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What is Data Science? Data Science Tutorial Guide for Beginner

Data science Tutorial Guide
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As data science is being considered as one of the most liked and preferred job for all technocrats, so today we have brought this blog post that can be considered as a guide of this profession. Data Science is the best and most preferred profession that may also need the deep understanding of a few basic concepts.

In this blog post, we will provide an introduction to data science along with its job trends and the basic data science components. We will also discuss what data science is and who can become data scientists? So, let us start our discussion with a brief introduction to the topic i.e. data science.

Topics to be covered in the Blog:

  • A Quick Introduction to Data Science
  • Data Science Tutorial – Who can be Data Scientists?
  • Why should you Learn Data Science?
  • How can Problems be solved in the Data Science?
  • What are the Components of Data Science?
  • Data Scientist Job Trends
  • Various Job Roles for Data Science Experts

A Quick Introduction to Data Science

The term data science involves two mathematical terms one is mathematical statistics and other is data analysis. The journey of this complete profile is amazing and can be easily accomplished by technical and non-technical persons. As it is all about machine learning so future prediction has been made possible by this as well.

As far as data science is concerned then it does mean data-driven science that uses scientific methods, processes, and methods that can be used to extract some useful information either from a structured or unstructured data.

Today, we will discuss these analytic processes and methods in this tutorial Guide so that you can become familiar with that.

Data Science Tutorial – Who can be Data Scientists?

It’s a well-known fact that data scientist must be proficient in mathematics, must be familiar with business fields and have great computer skills, but sometimes a person cannot have all skills. In such case, teams are formed so that each team has the experts of every field. But here the fact is that you should be familiar with at least one skill.

In most of the corporates, the complete job of data science is divided among teams and as per their expertise, the problems are resolved. Moreover, as per the expertise, one can brush-up his appropriate skill and learn Data Science to become the scientist.

Why should you Learn Data Science?

As today, there is a huge amount of data all over the internet and companies are storing more data so organizations analyze it and take out desired and required information from the data repositories. Processing of abundant data is one of the toughest jobs and therefore organizations are hiring professionals for their help.

Read:   What Exactly Does a Data Scientist Do?

With the help of data science, you can understand the customer’s behavior and know their expectations. Their feedback data can be analyzed to know the facts and their expectations. Apart from this, there can be countless benefits of Data Science. You cannot only make better and fruitful decisions but also reduce production cost and give your customer their desired product.

Data Science Tutorial

It basically provides the following advantages:

  • Reduced Cost
  • Focus on Next Product Generation
  • Better and Faster Decision Making
  • Improved Service or Product

How can Problems be solved in the Data Science?

Data science problems are solved by using algorithms, but here the big problem is to choose the right algorithm. There are manly below-listed problem types to be judged and scientists have to decide which algorithm should be used for any particular type of problems:

Is this of type A or B?  Classification Algorithms are used
Is the problem weird? Anomaly Detection Algorithms should be Used
How many or How Much to be Find  Use Regression Algorithms
How to Organize this? Use Clustering Algorithms
What Next Should be Done?  Reinforcement Learning must be Applied

Here, the algorithms selection depends on the type of problem. In the next section of this post, we will discuss each of the problems and their solution one by one:

A). Is this of Type A or B?

These are those problems which have an answer either ‘Yes’ or ‘No’ or we can say in 1 or 0 e.g if the problem is like What will you like to watch either cricket or football then you have only two options here to answer -cricket or football, and the answer cannot be basketball or badminton in any condition.

The problems that have only two types of answers are known as 2-Class Classification problems, while if there exist more than two answers then it is known as Multi-Class Classification problems. So, in short, we can say that such problems can be solved by using categorical algorithms.

B). Is the problem weird?

Such questions involve patterns that can be solved using Anomaly Detection Algorithms. When there is a break-in pattern the algorithm flags that particular event for review. Like if there are a number of transactions to be analyzed then any weird transaction can be flagged to review. As a result security measure can be implemented properly and human efforts can be reduced.

