Introduction to Data Science
In this article, I am going to give a brief introduction to Data Science. Data science is all about understanding the data and using that data to solve complex business problems. Its main goal is to find out the hidden pattern from the raw data. For achieving this goal data scientists use various tools, machine learning principles, and algorithms. This in turn allows organizations to manage costs, boost their market, and increase efficiency. At the end of this article, you will understand the following pointers.
- Need for Data Science
- Main Components of Data Science
- What is Business Intelligence
- Benefits of implementing Business intelligence
- What is Data Analysis, Data Mining, and Machine Learning
- Value Chain
- Data Analytics and Types of Analytics
- Data Analytics Project Lifecycle
Need for Data Science
From the last few years, the demand for Data Science is increasing day by day and also increases its importance in various industries. Below are some of the reasons which show why data science is important:
- Using various Big data tools help organizations resolve complex problems related to IT, Healthcare, and resource management.
- Due to the popularity of Data Science, the requirement of data scientists has also increased as they have to perform various tasks on handling data and deliver output for the problems.
- Clients are essentials to make a product successful or fail. Data Science helps organizations to connect with clients, understand them, and their requirements.
Why Data Science? or Why is this job so popular?
Whenever we hear the term Data Scientists, we always believe how fascinating this job is. Probably so many people are already chasing these jobs. So, the question is – Why is this job so popular? Why the hype now?
The answer lies in the name itself – Data.
We all know about the exponential growth in the amount of data being generated by humans. And many business giants are using this data to run and grow their business. Companies are shifting from their manual approach to a data-driven approach to succeed in markets. This is where Data Scientists help.
When data is in the right hands, it can help predict and transform the future. Data Scientists help companies to make data-driven decisions on a massive scale so that those decisions can impact the growth of their businesses.
Whether it is business, health industry, marketing, or research, all of them require data to give them momentum to move in the right direction and help in getting valuable insights for the growth. This is why there is a need to develop a robust system that can handle multiple industry issues and provide a solution to them. This is the reason behind the high demand for data scientists.
Foundation of Data Science
Data Science is a field that includes scientific progress and algorithms to extract conclusions and knowledge from structured and unstructured data. Data Science has relation with the domains of data mining, machine learning and big data. Working in Data Science requires knowledge of concepts like Statistics, Probability and Data Analysis as well.
Data Science is currently helping businesses to break down their big data into useful information and insights that can help in solving domain-specific issues and grow in terms of revenue as well. The amount of data being recorded has been increasing tremendously in previous years, this has led to growth in demand for data scientists.
Most of the data that is produced is mismanaged, messy and disorganized making it really tough for anyone to draw any conclusions. This is where Data Scientists come in.
But then the question arises, what all things a data scientist can do? Well, a data scientist is a person who will take up a dataset, explore it, create a use case for that data and then will perform different experiments and analysis for how that data can be brought in use for providing solutions which can later help in early diagnosis of possible problems. And this complete pipeline of events is the reason why there is a great demand for this skill in the current scenario where Data is considered as the new gold.
The actual power of a Data Scientist lies in good knowledge of Statistics, probability, and algorithms (deep mathematical intuition), and also problem-solving skills. It’s more about actually using all these skills in conjunction in a disciplined manner. From here onwards we will try to cover everything one by one and get a deep understanding of how everything works.
Main components of Data Science
The main components of Data Science are as follows:
- Data Exploration: In this first important step, data is collected from various sources. This is a time-consuming step. Mostly this data is in raw format which is unstructured. It has a lot of noise (unwanted data) in it. This step involves sampling and transformation of data in rows and columns and removing the unwanted data by using statistical methods. This step also includes finding the relationship among various columns if available. In this way, data is transformed for further use.
- Data Modelling: This step involves using machine learning algorithms to fit the data into the model. Modelling is done according to the requirement of the business. Accordingly, the model is decided and data gets fitted into the required model.
- Model Testing: In this step, the model created will be tested. Test data is used to test the model’s accuracy and other characteristics to get the desired result. If the desired result is not obtained, step 2 (Data modelling) is followed and testing is repeated.
