Data Science Tutorial

With the rapid changes in the developmental world, we see a drastic change in the technologies and advancements in the 21st century. Organizations deal with a large amount of data, and there would be an exponential increase in the volume of the data. From every corner of the world, data is generated every second. Data is the ultimate solution that helps in understanding, analyzing the business growth, allowing to take precise decisions for the betterment of the Organization. Data science is the field that is most demanding these days. In this tutorial, you will gain an understanding of what is data science, its advantages, applications, life cycle along with its components, job roles, and opportunities, etc.

What is data science?

Data science is referred to as a study of the large volumes of data, which allows you to gain insights from the structured, unstructured, and raw data by utilizing different technologies, scientific methods, and algorithms. Data science is a field of tools and techniques that helps in the manipulation of the data, allowing you to determine something different, new, and meaningful.

Data science is derived from the evolution of data analysis, statistics, and big data. Data science is the demanding field that helps in translating the business problem into a research project and translate it back to a solution. In simple terms, it refers to the study of large volumes of the data, from where the data has come, what it represents, and the different ways that can be used for the transformation into valuable inputs and help in creating new business strategies.

Data science is one of the hot topics among the different organizations and skilled professionals who are looking for the best career. The primary focus is on collecting the data and also draws meaningful insights that will help in the business development along with exponential growth quality.

We all know that data is the asset for an organization and needs to be processed efficiently and effectively. Data Science is also known as data-driven science that is used for extracting the knowledge from different resources represented in various forms and gaining an insight into the business.

Data Science is all about the following.
1. Probing with the right questions and performing the analysis of the raw data.
2. Modelling of Greater using different algorithms which are complex and efficient.
3. Visualization of the data to get a better perspective.
4. Understanding of the data to make precise decisions.

Let us take a simple example that is used in real life.

Let us say; you want to travel from place A to please B. In order to travel soon, you will need to make some decisions like which route should we choose as the best route to reach faster to the location, choosing the route in which there is no traffic jam and also cost-effectiveness. Based on the above factors treating them as the input data, you will need to analyze and decide the best route, which is a part of data science.

Importance of Data Science:

In today's world, we run around the data, and there is a vast increase in the volumes of the data. In the traditional Times, data was less and is stored in excel sheets, etc. But in today's living, we see that the data volume has been increased in bytes per day cleaning the data explosion. Handling and management of the data have become a tedious task for every OrganizationOrganization. In order to handle, analyze, process the data using complex, efficient algorithms and technologies, data science came up into existence.

The importance of data science has been increased tremendously due to the following reasons.

1. Data science provides the capability of building intelligence ability in systems or machines.
2. Data science makes use of the algorithms, technologies, tools, and techniques that will help in providing a distinct business advantage.
3. Data science provides the flexibility to make precise decisions in a better and faster way.
4. Data science provides extensive support in detecting fraud by utilizing the advanced algorithms of machine learning.
5. Data science is also paving a way to provide the right product's recommendation to the right customer to improve the business in all terms.

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Different job roles in Data Science:

Among all the job roles in data science, a data scientist is the job role that is most demanding in the current as well as the future era. As there is a high demand for data science, there are many opportunities that will be rolling out in the coming days. The scope of career options in data science is found to be high as per the research conducted by the experts. By 2026, it is expected that 11.5 million jobs will be based on Data Science, and hence, the growth in Data Science career seems to be high in the near future.

You need to gain an understanding of the different job roles available in the field of Data Science. Let me give you a brief description of each job role to help you choose the right one that suits your capabilities and interests.

1. Data Analyst:

A data analyst is responsible for performing operations like mining large volumes of data, modelling the data, identifying and determining the different trends and patterns, relationships, etc. A data analyst is involved in data visualization and data reporting, which helps in making precise decisions with problem-solving.
A data analyst must have knowledge of data mining, mathematics, statistics, business intelligence. It would be best if you had a brief understanding of some computer tools and languages like Python, SQL, Excel, SAS, R, JS, spark, hive, MATLAB, etc.

