What is Data Science? Learn from This Data Science Tutorial

Introduction

Data Science is considered one of the most liked and preferred jobs for all technocrats, so today we have brought this blog post that can be considered a guide to this profession. Data Science is the best and most preferred profession that may also need a deep understanding of a few basic concepts. Our online Data Science Certification Course and Training Program lets you master the basic and advanced concepts with real-life industry cases that will enhance your candidature in the job market.

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 a data scientist. Build data science skills, learn Python & SQL, analyze & visualize data, and build machine learning models with advanced Data Science Online Courses. So, let us start our discussion with a brief introduction to the topic.

What is Data Science?

The term Data Science involves two mathematical terms, one mathematical statistics, and the 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, 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 structured or unstructured data.

Today, we will discuss these analytic processes and methods in this tutorial Guide so that you can become familiar with them. Becoming a Data Scientist is a dream for many but does not have the end to end knowledge about the same. Get a full-fledged knowledge about What is Data Science with examples from this blog.

Why Should You Learn Data Science?

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

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 more fruitful decisions but also reduce production costs and give your customer their desired product.

It basically provides the following advantages:

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

See an advancement in your career by learning Data Science and getting certified in it.

How can Data Science be a Problem-Solver?

Data Science problems are solved by using Data Science 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 types of problems:

Is this 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 Should Next be Done?	Reinforcement Learning must be Applied

Here, the algorithm’s selection depends on the type of problem. Data Science allows us to solve problems with a sequence of steps:

Step1- Collection of data

Step2- Pre-processing of the data

Step3- Analyzing data

Step 4- Driving insights and generating reports

Step5- Taking insights based on decisions

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In the next section of this post, we will discuss each of the problems and their solution one by one:

A). Is this 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.

Hence, you have two options in this, but you have to answer only one like an on/off button (or toggle switch). 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 Data Science algorithms.

B). Is the problem weird?

You might have come across a game “odd one out,” in which you have to find the odd image or thing in the existing image.

The above image shows the “odd one out” concept. What is odd or weird in this image? Redman in the above image is odd or an anomaly.

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 several transactions to be analyzed, then any weird transaction can be flagged to review. As a result, security measures can be implemented properly, and human efforts can be reduced.

Other than just reading textbooks, it is better if you step up and follow top Data Science Influencers or Data Science leaders who have great experience around data. 

C). How many or how much is 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 it does not make any sense, then you may think about the functionalities of data cleaning. 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 formed.

You may clearly see in the above image the three different groups of clusters. Here, why did I use “different groups”? Because the cluster groups can be easily differentiated because of the three different colors. Similarly, with data with any information in it, clustering algorithms try to capture the common in them and cluster them together.

E). What should be done next?

When your computer has to make 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.

Learn how to become a Data Scientist with no experience from this blog. 

What are the Components of Data Science?

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

1). Datasets

There is a lot of data to be analyzed that is fed either through analytics tools or algorithms. The data is fetched by several past researchers. Datasets are 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 the R Foundation. R studio uses this language. Mainly the language is being used for the following reasons:

• Statistical and Programming Languages
• Data Analysis and Visualization
• Simple to Learn
• 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.

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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, a new tool and language can solve it easily.

4). Hadoop

Hadoop framework can be used to store and process large datasets in a 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. Get yourself signed in for our comprehensive, real-world projects led Hadoop Training Program to master the skills and tools in the Hadoop ecosystem.

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, and filtering even on large datasets. Spark R is like the R language and can be used with that as well.

What are the Different Categories of Data in Machine Learning?

Almost anything can be turned into DATA. Building a deep understanding of data is a crucial part of Machine learning models. Most classifications in Data Science can be done into 4 types from a Machine learning perspective-

1. Numerical Data- Also known as Quantitative data, this data type uses measurement and is characterized by Continuous or Discrete data. For example, the number of students taking Python classes would be a discrete data set. On the other hand, continuous data are numbers that can fall anywhere within a range.
2. Categorical Data- Categorical data represents characteristics and can also take numerical. For example, categorical data could be a class label like man, property, etc.|
3. Time Series Data- Time series data are a sequence of numbers collected in regular intervals over a period of time. Time series data has a time value, so it would be something like a date or a timestamp that you can look at trends over time.
4. Text- Text data is basically data in words. It stores any kind of text data and can contain both single-byte and multibyte characters.