- Model deployment: Once we get the desired result as per business requirement, the tested model is then deployed in the production environment.
What is Business Intelligence?
Business Intelligence refers to a set of techniques used for making decisions on organizational data. Business Intelligence provides the past, present, and future views of business operations. It provides the right information to the decision-makers at the right time as per the requirements.
Business Intelligence requires large amounts of data for proper analysis and predictions including – data related to business, transaction, customer, sales, and other regularly generated data. The main goal of business intelligence is to fast-track the decision-making process based on facts and these facts are derived from data.
Business Intelligence helps businesses in understanding the past and in the implementation of post – mortem analysis of a failed product or a customer complaint. So, it helps in evaluating the current situation to avoid future failures.
The primary component of business intelligence is to identify correct data from different sources and then analyze current scenarios through that data to avoid ambiguous situations and failure of products.
In another major component of BI, the data is consolidated, integrated, and analyzed, the output is to be visualized as relevant information for a better understanding of patterns resulting from such integrated data. This is achieved using various reporting mechanisms, alerts, and other useful data.
Business Intelligence can be used virtually at all levels and all the time. It can be used extensively at the top management level in making strategic decisions. Business Intelligence has almost become an integral part of the business ensuring business survival. It analyses historical data and forecasts the future keeping the business ahead of its competitors.
Benefits of implementing business intelligence
Following are some of the benefits of implementing business intelligence in the organization:
- Gain a comprehensive view of their organization’s data and translate it into insights that help improve business processes and strategic business decisions.
- Helps organization to analyze historical data from which organization will optimize operations, track the organization performance, identify and eliminate the business problems easily.
- Align the organization’s activity strategically which will improve the performance of the organization.
What is Data Analysis?
Data Analysis is a process to explore useful insights by data cleaning, transforming and modelling for making major decisions for the business. For example, before final exams, we explore previous year papers, analyse topics that have come already, and based on those insights, we make decisions for which topics need to be focused on more. In today’s world, where every business is collecting data, they can accelerate their growth by performing regular analysis on that data.
A data analyst can perform descriptive statistical analysis, visualize it and connect data points together to obtain conclusions for decision making. Data Analysis is considered to be a necessary level of data science.
Key skills of a data analyst include –
- Programming languages – R and Python
- Data Wrangling
- Understanding of Data Warehouse tool – HIVE
So, it is a process of collecting, cleaning, transforming, and modelling data that extract insights that support decision making for the organization. The main purpose of Data analysis is to extract useful information from raw data and make the decision based on the data analysis.
The perfect example of this is our day-to-day life, where we make decisions based on past decisions. We think about the causes and prons happened in the past and accordingly, take our decision for the future. We analyse the past mistakes we made which should not be repeated in the future. The same applies to the organization decisions, where we analyse the past data and strategies the future data for the benefit of the organization.
What is Data Mining?
Data Mining is a subset of data analysis. It includes extracting usable data from raw data. It helps in recognizing the trends and patterns, not visible manually, concluded from a large dataset. In data mining, we segment the data and evaluate the probability of occurrence of future events by using mathematical algorithms and thus discover hidden facts that the data is conveying.
The most common examples where data mining proves to be most helpful for businesses are customized content for a user, predicting ad campaigns that can influence large audiences for product success, and optimizing money spent on advertisements. Data mining is also used in cases of predicting employee behaviour, employee attrition and evaluating possible fraud.
Data Mining can help businesses plan their policies accordingly so that such possible cases of fraud and failure. Unlike data analysis, it doesn’t include visualization tools.
Data mining is the part of data science where we perform tasks on a particular data set. The data mining is broken down into five steps:
- Organizations collect raw data and load them to a data warehouse.
- Store and manage data either on in-house servers or on the cloud.
- The organization’s IT professionals like Business analysts, management personnel access the raw data and organize it.
- Sorting of data is being performed by the software application.
- Refined data is presented in an easy-to-use format like a graph, charts etc.