2. Machine learning expert:

The machine learning expert is responsible for working with the various machine learning algorithms that are used in data science. Some of them are clustering, classification, regression, random forest, decision tree, etc.

A machine learning expert should have knowledge on the different computer programming languages like Java, Hadoop, Python, C++, R. Apart from this; he also needs to have an understanding about the problem solving, analytical skills, algorithms, probability and statistics.

3. Data engineer:

Data engineer is responsible for working with a large amount of data, and also is involved in maintaining and building the data architecture of the data science project. A data engineer is involved in working with the creation of data set processes that are used in mining, modelling, acquisition, and verification.

A Data engineer must have an in-depth knowledge of Cassandra, Apache Spark, Hive, HBase, SQL, MongoDB, etc. along with the programming knowledge of Java, C/C++, Perl, Python, etc.

4. Data scientist:

A data scientist is referred to as a professional who is responsible for working with the enormous amount of data, bringing up insights of the business by deploying different tools, algorithms, techniques, strategies, and methodologies.

To develop a career as a data scientist, an individual should have knowledge about technical languages like SQL, Pig, Hive, Python, MATLAB, Apache Spark, SAS, R, etc. The data scientist should have an understanding of Mathematics, Visualization, Statistics, along with communication skills.

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Components of data science:

The most important components of data science are explained below.

1. Statistics:

Statistics is one of the essential components and critical components of data science. Statistics involve the ways to collect and analyze numerical data which is in large volumes and gather and provide meaningful insights accordingly.

2. Domain Expertise:

Domain expertise helps in binding the data science together. Domain expertise refers to the specialized skills or knowledge in a specific area. There are many areas in which we need domain experts, and hence there are high opportunities for individuals.

3. Data engineering:

Data engineering is another component of data science that involves various tasks or operations like storing, acquiring, retrieving, and transforming the data. Data engineering holds metadata (metadata here refers to data to data).

4. Visualization:

Data Visualization refers to the way of representing the data in a visual format, providing a user-friendly experience, allowing the users to easily understand the data. Data Visualization helps in providing easy access to the huge volumes of data.

5. Advanced computing:

Advanced computing refers to the heavy lifting of the data. Advanced computing involves different tasks or operations like writing, designing, debugging, and maintaining the code of computer programs.

6. Mathematics:

Mathematics is one of the important components of data science. Mathematics includes the study of different aspects like structure, quantity, changes, and space. Great knowledge in mathematics is a required skill for a data scientist.

7. Machine Learning:

Machine Learning is often referred to as the backbone of data science. Machine learning involves the training of a machine so that it would act as a human brain. However, in data science, machine learning algorithms solve the problems.

Life cycle of Data Science:

There are different phases in the life cycle of data science. Let me give you a brief explanation of each phase in the data science life cycle.

1. Discovery phase:

The discovery is the first phase in the data science life cycle, referring to probing for asking the questions. When you start working on a data science project, you will need to identify the basic requirements, budget, priorities, etc. Apart from these, you also need to determine the different requirements of the project like the time, data, goal and objective, technologies, etc.
In the discovery phase, the data is required from all the internal and external sources that help in resolving or answering the business query. The data can be logged from the web servers, gathered from social media, steam from different online sources, etc.

2. Data preparation:

The second place in the data science life cycle is data preparation. The data preparation phase is also called data munging. Data preparation includes different tasks like data cleaning, data reduction, data integration, and data transformation. Once the tasks are performed, you can use the data for further processing. As we deal with a large amount of data, the data can contain lots of inconsistencies like incorrect data format, blank columns, missing values, etc. To rectify these errors, you will need to process, explore, and condition the data before performing the modelling.

3. Model planning:

In the model planning phase, it is your responsibility to determine different techniques and methods used to establish the relationship between the input variables. We will be applying the exploration of Data Analytics making use of different formulas and visualization tools, helping in providing an understanding of the relationships between the variables, and also see what data can inform us. Some of the common tools that are used in the model planning phase are R, Python, SAS, SQL Analysis Services.