Your "Resume" is the most important aspect in getting you an interview call. Here is the Data Scientist resume guide that will give you an interview call. 

What are the Applications of Data Science?

Data Science is a wide concept of modern technology, and it is applicable to a wide range of platforms. Some of the primary applications of Data Science are: -

• Internet research: To search for a specific keyword, Google and other search engines make use of Data Science technology to show the results in a fraction of second.
• Suggestions-based system: You might have seen “Friend suggestions” on Facebook and “Follow suggestions” on Instagram. This is a suggestions system that also makes use of Data Science technology.
• Image and Speech Recognition: Alexa, Google Assistant, and Siri are the best assistant for modern people. Speech and image recognition are also done with the help of Data Science.

Data Processing happens when data is gathered and converted into usable data. Learn how to process Data from this blog. 

• Gaming: From game development to game monetization, the role of Data Science is enormous. With the growth in gaming users, user playing time, interaction time, quitting time, results, scores, etc. analytics are all performed by Data Science only.
• Online Price Comparison: When you search for the comparison of gadgets on Google, the mechanism used by those websites is Machine Learning. Several examples of websites in this criteria are- Junglee, PriceRunner, SmartPrix, Shopzilla, etc. These websites update themselves as per the filters you implement for the comparisons.
• Natural Language Processing: NLP or Natural Language Processing is a technology that is focused on the analysis of text-based information. With this technology of Data Science, we can develop intelligent bots that may answer the queries of the users.
• Self-driving cars: In developed countries like the USA, you may find self-driving cars which are making safer driving environments for the drivers. It also optimizes vehicle performance and adds great anatomy to the drivers.

Prepare yourself for the upcoming Data Scientist interview with our blog on Data Science interview questions and answers.

Career Tracks with Data Science

Although there are many career tracks associated with Data Science. But here, we are going to discuss two major career tracks which you can opt for your Data Science career. Get to know why and how to make a career in Data Science through this blog on Data Science Career Path. But before choosing your career in Data Science, ask yourself these questions-

• How do I know which machine learning model will work "best" with my dataset?
• How do I interpret the results of my model?
• How do I evaluate whether my model will generalize to future data?
• How do I select which features should be included in my model?

See the image below to know about Data Science Career Path by experience level-

Data Science Career with R

• R is data analysis software: Data scientists, statisticians, and analysts, anyone who wants to make sense of data, can use R for statistical analysis, data visualization, and predictive modeling.
• R is a programming language: It is an object-oriented language created by statisticians. R provides objects, operators, and functions that allow users to explore, model, and visualize data.
• R is an environment for statistical analysis: Standard statistical methods are easy to implement in R, and since much of the cutting-edge research in statistics and predictive modeling is done in R, newly developed techniques are often available in R first.
• R is an open-source software project: R is free and, thanks to years of scrutiny and tinkering by users and developers. It has a high standard of quality and numerical accuracy. R is an open interface that allows it to integrate with other applications and systems.
• R is a community: The R project leadership has grown to include more than 20 leading statisticians and computer scientists from around the world, and thousands of contributors have created add-on packages. With two million users, R boasts a vibrant online community.

The main objective of “R for data science” is that it helps you to learn the most important tools in R that will permit you to do data science.  Here is the R Programming Data Science Tutorial for Beginners.

Data Science Career with Python

You can opt for several career options after choosing Python as a career track in Data Science. There are frameworks you can learn, which may help you in the advancement of your career using Python for Data Science. Some of the career paths are given below.

• Django for Web Development
• Pygame for Game Development
• Hadoop for Big Data
• Selenium for Web Testing

Python for data science has now emerged as the preferred language to be used by many data scientists around the world. Checkout this blog to know how to use Python in Data Science.

Various Job Roles for Data Science Experts

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

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

To take the career opportunity in Data Science, one must keep on updating his skills, and it is quite clear from the above statistics that the person having more skills will have more chances to get higher salaries. Here is the average Data Scientist salary across the globe-

Job Responsibilities of Data Scientist

Today, almost all organizations and businesses of all sizes and kinds, are spending large amounts of time collecting data. But, what exactly does a Data Scientist do?

