This blog intends to enlighten you about the following concepts:
- What is Data Science and Why It is Important?
- Benefits of Data Science for Businesses
- How Data Science Helps?
- Data Science Life Cycle and its stages
- Data Science Venn Diagram
- Data Science Vs Computer Science
- Data Science vs Software Engineering
- What are Maths and Statistics Required for in Data Science?
- Why Domain Knowledge is Important for Data Science?
- 5 Job Roles in Data Science
- Data Scientist
- Data Analyst
- Business Analyst
- Database Administrator
- How to Learn Data Science?
- Is It Compulsory to Pursue Post-Graduation to Get into Data Science?
What is Data Science and Why It is Important?
Data Science definition in simple words:
“Data Science is a multi-disciplinary domain which is made of three different fields of studies, i.e., Computer Science, Mathematics & Statistics, and Domain Expertise”.
In other words, Data Science is the study of raw information/data in bulk, to understand what it represents and how it can be effectively turned into valuable and actionable insights, which help business owners to make various strategic decisions.
Here below find what a Data Scientist has to say:
“I am fascinated about Data Science as it gives the pleasure of unraveling mysteries. Makes you feel a scientist, a detective, domain expert, deal complex problem with facts and no gas!”– Sandhaya Kumari (Data Scientist).
Quite often than not, you open Facebook, and, after every few scrolls you observe a Sponsored Ads column of various products. These are of those products that either you might have been checking out online or something that matches your taste or preference based on your previous social media engagement. But how did Facebook know that?
Well! Since the inception of Big Data and Data Science Technologies, analyzing and predicting visitors’ taste and preferences is no more a mystery. Facebook’s targeted advertisement is so powerful that it can reach you based on Location, Demographics, Behavior, Interests, and Connections too.
CNBC reports that, Facebook made $40 billion in advertising revenue last year, second only to Google.
Data Science has numerous benefits in terms of Business and Career. In the modern market landscape, ‘customers are kings’, and to meet their expectations, companies have to go an extra mile and look beyond just offering the minimal requirement.
That is when Data Science plays a vital role, and a company-wide deployment of Data Science techniques is only going to let the organizations stand out from the rivals. Let’s dive deep into the reasons why businesses need Data Science.
Benefits of Data Science for Businesses
‘Data Science’ is a buzzword that has been the talk of the tech-town over this decade. But what makes Data Science so compelling that more and more companies are inclining towards this emerging technology?
McDonald’s spends heavily on Data Analytics and Data Science to compete with Burger King considering their usual rivalry with the latter always remaining second to the former. However, Burger King also leveraged Data Science and Data Analytics to first enhance the customer experience through various digital mediums which caused a huge increase in the footfall.
Data Science is taking healthcare industry to a whole new level by offering complicated services in lesser cost, whether it is Medical Image Analysis, Genetics & Genomics, Drug Discovery, Virtual Assistance, and many more areas.
Walmart uses Data Science technologies to manage the order/shipment preparations, transportation, routing, managing inventory levels, etc., all of which are seamlessly handled with the help of heuristics offered by Machine Learning Algorithms.
Read this exciting blog on How AI helps fight against Coronavirus!
How Data Science Helps?
Following are some the prominent ways Data Science is helping businesses unlock methods of achieving success:
- Predicting Customer Preferences: Data Science comes with predictive analytics techniques that allow companies to track customers’ preferences and helps offer them customized services. This not only traces new potential customers; it also helps them retain the existing ones at a lesser cost.
- Competitive Edge: While every other company is racing to reach the top and acquire larger market share, Data Science has emerged as the superhero helping them channelize their resources in a way that can help them develop a competitive edge over the rivals.
- Targeted Advertisement: With data coming in from multiple sources at an unprecedented and almost uncontrollable speed, it becomes harder for the firms to track all the data and analyze them with conventional technologies. The ability of Data Science technologies to deal with heterogeneous and asymmetrical data, makes it easier for companies to maintain the analytical environment and do targeted advertisement to various niche segments.
- Supply Chain: Data Science has been proved to be one of the most ground-breaking technologies in terms of managing the supply chains. Predicting the demand and managing inventory levels on that basis, assessing the picking and delivery services, determining the shortest delivery routes, and managing all the middlemen services in a cost-effective way are some of the prominent solutions derived with the Data Science Techniques.
