- What is Data Science?
- Data Science definition
- Why Businesses Need Data Science
- Applications of Data Science
- Business Intelligence Vs. Data Science
- Data Science vs Data Analytics
- Why You Should Build a Career in Data Science?
- Who is a Data Scientist?
- What Are The Essential Skills to Become a Data Scientist?
- Data Science Salary Trends Across Job Roles
- Who Can Become a Data Scientist/ Analyst/ Engineer?
- Top Tools in Data Science Domain
What is Data Science?
Data Science continues to be a hot topic among skilled professionals and organizations that are focusing on collecting data and drawing meaningful insights out of it to aid business growth. A lot of data is an asset to any organization, but only if it is processed efficiently. The need for storage grew multifold when we entered the age of big data. Until 2010, the major focus was towards building a state of the art infrastructure to store this valuable data, that would then be accessed and processed to draw business insights. With frameworks like Hadoop that have taken care of the storage part, the focus has now shifted towards processing this data. Let us see what is data science, and how it fits into the current state of big data and businesses.
Data Science Definition
Broadly, Data Science can be defined as the study of data, where it comes from, what it represents, and the ways by which it can be transformed into valuable inputs and resources to create business and IT strategies.
Why businesses need Data Science?
We have come a long way from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from various sources. The traditional Business Intelligence tools fall short when it comes to processing this massive pool of unstructured data. Hence, Data Science comes with more advanced tools to work on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files.
Mentioned below are relevant use-cases which are also the reasons behind Data Science becoming popular among organizations:
- Data Science has myriad applications in predictive analytics. In the specific case of weather forecasting, data is collected from satellites, radars, ships, and aircraft to build models that can forecast weather and also predict impending natural calamities with great precision. This helps in taking appropriate measures at the right time and avoid maximum possible damage.
- Product recommendations have never been this precise with the traditional models drawing insights out of browsing history, purchase history, and basic demographic factors. With data science, vast volumes and variety of data can train models better and more effectively to show more precise recommendations.
- Data Science also aids in effective decision making. Self-driving or intelligent cars are a classic example. An intelligent vehicle collects data in real-time from its surroundings through different sensors like radars, cameras, and lasers to create a visual (map) of their surroundings. Based on this data and advanced Machine Learning algorithm, it takes crucial driving decisions like turning, stopping, speeding, etc.
Data Science Applications
Business Intelligence Vs Data Science
Business Intelligence focuses on historical data to draw insights regarding business trends. It enables you to extract data from internal and external sources, and prepare it to run queries. You can find logical answers to specific business problems by applying BI. It can also evaluate the impact of certain events in the near future.
On the other hand, Data Science is an exploratory approach to analyse the current and past data to predict future outcomes. The aim is to make informed business decisions by answering open-ended questions to ‘what’ and ‘how’.
Some of the major differences are:
|Business Intelligence (BI)||Data Science|
|Data sources are structured (Usually SQL, often Data Warehouse)||Utilise both structured and unstructured data sources ( logs, cloud data, SQL, NoSQL, text)|
|Follows the statistics and visualization approach||Approaches the problems with statistics, machine learning, graph analysis, neuro-linguistic programming (NLP)|
|Focuses on past and present data||Focuses on present and future|
|Tools used – Pentaho, Microsoft BI, QlikView, R||Tools used – RapidMiner, BigML, Weka, R|
Data Science vs Data Analytics
To comprehend the amount of data that is being generated in today’s world, making use of proper tools to do so has become extremely important. Data Science and Data Analytics are integral elements of Business Intelligence and Big Data Tools. The terms are, however, largely interchanged. They can be confusing to differentiate and despite being interconnected, both tools follow a different approach. Here’s how they are different from one another.
|Data Science||Data Analytics|
|Scope – Macro||Scope – Micro|
|Data Science aims to find and define new business problems that lead to innovation.||Here, the problem is known.The analyst tries to find the best solutions to the problem.|
|The input is usually raw or unstructured data. This is cleaned and organized to be sent for analytics.||The input is structured data. Design principles and data visualization techniques are applied on this.|
|Used for recommender systems, internet research, image recognition, speech recognition, and digital marketing.||Used in domain areas such as healthcare, travel and tourism, gaming, finance and so on.|
Why you should build a career in Data Science?
Now that we have seen why businesses need data science in the above section, let’s see why is data science a lucrative career option through this video:
A data scientist identifies important questions, collects relevant data from various sources, stores and organizes data, decipher useful information, and finally translates it into business solutions and communicate the findings to affect the business positively.
Apart from building complex quantitative algorithms and synthesizing a large volume of information, the data scientists are also experienced in communication and leadership skills, which are necessary to drive measurable and tangible results to various business stakeholders.
If you would ask ‘what are the top five qualities of a good data scientist’? Then the answer is:
- Statistical Thinking
- Technical Acumen
- Multi-modal communication skills
- Curious mind
If you want to master all of the Statistical Skills related to Data Science, you can go through the below video
What are the essential skills to become a Data Scientist?
Data Science is a field of study which is a confluence of mathematical expertise, strong business acumen, and technology skills. These build the foundation of Data Science and require an in-depth understanding of concepts under each domain.