• Selecting features, building and optimizing classifiers using machine learning techniques
• Data mining using state-of-the-art methods
• Extending the company’s data with third-party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Creating automated anomaly detection systems and constant tracking of its performance.

Skills and Qualifications for Data Scientists

Data scientists are expected to possess some of the basic skills for data scientists are deep thinking, intellectual curiosity, ability to discover new concepts, etc. Explore more about the skills of a Data Scientist from this blog.

• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Experience with common Data Science toolkits, such as R, Weka, NumPy, MatLab, etc. (depending on specific project requirements). Excellence in at least one of these is highly desirable
• Great communication skills
• Experience with data visualization tools, such as D3.js, GGplot, etc.
• Proficiency in using query languages such as SQL, Hive, Pig
• Experience with NoSQL databases, such as MongoDB, Cassandra, HBase (depending on project requirements)
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Good scripting and programming skills
• Data-oriented personality

Explore the trending questions in data science by joining our Data Science community and forum. 

Final Words:

So, here we come to the final section of our blog, and it’s very clear that Data Science can provide you with the most promising career options today. It is not that difficult to learn Data Science, and any pre-existing skill can definitely help you.

Python and R are the 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, and Naïve Bayes are a few of the popular algorithms used in Data Science frequently, and practical knowledge can always stand you ahead of the crowd. If you wish to venture into the world of data science, be prepared to face the challenges. We can simplify things by guiding you through our online Data Science Certification Course and Training Program that aims to make you ready for your next big move!

FAQs

Q1. Is there any difference between data science and data analytics?

Ans- Data science focuses on broader insights into data sets. Data Analytics is a part of data science that answers specific questions brought by data science. Data science brings innovations to businesses in unique ways, while data analytics provides solutions to these questions.

Q2. What are the responsibilities of a data scientist?

Ans- A data scientist is mainly involved in gathering, and analyzing data, using various types of tools to analyze data, and detect trends and reports. He is also responsible for building models to address business problems and develop statistical reports on them.

Q3. Can I become a data scientist after class 12?

Ans- In order to become a data scientist, you need to possess a bachelor’s degree in computer science or software engineering with a sound knowledge of mathematics. However, you can become a data scientist after your class 12th by doing professional courses on Data Science which are available at our JanBask training.

Q4. What will I be learning in this course?

Ans- In the Data Scientist course, you will learn R programming, Python, machine learning, deep learning, regression analysis, data architecture, visualization techniques, risk analysis, process improvement, systems engineering, and many more concepts that are important for the Data Scientists certifications exam and to be an industry-ready professional.

Q5. Which data certifications are in demand?

Ans- Here are the few in-demand Data Science Certifications-

• i) Apache Hadoop certifications
• ii)Certified Health Data Analyst
• iii) Data Science EMC Proven Professional
• iv) IBM Cognos Business Intelligence certifications
• v) Microsoft Certified Solutions Associate
• vi) Microsoft Certified Solutions Expert

Q6. What are the steps for qualitative data?
Ans- Here are some steps for qualitative data-

i) Collection of data
ii) Analyze your data
iii) Organize the data
iv) Categorize the data
v) Validate the data
vi) Conclude the data analysis

Q7. Is qualitative data better than quantitative data?
Ans- It is not possible to say which is better. Rather, they work together to produce the best data results. Understand the importance of both types of data and utilize them accordingly for the most productive outcomes.

Q8. Which are the fields where Quantitative data are used?
Ans- Quantitative data is used in many fields of study like psychology, economics, demography, marketing, sociology, political science and human development. They are less commonly used in fields like history and anthropology.

Q9. Why are Data science courses necessary?
Ans- Data Science certifications are important to have as they improve & validate your skills for the real-time job market, give an edge in your resume/portfolio, increase your probability of getting hired over non-certified professionals, help during salary discussions and brins great confidence while commencing any job or taking up any project.

Q10. What can I expect after this course?
Ans- After completing our Data Science Certification training, you will end up having competent skills & knowledge. It imparts intellectual ways to penetrate in the in-demand job roles. A Data Scientist Training Certificate by a highly acclaimed name in the e-learning world “JanBask Training” - marking as proof of the successful completion of your learning-filled training journey. Our training completion certification will give you great recognition during the hiring process.

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