data Science. That way, you will learn it in an interactive way and will be on your way to becoming an accomplished Data Scientist.
- Practice every day and gain a definitive edge
So far in this Data Science tutorial, you have learned Data Science, but that would not be enough. If you want to build your skills and hone it to perfection, then you need to practice every day since, as we all know, practice makes a man perfect. To learn Data Science, the rule is not much different; you need to practice a lot to achieve perfection.
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Comparison of Data Science with Data Analytics
A lot of people confuse the role of a Data Scientist with the role of a Data Analyst. So, we will go ahead and understand the similarities and differences between Data Science and Data Analytics in this Data Science tutorial.
|Criteria||Data Science||Data Analytics|
|Skills Needed||Data capturing, statistics, and problem-solving||Analytical, mathematical, and statistical skills|
|Type of Data Used||All types of data||Mostly structured and numeric data|
|Standard Life Cycle||Explore, discover, investigate, and visualize||The report, predict, prescribe, and optimize|
The above table gives you a high-level understanding of what the major difference is between a Data Scientist and a Data Analyst. One more key difference between the two domains is that data analysis is a necessary skill for Data Science. Thus, Data Science can be thought of a big set, where data analysis can be a subset of it.
In this Data Science tutorial, you have learned top tools, technologies, and skills of Data Science from scratch. This is your preliminary step to learn Data Science and become an accomplished Data Scientist.
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