Machine learning is an automated process that learns from the data, identifies the patterns and makes decisions without any human interventions. In this process, machines learn or observe the data at first sight and also look for the patterns generated in the data and conclude with better decision making.
Machine Learning is a domain where mathematical algorithms are used to extract data, get trained on it and then make future predictions and analyze trends for the given problem.
The most common example of how Machine learning works is YouTube. They analyze the data for the videos you have shown interest in, like or disliked, added in any of your playlists and then next time, you can see related videos in the recommended feed. This leads to an increase in your screen time and benefits their business.
Key skills for Machine Learning include –
- Programming fundamentals
- Probability and skills
- Data evaluation and modelling
Process of Machine Learning
Data scientists play a crucial role in creating machine learning applications by closely working with business professionals to understand the model created by them. Following are the four basic steps for building machine learning applications:
- Training data is being prepared at the first stage. This training data is in the form of a data set that is being ingested by machine learning models.
- The next step is to choose the algorithm to be run on the training data.
- Training the algorithm in an iterative manner to produce an accurate result. This process compares each output with the desired result.
- In the final step, the created model is being used with new data created through an iterative process.
Analytics vs Data Science
It is a little confusing to understand the exact difference between data analytics and data science since these two are interconnected. But the main thing that differentiates the two is – the approach and the final outcome.
|Data Analytics||Data Science|
|It includes analysis of existing datasets. It helps you to create methods to organize and process data to get insights from it.||It includes finding actionable insights from data so that a particular problem can be solved. It is more specific.|
|It helps you find the data on which we can perform an action (a right chunk of data).||It helps you ask specific questions and find conclusions for them from the data.|
|Involves usually small dataset and scope is also limited||It can be helpful in dealing with large datasets as well.|
|Deals with structured data only||Deals with both structured and unstructured data.|
|Major skillset –
Major skillset –
Value Chain Analysis
Value chain analysis is used to visually analyze the progress of the organization. This tool is used to create the greatest possible value for the customers. It represents how a firm can transform inputs into valuable output. It’s a strategic tool used to analyze the internal activities of the firm. Its main goal is to recognize which activities are the most valuable to the firm and which can be improved to provide more output from less utilization of resources.
Value Chain can be defined as a complete chain of process of creation of a product or a service. It includes beginning from the initial reception of materials to the final delivery of the product in the market.
In the value chain, a business divides the whole process into primary and secondary activities. And then they analyze the outcome of each activity and the scope of their improvement in terms of time, money and effort required.
The application of value chain concepts by businesses on their use case is known as Value Chain Analysis. Let’s now see what exactly these primary and secondary activities are –
Primary Activities –
They are responsible for the physical creation of the product, sale, support and maintenance. If these primary activities are well managed, then the company can save up money and create a product at a lower cost than the other alternative companies.
Primary activities include –
- Inbound Operations
- Outbound logistics
- Marketing and sales
Secondary Activities –
They are responsible for supporting the primary activities.
They include –
- Technology development
- Human Resources management
- Company Infrastructure and Quality Assurance
- Value Chain Analysis can help analyze and take major business-related decisions accordingly
- Analyze dependencies of different domains
- Optimize the whole process of product development by maximizing the output and minimizing the cost and expenses
- Analyze the scope of improvement
Data analytics is the process where we analyze the raw data to find out the hidden patterns and answer the multiple questions of the organization. Successful data analytics can predict a clear picture of where the organization stands, where it should be, and where it should go in future. It helps business to boost their performance. Implementing this technique into the business will help reduce their costs by identifying the efficient way of doing business.
Types of Data Analytics
Majorly there are four different types of analytics, varying on the basis of complexity and the value that they have.
- Descriptive Analysis
- Diagnostic Analysis
- Predictive Analysis
- Prescriptive Analysis
It provides a summary of what has happened over a period of time. It summarizes a large number of data sets to describe outcomes to the stakeholders. Key Performance Indicator (KPI) is used to track the progress of the organization. Metrics such as Return of Investment (ROI) are being tracked to measure the performance. In this process data is being collected, then processed, then analyzed and insights are displayed using various methods like graphs, charts, etc.