4. Model building:

In the model building phase, the process of a model building begins in this phase. The creation of the data sets takes place for training and testing purposes. In order to build the model, a model building requires Different techniques like classification, clustering, association, etc. Some of the model-building tools are WEKA, MATLAB, SPCS Modeler, SAS Enterprise Miner, etc.

5. Operationalize:

The fifth stage in the data science life cycle is the operationalize stage, which involves delivering the final report of the project. The reports include project briefings, coding, technical documents, etc. The operational life stage helps in providing an extensive view of the complete product performance and also the other components on a small scale before the complete deployment takes place.

6. Communicate results:

This is the last phase in the life cycle of data science. In the communicate results phase, we will be checking if the final goal has been reached or not, that we have set up in the initial phase. This page also includes communication of the findings and the final result that are obtained with business teams.
Tools used in data science:

As said earlier, data science is the field that deals with the algorithms, tools, techniques, and methodologies; it is essential for us to know the tools used in data science. A data scientist works on multiple tools when he is dealing with the project. However, there are some set of tools that are used in every data science project. The tools in data science are divided into four different categories and are listed below.
1. Data storage
2. Exploratory data analysis
3. Data modelling
4. Data visualization

1. Data storage:

In the data storage category, the tools help in storing the large amount of data. A few of the data storage tools are Hadoop, Microsoft HD insights, apache-spark, etc.

2. Exploratory data analysis:

Exploratory data analysis (EDA) is one of the approaches that help in performing the analysis of large volumes of unstructured data. Few of the EDA tools are SAS, information, MATLAB, Python, etc.

3. Data modelling:

Data science includes data modelling tools that have inbuilt machine learning algorithms. All you need to do is pass the processed data to Train Your model accordingly. Data modelling tools are BigML, TensorFlow, Scikit Learn, Data robot, H20.ai, etc.

4. Data visualization:

After performing all the different stages, we need to visualize the data to find out the inside and hidden patterns from it to deliver the reports properly.

Let me give you a brief explanation on how each tool is used in Data Science.

SAS:

SAS is one of the data science tools, a closed source proprietary software, which is specifically designed for operations. SAS is used by large organizations to perform the analysis of the data. SAS tool uses SAS programming languages that helps in performing the statistical modelling.

Apache Spark:

Apache Spark is an improvised tool of Hadoop and is capable of functioning 100 faster when compared with MapReduce. Apache Spark is designed for the management of batch and stream processing. Machine learning in Apache Spark allows the data scientist to provide the flexibility to make perfect predictions with the given data. Apache Spark is one of the highly used, superior among all the tools.

Tableau:

Tableau is a data visualization software which helps in creating interactive visualizations by utilizing graphics. Tableau has a capability to interface with databases, OLAP (online analytical processing) cubes, etc. It is one of the tools that is best suited for the organizations which work on business intelligence projects.

MATLAB:

Matlab is referred to as a numerical computing environment that involves the processing of complex mathematical operations. It also maintains a graphics library that helps in creating the visualizations. MATLAB tool is one of the popular tool among the data scientists, as it provides its extensive support in clearing the multiple problems from data cleaning to advanced algorithms. MATLAB tool is compatible and easy to integrate with embedded systems and enterprise applications.

Difference between Data Science and business intelligence:

Both data science and business intelligence are used to perform the data analysis of their large volumes of data of the OrganizationOrganization. There are some differences between and Data Science which are listed below.

1. Methodology:

Business intelligence: In business intelligence, it is purely based on analytical or historical data.
Data Science: The methodology in data science is a scientific methodology, where it goes deeper to find out the reason for the data report.

2. Primary focus:

Business intelligence: The primary focus of business is based on present and past data.
Data science: The primary focus is on the present, past, and future as well.

3. Skillset:

Business intelligence: To develop a successful career in business intelligence, visualization and statistics are the two important skills that are required in an individual.
Data science: To develop a successful career in data science, visualization, machine learning, and statistics are the important skills required in an individual.

