Beginner’s Guide to Understanding Data Science

Data science is a multi-dimensional field of science that is used for the analysis of multiplex and complex data. It uses empirical scientific approach, algorithms, and techniques to extricate insights from composite and non-composite data. It is the same as data processing and data mining, but data science uses the most advanced techniques to make the best use of data.

Data science is not a single field or domain, but it is a realm that combines related concepts from statistics, mathematics, and machine learning, and implements them for data analysis.

This is a beginner’s guide to understanding data science.

What Does Data Science Do?

Data science gives a broader and comprehensive dimension of the methods. It has increased productivity and efficiency with its reach in almost every field and industry.

Data science has shifted the paradigm and the essence of science by instigating and expediting change in concepts, research methods, standards, methodologies, and patterns. It uses scientific methods to evaluate the unstructured and complex data and discovers the patterns, behaviors, and trends in it.

Data science understands the business requirement and its problem and uses a data-based algorithm to find a solution. (Blei and Smyth, 2017)

How Does Data Science Help Us?

Artificial intelligence and machine learning are changing the world by giving insights and solutions to complex situations. Data is the element that is underpinning all revolutionary technologies. Data is the need for every organization to be run. From health, aviation, transportation, production, education, sales/marketing, data is required by all to operate, plan, and execute their framework to deliver their services and products to the customers.

But before that, the data is collected through various means and then sorted, processed, and filtered to target the needs and problems of the particular industry. Data science gives you information that is relevant, which is analyzed, interpreted, used to generate reports, organized, and digitalized for easy access and manipulation and predictions. This processed data gives them insights and scenarios to foresee market trends, preferences, and solutions.

Data science uses the most advanced techniques, strategies, and technologies to analyze data to get answers to complex questions, understand consumer behavior, and make efficient business decisions. Data science gathers the skills and concepts of mathematics, statistics, artificial intelligence, and business intelligence.

Data science can help reduce costs, give us perception and understanding of horizons of starting a new service or product, help us get through new markets and also tell us how effective a marketing campaign can be, and what audience can be targeted with the help of data regarding demographic. (Grabowski and Rappsilber, 2019).

Life Cycle of Data Science

Since we now know the purpose of data science and how it works, we will understand the implementation of data science by having a look at its life cycle. The data science lifecycle comprises of five stages.

1. Business Understanding

Decisions in business are made based on data and information derived from them. To provide solutions to business problems, understanding the business is a very crucial part of the lifecycle. We should understand the power of data because the conclusions or solutions will be based on them.

2. Data Mining

The next step is to understand what type of data is needed. Selecting the right data from the right sources is the right way to do the thing. After the acquisition of data, data is cleaned for errors. This step consumes most of the time.

3. Modeling

Data modeling is the analysis process. It has further sub-processes that provide the structure for the project. In this process, the data is transformed into information and represents the business problems.

4. Deployment

After the successful implementation of the models, the deployment process starts, and the insights are created with the help of applications and forecast, and insights are provided.

5. Customer Acceptance

The final stage for the deployment of the model is giving results according to the customer’s needs and satisfying their business requirements.

Road Map to Data Science

The career path or road map towards learning data science requires versatile knowledge of subjects such as mathematics, statistics, and artificial intelligence, along with skills of different software and learning programming languages. To master data science and penetrate the field, knowledge of the following subjects is necessary.

1. Artificial Intelligence

Artificial intelligence (AI) or machine intelligence is a field of computer science. It stimulates the procedures and actions of human intelligence, which is processed by computer systems and specially designed machines.

These machines are often referred to as “intelligent agents” in which the device is capable of understanding its environment and performing certain actions that increase the probabilities of achievement of goals.

Artificial intelligence is a tool that is used in the field of data science. It is incorporated into applications, systems, and methodologies for extracting:

2. Statistics

Statistics is itself a science that deals with the collection of data and organizing it, exhibition, analysis, interpretation or evaluation, and finally, the presentation of data.

Statistics is a very powerful and useful tool that is used in data science for analysis of the data in a technically and focused way, which is driven by the information.

It gives us fine-tuned and deep insights into the data structures, which can be optimized through the techniques of data science to get more informative data. Data is analyzed through various methods of predictions, probability, and statistics to gain useful information and make decisions accordingly.

The concepts of statistics widely used in data science are the analysis of quantitative data, probability theory and distribution, regression analysis, hypothesis testing, and sampling.

3. Mathematics

Mathematics has no proper definition. It is the science of calculation, measuring, and logical reasoning. It is the most important subject in learning data science.

