What is Data Science: Definition, Lifecycle, Applications, Prerequisites

What is Data Science?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can be from multiple sources and present in various formats.

Now that you know what is data science, let’s see why data science is essential in the current scenario.

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Why Data Science?

Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:

• Find the leading cause of a problem by asking the right questions
• Perform exploratory study on the data
• Model the data using various algorithms 
• Communicate and visualize the results via graphs, dashboards, etc.

In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. With the help of data science, airlines can optimize operations in many ways, including:

• Plan routes and decide whether to schedule direct or connecting flights
• Build predictive analytics models to forecast flight delays
• Offer personalized promotional offers based on customers booking patterns 
• Decide which class of planes to purchase for better overall performance

In another example, let’s say you want to buy new furniture for your office. When looking online for the best option and deal, you should answer some critical questions before making your decision.

Using this sample decision tree, you can narrow down your selection to a few websites and, ultimately, make a more informed final decision.

Prerequisites for Data Science

Here are some of the technical concepts you should know about before starting to learn what is data science.

1. Machine Learning

Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics. 

2. Modeling

Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics

Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

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4. Programming

Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Databases

A capable data scientist, you need to understand how databases work, how to manage them, and how to extract data from them.

Data Science Skills

This section of ‘What is Data Science?’ article gives you an idea of the skills and tools used by people in different fields of data science.

Let us understand what a data scientist does in the next section of the What is Data Science article.

What Does a Data Scientist Do?

A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

• Ask the right questions to understand the problem
• Gather data from multiple sources—enterprise data, public data, etc
• Process raw data and convert it into a format suitable for analysis
• Feed the data into the analytic system—ML algorithm or a statistical model
• Prepare the results and insights to share with the appropriate stakeholders

Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.

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Must-Know Machine Learning Algorithms

The most basic and essential ML algorithms a data scientist use include:

1. Regression

Regression is an ML algorithm based on supervised learning techniques. The output of regression is a real or continuous value. For example, predicting the temperature of a room.

2. Clustering

Clustering is an ML algorithm based on unsupervised learning techniques. It works on a set of unlabeled data points and groups each data point into a cluster.

3. Decision Tree

A decision tree refers to a supervised learning method used primarily for classification. The algorithm classifies the various inputs according to a specific parameter. The most significant advantage of a decision tree is that it is easy to understand, and it clearly shows the reason for its classification.

4. Support Vector Machines

The Support vector machines (SVMs) is also a supervised learning method used primarily for classification. SVMs can perform both linear and non-linear classifications.

5. Naive Bayes

The Naive Bayes is a statistical probability-based classification method best used for binary and multi-class classification problems.

People who are willing to know what is data science should also be aware of how data science differs from business intelligence.

Difference Between Business Intelligence and Data Science

Business intelligence is a combination of the strategies and technologies used for the analysis of business data/information. Like data science, it can provide historical, current, and predictive views of business operations. However, there are some key differences.

The Lifecycle of a Data Science Project

To give further clarity on what is data science, here is a detailed description of the stages involved in the lifecycle of a data science project.

1. Concept Study

The first phase of a data science project is the concept study. The goal of this step is to understand the problem by performing a study of the business model.

For example, let’s say you are trying to predict the price of a 1.35-carat diamond. In this case, you need to understand the terminology used in the industry and the business problem, and then collect enough relevant data about the industry. 

2. Data Preparation

Since raw data may not be usable, data preparation is the most crucial aspect of the data science lifecycle. A data scientist must first examine the data to identify any gaps or data that do not add any value. During this process, you must go through several steps, including:

• Data Integration

  Resolve any conflicts in the dataset and eliminate redundancies

• Data Transformation

  Normalize, transform and aggregate data using ETL (extract, transform, load) methods

• Data Reduction

  Using various strategies, reduce the size of data without impacting the quality or outcome

• Data Cleaning

  Correct inconsistent data by filling out missing values and smoothing out noisy data

Model planning is the next phase to be discussed in What is Data Science article.

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3. Model Planning

After you have cleaned up the data, you must choose a suitable model. The model you want must match the nature of the problem—is it a regression problem, or a classification one? This step also involves an Exploratory Data Analysis (EDA) to provide a more in-depth analysis of the data and understand the relationship between the variables. Some techniques used for EDA are histograms, box plots, trend analysis, etc. 

Using these techniques, we can quickly discover that the relationship between a carat and the price of a diamond is linear. 

Then, split the information into training and testing data—training data to train the model, and testing data to validate the model. If the testing is not accurate, you will need to retrain the model of the processor uses another model. If it is valid, you can put it into production.

The various tools used for model planning are:

• R

  R can be used both for regular statistical analysis or mission learning analysis, including visualization for more detailed analysis

• Python

  Python offers a rich library for performing data analysis and machine learning

• Matlab

  Matlab is a popular tool and one of the easiest to learn

• SAS

  SAS is a powerful proprietary tool that has all the components required to perform a complete statistical analysis

4. Model Building

The next step in the lifecycle is to build the model. Using various analytical tools and techniques, you can manipulate the data with the goal of ‘discovering’ useful information. 

In this case, we want to predict the price of a 1.35-carat diamond. Using the pricing data we have, we can plug it into a linear regression model to predict the price of a 1.35-carat diamond.

Linear regression describes the relation between 2 variables - X and Y. After the regression line is drawn, we can predict a Y value for an input X value using the formula:  

Y = mX + c

where,

m = Slope of the line

c  = y-intercept

If you can validate that the model is working correctly, then you can go to the next level—production. If not, you need to retrain the model with more data or use a newer model or algorithm, and then repeat the process. You can quickly build models using Python packages from libraries like Pandas, Matplotlib, or NumPy.

After model building, the next phase to focus on in the What is Data Science article is communication. 

5. Communication

The next step is to get the key findings of the study and convey those to the stakeholders. A good scientist should be able to communicate his findings to a business-minded audience, including details about the steps taken to solve the problem.

6. Operationalize

Once all parties accept the findings, they get initiated. In this phase, the stakeholders also get the final reports, code, and technical documents.

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Applications of Data Science

Data science has found its applications in almost every industry.

1. Healthcare

Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.

2. Gaming

Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.

3. Image Recognition

Identifying patterns in images and detecting objects in an image is one of the most popular data science applications.

4. Recommendation Systems

Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.

5. Logistics

Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.

6. Fraud Detection

Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.   

Data Science as a Career

Over the last five years, the job vacancies for data science and its related roles have grown significantly. Glassdoor has named data scientist as the number one job in the United States as per its 2019 report. The U.S. Bureau of Labor Statistics predicts the rise of data science needs will create 11.5 million jobs by 2026.

There are several job roles that you can look for in the data science domain. 

Some of the important job roles are:

1. Data Scientist
2. Machine Learning Engineer
3. Data Consultant
4. Data Analyst

According to Glassdoor, the average salary of a data scientist in the United States is $113,000 per annum and in India, it’s 907,000 Rupees per annum.

If you want to grow your career in data science and become a data scientist, here is a useful certification course that you could enroll for. This Post Graduate program in Data Science is in collaboration with Purdue University and IBM. 

Check out the infographic below to summarize your understanding of what data science is -

Conclusion

Data is the oil for companies in the coming decade. By incorporating data science techniques into their business, companies can now forecast future growth and analyze if there are any upcoming threats. Now, it’s the right time for you to start your career in data science, if you’re interested. 

Do you have any questions regarding this ‘What is Data Science’ article? If so, then please put it in the comments section of the article. Our team will help you solve your queries at the earliest.