4. Data sources:

Business intelligence: Business intelligence deals with large volumes of data that is structured in format. An example is a data warehouse.
Data science: Data Science is the field that deals with both structured and unstructured data. Feedback weblogs are some of the examples.

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Data science applications:

Nowadays, we see a lot of changes in the current level of technology and development. There are many real-time applications that we come across in our day to day life. Few of the data science applications are listed below.

1. Gaming world:

Every individual is now involved in gaming. We are living in the world of games, hence there is an increase in the use of machine learning algorithms on a daily basis. Some of the widely used games using data science are Sony, Nintendo, eA Sports, etc.

2. Image recognition and speech recognition:

Data science is currently used for image recognition in speech recognition. Let us take an example of a Social Media application like Facebook, which helps in uploading the image on Facebook and you will start getting suggestions to tag your friends. These automatic suggestions of tagging your friends utilize the image recognition algorithm which is from data science. And also we keep saying ok Google, ok Siri, etc e and page on our voice control the devices will be responding. To get this done, a speech recognition algorithm is used which is a part of data science.

3. Healthcare:

Data science is providing its extensive support in the Healthcare field as well but providing a lot of benefits. As health care deals with different aspects like tumor detection, virtual medical bots, medical image analysis, drug Discovery, etc, data sense is also a part of the Healthcare sector.

4. Transportation:

Transportation or the transport industries are also utilizing database Technology to create self-driving cars. With the usage of self-driving cars, the number of accidents will be minimized.

5. Internet search:

In this world, surfing or searching on the internet for some information has become common. When you search for a particular thing on the internet, using different search engines like yahoo, google, Bing, Ask, etc, the search experience will be better as they use data science in it. And also the result will be reflecting within a fraction of seconds.

6. Recommendation systems:

Most multinational companies like Netflix, Amazon, Google Play, etc are now using data science technology for providing a better User experience with personalized recommendations. Whenever you search for something on the Amazon website or application, you also receive suggestions for similar products which is also a part of data science.

7. Risk detection:

Finance industries come across many risks when they run the business. Finance industries hold a lot of losses and fraud issues, which can be resolved through data science. Every finance organization is now looking for a data scientist who can put up the skills in reducing the risk and loss with an increase in customer satisfaction.

Advantages of Data Science:

In the current world as of 2020, there is an increase in the level of Data Science in the current industries and technological side. Let us know about the benefits of using Data Science.

High Demand:

There is a high demand for Data Science in the current era of living. Every individual is now focusing and trying to create and develop a career as a data scientist as data scientists are most needed in the market because everything is dealt with the large volumes of data. Data science is a promising career in the future as it is providing many job opportunities for individuals with the right skills. As every organization deals with large volumes of data, every OrganizationOrganization will also need a data scientist to predict the future of the business.

Customized User experience:

Comprises utilizes machine learning which has enabled all the industries to create new products or better products that are based on customer experiences. The customer experience or User experience is one of the most important aspects for the success of the business. Data science provides product recommendations to users in eCommerce websites, by considering their personal insights to the users based on the previous purchases or searches history.

Improvement in the Healthcare field:

There has been a lot of improvements in the Healthcare field since the emergence of data science. With the use of machine learning in data science, it has made it easier to detect the early-stage tumors. Most of the healthcare industries are utilizing data science to enhance the client experience.

Conclusion:

Data Science is a booming technology which is leading to changes in the development and technologies in the world based on its functionalities. To develop a promising career in data science, it is essential for you to learn and have a brief knowledge about programming, technical skills. It is time for you to upskill yourself to take an advantage of the upcoming job opportunities in data science. I would recommend you to get trained and certified in Data Science, which would help you in building the progress of your career in the right path. I hope the above information has given you an understanding about Data Science and its concepts. To be more precise, start learning and deep diving more on data science which would bring your hands up high in the sky with flying colors and achieve your goal.

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.