IBM anticipates that the popularity of Data Science will ascend by 28% by the end of 2020. Today, LinkedIn’s fastest-growing jobs belong to Data Science domain.
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.
In this Data Science tutorial, you will learn Data Science from the basics. Data Science is a multidisciplinary domain that includes working with huge amounts of data, developing algorithms, working with machine learning and more to come up with business insights.
You will work with huge amounts of data. You will clean the data, prepare it, convert it into a format through which you can derive valuable insights from it.
You need an inquisitive mind, ability to work with huge amounts of data, ask the right questions, and arrive at a solution through the application of various tools, technologies and skills.
Anybody can learn data science since there are no prerequisites for learning this domain.
In this Data Science tutorial, you will also learn about the importance of Data Science in today’s digitally-driven world. Due to the incessant amount of data that we are creating, there is an urgent need to derive valuable insights from this data. Data is the oil of our generation. With the right tools, technologies, algorithms we can make sense of data and convert it into a distinctive business advantage.
Check this table to find out the highlights of data science:
|Criteria||How data science accomplishes it|
|Making sense of data||Through use of various tools, techniques, algorithms|
|How it is connected to AI||Machine learning techniques are used widely here|
|Type of domain||Is both an art and a science|
|Business importance||Is extremely important in a data-driven world|
In this Data Science tutorial, you will not only learn Data Science but will also find out about the various roles in the domain of Data Science which are listed as below:
A data analyst is entrusted with the responsibility of mining huge amounts of data, look for patterns, relationships, trends and so and come up with compelling visualization and reporting for analyzing the data to take business decisions.
The data engineer is entrusted with the responsibility of working with large amounts of data. He should be available to clear data cleansing, data extraction and data preparation for data business for working with large amounts of data.
Machine Learning Expert
The machine learning expert is the one who is working with the 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.
Some of the major applications of data science are as below:
If you want to learn Data Science you should also 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 will possess include, technical acumen, statistical thinking, analytical bent of mind, curiosity, problem-solving approach, big data analytical skills and so on.
If you want to be an expert data scientist, then you need to practice the following things:
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 the various data science problems in the real world. This way you will have an inside understanding of this domain.
There are a lot of competitions and 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 to practical use also.
There is a huge amount of data that is available on the internet. It could be real data or just practice data set. But whatever the nature of the data, it will be very well to work on these data sets to implement your knowledge of data science and get hands-on practice in the domain of data science.
Since data science is a very vast field, in the initial days it could be very good to have a collaborative approach to learn data science. That way you will learn in an interactive and collaborative way and will be on your way to becoming an accomplished data scientist.
In this data science tutorial, you will learn data science to help you get started, 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 that practice makes a man prefect. And to learn data science it is not much different, you need to practice a lot to achieve perfection.
|Criteria||Data Science||Data Analytics|
|Various skills required||Data capturing, statistics, mathematics, problem-solving||Analytical, mathematical, statistical|
|Need to be experts in||Data mining||Data visualization|
|Type of data used||All types of data||Structured & mostly numeric data|
|Standard lifecycle||Explore, discover, investigate & visualize||Report, predict, prescribe & optimize|
A lot of people confuse the role of a data scientist with the role of a data analyst. There are a lot of similarities but there are a lot of difference as well. So the above table gives you a high-level understanding of what are the major difference 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 while data analysis can be thought of as a subset of it. In this data science tutorial you will learn top tools, technologies, skills needed to be a successful data scientist. So this is your preliminary step to learn data science and become an accomplished data scientist.
It is the civil engineering of data. Its acolytes have a practical knowledge of materials and tools joined with a theoretical understanding of what is feasible. Data science is a technique to change the raw data into information. It is the study of where the valuable data comes from, what it represents and how it can Read More
Here, we are going to look at the most convenient and conman Data Science Command tools for quick analysis of data. alias It defines or display aliases. It is a Bash built in. $ help alias $ alias ll='ls -alF' bash GNU Bourne-Again SHell $ sudo apt-get install bash $ man bash bc It is Read More
It is a process or collection of rules or set to complete a task. It is one of the primary concept in, or building blocks of, computer science: the basis of the design of elegant and efficient code, data processing and preparation, and software engineering. In Data Science there are mainly three algorithms are used: Read More
There are many ways to get a dataset like configuring an API, internet, database, etc. To convert binary data into a useful data, we need to perform certain tasks which includes-Decompress files, Querying relational database, etc. It is very much important to track the origin of database and check whether that data is up to date Read More
As we know the obtained data has inconsistencies, errors, weird characters, missing values or different problems. In this situation, you have to scrub, or clean the data before to use this data. So for scrubbing the data some techniques are used which are as follows:- Filter lines Extract certain columns or Read More
Here we will be using R programming language to visualize data. It is very important to visualize the result in a graphical format, to analyze the obtained output. Apart from that, we will be deriving statistics to get all the unique values, identifiers, factors, and continuous variable. We can check the overall result through summary Read More
To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithm like SVM, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data to structured data. It Read More
Download Interview Questions asked by top MNCs in 2019?