The subjects of mathematics widely used to understand data science and its implementation in the field are linear algebra and calculus. Linear algebra is also used in deep learning, also known as machine learning.

It is used to understand the operations of algorithms, which is a set of rules used by computers to solve problems and do calculations. Similarly, calculus is also used in machine learning to code the functions which derive the algorithms to fulfill their tasks.

Languages to Know

Once you grasp the knowledge of all the subjects and their techniques, models, methodologies, the next step is the implementation of all these sets of values, methodologies, and scientific procedures through some means. That medium is the programming language. The following languages are used in data sciences.

1. Python

Python is a very powerful and general-purpose programming language that applies techniques of statistics and is widely used in data science for data analysis.

It has simple, efficient, and precise syntaxes. It has abundant packages for scientific calculations such as data analysis and visualization etc. It has different learning modules starting from beginners to advance level.

2. SQL

SQL is used for basic processes like storage of data, data extraction, data parsing, data manipulation, extracting, and management. SQL means Structured Query Language, which accesses and updates the data with the help of queries.

3. R

R is a programming language similar to python, which is used for statistical computation and graphical representation. It is also used commonly for data mining and data analysis and used in the development of software used for statistical calculations.

A Career in Data Science

The professionals in data science are called as data scientists, data analysts, and data engineers. All work in the same field but have more or less different job descriptions and tasks. The following will provide a brief guide to the desired career path.

1. Data Analysts

A data analyst is considered to be an entry-level position in the field of data science and is also known as the junior scientist. The job of a data analyst is to collect the data, process it, and perform analysis on it.

The data can be presented in a descriptive form to make the customer understand and decide. A data analyst uses computer applications to extract data from different sources. (Hey, 2009)

The useful information is then extracted, and irrelevant information is discarded. The data is analyzed with the help of various methodologies, and results are interpreted.

Market patterns and trends are highlighted, and new ways are proposed for the improvement. A data analyst is also responsible for creating databases and maintaining systems and databases and fixing issues related to data and coding.

Skills and knowledge required

  • Programming in Python, R, SQL
  • Probability and statistics
  • Data visualization and data cleaning

2. Data Scientists

Data scientists also do the same things as that of data analysts, but they have advanced learning and the capability of building machine models that make spot-on future predictions that are based on the previous data.

A data scientist has the liberty to implement his schemes and designs to find desired patterns in the data that are interesting and innovative. They can be asked to evaluate a change in the overall market strategy that might work more efficiently for the company.

This has risks and requires a vast data analysis or done simply with the help of past data by giving predictions relying on it.

Skills required:

  • All Skills of a data analyst
  • Supervised and non-supervised machine learning methods
  • An understanding of statistics and statistical, computational model evaluation
  • Advanced-Data science programming skills in Python or R
  • Knowledge of Apache Spark, SQL Stream or Splunk

3. Data Engineers

Data engineers are the ones who deal with “Big Data.” They construct, experiment, and provide support databases and systems. This job is more inclined toward software engineering and programming and less toward statistical analysis.

A data engineer is responsible for managing the data infrastructure of the company. The data engineer is required to move the data from one system to another, which is called the data pipeline.

This is done to acquire the latest data regarding marketing, sales, and revenue generation and pass it on to the data analysts in a readable form easy to be used by them.

The process of passing data is done with the help of building a completely flawless infrastructure which also stores and access data. The job requires due diligence and responsibility due to its complex operations.

Skills and knowledge required:

The skills requirements of the data engineers are more concentrated and targeted towards software developments, so a company hiring a data scientist may look for the following set of skills.

  • Advanced and strong programming skills (especially in Python) to deal with huge datasets and pipelines
  • Advanced SQL skills and knowledge of Postgres


Data science is emerging as a lucrative and most demanding industry in the field of computer science and requires experts specialized in these fields to handle the most critical and complex problems of the business industries. As technology will progress further, we will see more advancements in this field of study.


  1. Blei, D.M., & Smyth, P. (2017, August 15). Science and Data Science. Proc Natl Acad Sci U S A, 114(33), 8689-8692.
  2. Grabowski, P., & Rappsilber, J. (2019, January). A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?. Trends Biochem Sci, 44(1), 21-32.
  3. Helleputte, T., Gruson, D., & Rousseau P. (2016, July). Data science, Artificial Intelligence, and Machine Learning: Opportunities for Laboratory Medicine and the Value of Positive Regulation. Clin Biochem, 69, 1-7.
  4. Hey, T. (2009, October). The Fourth Paradigm: Data-Intensive Scientific Discovery. International Symposium on Information Management in a Changing World. 317, 1-1.
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