Read:   Difference Between Data Scientist and Data Analyst

C). How many or how much to be found?

If there is any problem that involves mathematical calculation, then it can be solved by using regression analysis. All problems that involve numerical values and figures can be easily solved by using regression analysis.

For example, if one wants to predict the temperature of the next day or week then the answer to this question will be a numeric value and regression analysis can help in finding the answer.

D). How to Organize this?

If you have some data and do not have any idea how to use it and does not make any sense, then you may think that how the problem will be solved? It can be solved by using a clustering algorithm. In these solutions, the data are grouped as per their common characteristics and then the clusters are being formed.

E). What should be done the next?

When your computer has to take any decision depending on your problem then reinforcement algorithms are being used. These algorithms are based on human psychology in which computers like to be appreciated when they are trained. Here, you do not teach computers instead they take their decisions and take the appropriate action.

What are the Components of Data Science?

Data science is a vast field and the complete process has few main components that we are going to discuss in our next section.

Data Science Tutorial

1). Datasets

There are lots of data to be analyzed that is fed either through analytics tool or algorithms. The data is fetched by a number of past researches. Datasets are being formed with the help of such data and then are analyzed.

2). R Language

R is an open source programming language that is used for statistical computing and graphics that is supported by R foundation. R studio uses this language. Mainly the language is being used for the following reasons:

  1. Statistical and Programming Languages
  2. Data Analysis and Visualization
  3. Simple to Learn
  4. Open Source or Free

R Studio can be used to analyze large datasets that can have structured and unstructured data. Such data is also known as Big Data.

3). Big Data

Big Data is a collection of data sets that are too large and complex, so it becomes difficult to process traditional data and database management. As traditional data cannot be handled by the existing software so a new tool and language can solve it easily.

4). Hadoop

Hadoop framework can be used to store and process large datasets in distributed and parallel fashion. Hadoop can be used to store and process data for this it uses HDFS and provides high availability across the distributed ecosystem. MapReduce is used to process data and it uses the ‘map’ and ‘reduce’ processes to analyze data.

Read:   An Insight into the Intriguing World of the Data Scientist

5). Spark R

This R package is a lightweight way to be used with R. It is being used over R applications as it provides a distributed data frame to support selection, aggregation, filtering even on large datasets. Spark R is like R language and can be used with that as well.

Data Scientist Job Trends

This is clear from the graph that job options are just the plenty for the role of data scientist and they are getting attractive salary packages too as per their skills and experience.

Data Science Tutorial

So, you must be pretty much sure now why learning data science actually makes sense. This is not only useful for organizations but had a prosing career choice in the near future too. In the next section, we will discuss the various job roles for data science experts and their average salaries in Indian and the USA.

Various Job Roles for Data Science Experts

The candidates who have the data scientist skills can get various job titles like listed below:

  • Data Engineer
  • Data Scientist
  • Data Architect
  • Data Analyst
  • Data Administrator
  • Business Analyst
  • Analytics Manager
  • Business Intelligence Manager

As per PayScale.com average salary of data scientists in the US and India is shown below:

Data Science Tutorial

To take the career opportunity of data science, one must keep on updating his skills and it is quite clear by the above statistics that the person having more skills will have more chances to get higher salaries. Moreover, as the chart is prepared as per skills, so the variation clearly indicated that Python and Machine Learning languages are at the top in India, and the US both.

Final Words:

So, here we come to the final section of our blog that is the conclusion which makes it clear that the data science can provide you with the most promising career options today. It is not that much difficult to learn the data science and any pre-existing skill can help you definitely.

Python and R, are two languages that are being used to analyze the data. So by learning these languages, you can become a professional data scientist. K-Means, Clustering, Decision Tree, Naïve Bayes are a few of the popular algorithms used that are used in the data science frequently and a practical knowledge can always stand you ahead of the crowd.

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