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Data Science Summarized In One Picture

Last updated:
5th Jul, 2018
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Data Science Summarized In One Picture

Lately, the term ‘Data Science’ has been on the rave. Everywhere we look, there’s something that points us towards Data Science. Why is it so? The answer is quite simple – our world is rapidly transforming into a data-driven field where technological innovations, business processes, business decisions are all being defined by data. In fact, 90% of the world’s data has been generated in the past two years. Every day, nearly 2.5 quintillion bytes of data is generated on a global scale. So, how exactly are we making sense of this enormous amount of data?
Well, it is all because of Data Science.

What is Data Science?

Data science is a multidisciplinary study that combines data inference with advanced algorithms, scientific processes, and technology with an aim to extract meaningful information hidden within both structured and unstructured data. It is multidisciplinary in the sense that it involves the concepts, tools, and expertise in the field of Mathematics, Statistics, Computer Science, and Information Science.

How to Make a Bright Career in Data

Essentially Data Science is all about unravelling the hidden trends, patterns, and insights from within data. Once data professionals (data scientists, data analysts, statisticians) discover these valuable insights, business analysts incorporate the information within the organization’s infrastructure to enhance the decision-making process, boost sales and revenue, enhance employee productivity, and improve customer satisfaction. Data Science also includes the process of developing of the ‘data product.’ A data product refers to the technical asset that leverages data to produce algorithm-oriented solutions. Personalized recommendation lists are the most excellent examples of a data product. For instance, Amazon dives into consumer data to curate ‘personalized’ shopping suggestions for individual customers based on their browsing history and previous purchases.

Now let’s break down Data Science into the five stages as shown in the picture above:

Data Quality

When dealing with massive data sets, first the data needs to be assessed to determine its reliability, fitness, and efficiency to serve a particular purpose according to the context of a problem that needs to be addressed. Data is examined from various perspectives to calculate its accuracy and relevance. In the context of organizational and business processes, it is crucial that the data is reliable so that it can promote healthy business decisions and solutions.

Descriptive Statistical Analysis

Descriptive statistical analysis is the process of describing, presenting, and organizing a particular data set by providing precise summaries about the data sample through graphs, tables, or numerical calculations. The three most common types of descriptive statistics are mean, median, and mode. Descriptive statistical analysis is primarily used to transform complex quantitative information into bite-sized descriptions for the ease of understanding.
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Data Diagnosis

Once the relevance of the data is established and is broken down into smaller fragments, it is necessary to conduct a data diagnosis to examine and review an organization’s data infrastructure. The aim here is to identify issues within the data structure and create an effective strategy to fix the problems while simultaneously chalking out the possible improvements that can be incorporated into the data system. Since the entire data infrastructure has to be reviewed, multivariate data analysis is the ideal method. Multivariate data analysis denotes a statistical technique of analyzing data arising from more than a single variable.

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Predictive Analytics

Predictive analytics refers to the practice of extracting valuable insights from existing data sets to predict possible outcomes in the future. It leverages data mining and machine learning techniques, and statistical algorithms on historical data to determine the probability of future results. By forecasting future possibilities, predictive analytics allows businesses to better understand their products, the market, and consumer trends, and also to identify potential risks and fresh opportunities for expanding their reach in the market.

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Semantic Analysis

Data scientists and analysts have to analyze vast quantities of both structured and unstructured data such as emails, texts, blog posts, social media posts, tweets and much more. The difficulty with unstructured data is one has no preconceived idea to figure out how the data elements are related to each other. This is where semantic analysis comes in. It facilitates the clustering of various data elements according to their similarity quotient instead of traditional classification techniques (positive, negative and neutral). It is all about teaching the machines how to ‘learn.’ Semantic analysis not only provides relevant clues to the meanings of different words but also hint at their relationship with one another. This can be highly beneficial for businesses as it can unravel information regarding how consumers are interacting with their products/services, how are the products/services creating value for the consumers, what is their preferences and taste patterns, and so on.

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So, that’s how Data Science works!


Abhinav Rai

Blog Author
Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.

Frequently Asked Questions (FAQs)

1What are the different fields of expertise in Data Science?

Data Science mainly covers six topics that require expertise

1. Statistics : Statistics refers to the study and manipulation of data. It includes collection, organisation, analysis, interpretation and presentation of data. In Data Science it can be used for Experimental Design, Frequent Statistics and Modeling.
2. Linear Algebra : According to Wikipedia Linear Algebra is the branch of mathematics concerning vector spaces and linear mapping between such spaces. Nowadays, Linear Algebra can be used in Data Science prominently for Machine learning, Modeling, Optimization, Programming, Database, Collaboration.
3. Machine Learning: Machine Learning refers to a group of techniques used by data scientists to analyse big data in an automated process. It is gaining a lot of prominence and recognition in Data Science today. Machine Learning can be further divided into two sub types – Supervised Learning and Unsupervised Learning.
4. Data Mining : Data Mining is a process of exploring and analysing big volumes of data to glean meaningful patterns and trends to find hidden value that helps companies in solving problems, reducing risks and taking advantage of new opportunities. It includes Data Wrangling, Data Munging, Data Cleaning and Data Scraping.
5. Data Visualization: Data visualisation is the graphical depiction of large amounts of data and information using visual components such as charts and graphs. Some common types of Data Visualizations are: (a) Multidimensional – pie charts, histograms and scatter plots (b) Time driven - Time series, Gantt charts and arc diagrams.

2In which different fields Data Science applications can be used?

1. Fraud and Risk Detection - especially for banks
2. Healthcare – for Medical Image Analysis, Genetics and Genomics, drug development, etc
3. Internet Search
4. Targeted Advertisement
5. Website Recommendations
6. Image Recognition
7. Speech Recognition
8. Airline Route planning
9. Gaming
10. Augmented Reality

3What are the career opportunities in Data Science?

Data Science is one of the most in demand skill jobs for 21st century. It offers big opportunities like

1. High salary
2. Lowers the risk of job automation
3. Find solutions to complex problems like – increasing sales, distinguishing a target audience segment, building infrastructure to centralize all the data for an organisation.

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