- It is usually used for finding the answer to the question – what happened
- It is simplest in terms of complexity
- This analysis identifies that something is right or wrong, but doesn’t provide a proper explanation of why it is so.
- For large scale companies, descriptive analysis is not at all preferred, as it won’t be of any use
This approach identifies why things happened. Most of the findings are being taken from descriptive analytics and then dig deeper to find out the causes.
- It is more complex than the descriptive analysis
- Along with what happened, it also answers the question of why it happened
- It gives a detailed insight into a particular issue
- The diagnostic analysis is a good choice, but there should be enough specific data accumulated already so the time is not wasted in collecting the data for a specific issue
This type of analytics is related to the prediction of what is going to happen in the future. By analyzing the past data future prediction is performed. This technique includes various statistical data, decision trees, and regression.
- It combines the insights of descriptive and diagnostic analysis, to answer the question – what is likely to happen in future.
- This is, therefore, more complex than the previous two
- It identifies clusters and ambiguous trends for predicting future occurrences
- It brings highly valuable insights.
- This is just a forecast of future events, but the accuracy of these findings totally depends on the data quality
This technique is used to find out what should be done. Some findings should be taken from predictive analytics and data-driven decisions should be made from the findings. This technique mostly depends upon machine learning strategies.
- It basically helps in finding the answer to the question of what action to take, so a future issue or failure can be avoided.
- It makes use of advanced technologies like – machine learning and business knowledge
- It is highly complex, and the decision of performing the prescriptive analysis should be taken with utmost care and after all the considerations
Data Analytics Project Lifecycle
To resume a data analytics project, the lifecycle of data analytics is very much important. It should be based on the six key steps. A project life cycle is the process of implementation of a project related to any domain. Data Analysis Lifecycle consists of six major steps –
- Understanding of the business problem
- Understanding of data
- Preparation of data
- Exploratory Data Analysis and Modelling
- Visualization and final conclusion presentation
Understanding Business: Examine the scope of work with respect to business and the objectives to be attained and gather required information for expectations. This phase outlines the objectives of the business. Before starting any data analysis project, one should examine the scope of work, the objective of the work, types of analysis required from the given data, type of information required by the stakeholder after analysis. Understanding the requirement will help you to work smoothly and it will not affect the deliverables.
Understanding Data: Collect initial data, identify data requirements, and process it for further analysis. Data acquired from the source can be unstructured or structured. There is a need of arranging the data into data sets or categorize according to the need of the organization. For these various types of tools are used. For a small data set you can use excel which will fulfil your requirement but for larger data experts suggests using tools like R, Python, Tableau. These tools are capable of cleansing the data, looking for errors in the data.
Data Preparation: Gather data from multiple resources, clean and format it so that it can be used further for problem solution. This step will organize the data into the dataset. You can also perform cleaning of data and input the variable wherever required into the dataset. You should also check for the duplication of data.
Data Analysis and Modelling: Analyse the data and determine important variables from that data. Build and assess the model. In this step, models are prepared to test the data to seek the objective. If any model failed to get the required output it should follow the preparation stage one more time. These steps are performed many numbers of times to get quality data.
Data Validation: Evaluate results and on the basis of those results and determine upcoming steps. This is a testing stage where the prepared models and data are being tested for final deliverables. Does it also check whether the models that are created are working properly? Does data require more cleaning? This is mostly a trial-and-error method with various perspectives.
Visualization and Presentation: Communicate the results through insights and determine the best method to present the results to the audience. This is the final stage where the data is visualized in various forms. Data visualization should be in the desired format which is easily understandable to the client. Sometimes clients are not tech-savvy to understand the tableau tool or any other tools. So, it should be visualized in a simple way where layman users can also understand the data.
In the next article, I am going to give you a brief introduction to data. Here, in this article, I try to give you an overview of Data Science and I hope you enjoy this Introduction to Data Science article.
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.