These are the skills you need if you want to become a Data Scientist
- Mathematical Expertise: There is a misconception that Data Analysis is all about statistics. There is no doubt that both classical statistics and Bayesian statistics are very crucial to Data Science, but other concepts are also crucial such as quantitative techniques and specifically linear algebra, which is the support system for many inferential techniques and machine learning algorithms.
- Strong Business Acumen: Data Scientists are the source of deriving useful information that is critical to the business, and are also responsible for sharing this knowledge with the concerned teams and individuals to be applied in business solutions. They are critically positioned to contribute to the business strategy as they have the exposure to data like no one else. Hence, data scientists should have a strong business acumen to be able to fulfil their responsibilities.
- Technology Skills: Data Scientists are required to work with complex algorithms and sophisticated tools. They are also expected to code and prototype quick solutions using one or a set of languages from SQL, Python, R, and SAS, and sometimes Java, Scala, Julia and others. Data Scientists should also be able to navigate their way through technical challenges that might arise and avoid any bottlenecks or roadblocks that might occur due to lack of technical soundness.
Other roles in the field of Data Science:
So far, we have understood what is data science, why businesses need data science, who is a data scientist, and what are the critical skill sets that are required to enter the field of data science. Now, let us look at some other data science job roles apart from that of a data scientist:
- Data Analyst: This role serves as a bridge between business analysts and data scientists. They work on specific questions and find results by organizing and analyzing the given data. They translate technical analysis to action items and communicate these results to concerned stakeholders. Along with programming and mathematical skills, they also require data wrangling and data visualization skills.
- Data Engineer: The role of a data engineer is to manage large amounts of rapidly changing data. They manage data pipelines and infrastructure to transform and transfer data to respective data scientists to work on. They majorly work with Java, Scala, MongoDB, Cassandra DB, and Apache Hadoop.
Data Science Salary trends across job roles
Who can become a Data Scientist/ Analyst/ Engineer?
Data Science is a multidisciplinary subject and it is a big misconception that one needs to have a Ph.D. in science or mathematics to become a data science professional. Although a good academic background is a plus when it comes to data science profession, it is certainly not an eligibility criterion. Anyone with a basic educational background and an intellectual curiosity towards the subject matter can become a data scientist.
Top tools in Data Science Domain
- SAS – It is specifically designed for operations and is a closed source proprietary software used majorly by large organizations to analyze data. It uses the base SAS programming language which is generally used for performing statistical modelling. It also offers various statistical libraries and tools that are used by data scientists for data modelling and organising.
- Apache Spark – This tool is an improved alternative of Hadoop and functions 100 times faster than MapReduce. Spark is designed specifically to manage batch processing and stream processing. Several Machine Learning APIs in Spark help data scientists to make accurate and powerful predictions with given data. It is a highly superior tool than other big-data platforms as it can process real-time data, unlike other analytical tools which are only able to process batches of historical data.
- BigML – BigML provides a standardized software using cloud computing, and a fully interactable GUI environment that could be used for processing ML algorithms across various departments of the organization. It is easy to use and allows interactive data visualizations. It also facilitates the export of visual charts to mobile or IoT devices. BigML also comes with various automation methods that aid the tuning of hyperparameter models and help in automating the workflow of reusable scripts.
- MATLAB – It is a numerical computing environment that can process complex mathematical operations. It has a powerful graphics library to create great visualizations that help aid image and signal processing applications. It is a popular tool among data scientists as it can help with multiple problems ranging from data cleaning and analysis to much advanced deep learning problems. It can be easily integrated with enterprise applications and other embedded systems.
- Tableau – It is a Data Visualization software that helps in creating interactive visualizations with its powerful graphics. It is suited best for the industries working on business intelligence projects. Tableau can easily interface with spreadsheets, databases, and OLAP (Online Analytical Processing) cubes. It sees a great application in visualizing geographical data.
- Matplotlib – Matplotlib is developed for Python and is a plotting and visualization library used for generating graphs with the analyzed data. It is a powerful tool to plot complex graphs by putting together some simple lines of code. The most widely used module of the many matplotlib modules is the Pyplot. It is an open-source module that has a MATLAB-like interface and is a good alternative to MATLAB’s graphics modules. NASA’s data visualizations of Phoenix Spacecraft’s landing were illustrated using Matplotlib.
- NLTK – It is a collection of libraries in Python called Natural Language Processing Toolkit. It helps in building the statistical models that along with several algorithms can help machines understand human language.
- Scikit-learn – It is a tool that makes complex ML algorithm simpler to use. A variety of Machine Learning features such as data pre-processing, regression, classification, clustering, etc. are supported by Scikit-learn making it easy to use complex ML algorithms.
- TensorFlow – TensorFlow is again used for Machine Learning, but more advanced algorithms such as deep learning. Due to the high processing ability of TensorFlow, it finds a variety of applications in image classification, speech recognition, drug discovery, etc.
If you have a work experience of less than 3 years, do check out Great Learning’s Postgraduate program in Data Science and Engineering. Candidates from the course are able to transition to roles such as business analysts, data analysts, data engineer, analytics engineer etc. by
learning relevant data science techniques, tools and technologies and hands-on application through industry case studies.
For professionals with work experience of more than 3 years, we do have another program –Postgraduate program in Data Science and Business Analytics by Great Learning in collaboration with The McCombs School of Business at The University of Texas at Austin and Great Lakes, India. It is a comprehensive Data Science and Business Analytics Course that covers the latest analytics tools and techniques along with their business applications.5