What is Data Science: A Comprehensive Guide for Beginners

Data Science has taken the world by storm. Everything in today’s cloud-based business world moves fast, producing, and compiling data at a rapid rate. The possibilities of managing and utilizing data operations are endless with new applications of Data Science being discovered every day and every minute —creating jobs across industries and a multitude of sectors and giving rise to new career paths. So, knowing what is data science is imperative if you are considering a career in this field.

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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 ML 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.

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.

Field	Skills	Tools
Data Analysis	R, Python, Statistics	SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
Data Warehousing	ETL, SQL, Hadoop,  Apache Spark,	Informatica/ Talend, AWS Redshift
Data Visualization	R, Python libraries	Jupyter, Tableau, Cognos, RAW
Machine Learning	Python, Algebra, ML Algorithms, Statistics	Spark MLib, Mahout, Azure ML studio

Let us understand what data scientist does in the next section of 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 what is data science clearly.

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 on 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

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

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.

Business Intelligence	Data Science
Uses structured data	Uses both structured and unstructured data
Analytical in nature - provides a historical report of the data	Scientific in nature - perform an in-depth statistical analysis on the data
Use of basic statistics with emphasis on visualization (dashboards, reports)	Leverages more sophisticated statistical and predictive analysis and machine learning (ML)
Compares historical data to current data to identify trends	Combines historical and current data to predict future performance and outcomes

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.

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. 

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.

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

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. 

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.

Operationalize

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

Career Options for a Data Scientist

The demand for data scientists is massive, but the supply is insufficient. With millions of worldwide job openings, the role of a data scientist has become one of the hottest jobs of the decade. While data science is present in all industries, the demand for data science is exceptionally high in the technology, marketing, finance, healthcare, and gaming industries. Apart from knowing what is data science, you can understand the career options available in this field by checking out our article on How to build a career in data science and consider enrolling for the Data Science Certification Training Course.

Do you find data science a fascinating career field? Want to become part of the data revolution, sweeping across industries worldwide? Check out Data Scientist Master’s Program co-developed with IBM.