Nevertheless, these are not the only benefits, as Data Science Techniques and Algorithms are creating new avenues for businesses to formulate strategies and capitalize on them.
But how are Data Science projects carried-out? What are the stages that Data Scientists move through to solve a business problem? Let’s dig deeper in the following section!
Data Science Life Cycle
Data Science is not a phenomenon, but a process that goes through several stages to complete a project. These stages are collectively called as ‘Data Science Life Cycle’. Data Science Life Cycle can be considered as a standard workflow for any Data Science project carried-out to keep various teams in coordination.
Every project has some goals to accomplish, where Data Science Life Cycle proceeds from one stage to another keeping the final outcome in mind.
Data Science Life Cycle consists of following crucial stages:
Stage 1: Understanding Business Problem
Key Objectives: The key objective of this stage is to specify the variables whose metrics are going to decide the success of the project. Another important objective to be followed in this stage is to identify the data sources.
“Correctly understanding the real-life problem and mapping it into an ultimate solution is one of the key aspects in Data Science.”
At this stage, Data Scientists coordinate with clients, various teams and the stakeholders to understand the underlying business problem that is attempted to be solved by using Data Science techniques and methods.
In order to understand the business problem entirely, Data Scientists need to question the following:
- What are the dependent and independent variables? (Regression)
- Which observation should belong to which category? (Classification)
- Which group the observation should fall into? (Clustering)
- Are there any outliers? (Anomaly Detection)
- What can be the recommendations for future practice? (Recommendation)
The above questions are answered by identifying the right data sources. As soon as these questions are answered, the team is assigned with roles, and responsibilities, and the goals, and metrics are determined for all the members.
Stage 2: Data Acquisition
Key Objectives: The key objectives of this stage are to cleaning, and preparing the dataset to be analyzed.
Data Acquisition can be defined as collecting data from various sources, internal or external, so that the Data Science analysis can be carried out. Before Big Data was born, companies only had the access to the organizational data to conduct the analysis. However, with the advent of modern technologies and digital disruption, obtaining outside data is a matter of few clicks.
It is very much important to scrutinize the ways in which the data is collected. After all, data is the very basic need for any data science technique or algorithm to work.
- Are all the data being recorded? If yes, then how?
- How reliable are the data?
Data is acquired from various sources which can cause lots of noise in them. The source can either be Corporate Databases, Web APIs, Relational or Non–Relational Databases, Kaggle, or a combination of all these.
Stage 3: Data Preparation
Key Objectives: As soon as the data is gathered, Cleaning and transforming the Data is the next step to perform, and creating a Data Pipeline so that data can be ingested into the analytics environment.
At this stage, the team of Data Scientists audits the data through summarization and visualization, post which a Predictive Model is developed to understand the inherent patterns in the data.
After the patterns are discovered, a Data Pipeline is created to keep the analytic system updated with fresh data. A pipeline can be created as a batch system processing, a streaming or real-time system, or a hybrid system.
Since all of the data may not be useful, the unwanted bulk of data is removed. Now that one has all of the data that are required, checking whether all are in machine-understandable format is the next thing. If not, then the Data Scientist needs to convert the data to the formats. At times, this step takes the longest time to finish as data are gathered from multiple sources.
Also, a data scientist has to check if all the values, variables, and information are in place if not, then correcting them is also a part of scrubbing the data.
The Data Science team delivers a Data Quality Report which describes the relation between various attributes, variable ranking, etc. The description of Data Pipeline is delivered in a Solution Architecture Report. The Data Science team reviews and re-evaluates the project values and decides on the various checkpoints to consider.
There is a large number of tools available in the market dedicated to the data acquisition and data cleaning process. In terms of tools, these go by the name, Data Integration ETL tools. Here, ETL stands for Extract, Transform, and Load.
Tableau, Informatica, SAP, IBM InfoSphereInformation Server, Oracle, SAS, etc., are some examples of Data Integration ETL tools.
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Stage 4: Data Analysis
Key Objectives: The objective of this stage is to explore the data gathered and cleaned in previous stages.
Exploring the data includes several steps:
1. Inspect the Data: The accumulated data is not of a single type. It can be numeric, alpha-numeric, categorical data, or ordinal data.
2. Use Statistical Analysis: Use statistical methodologies to test the variables in order to find a correlation between them and to find trends and patterns in them.
3. Visualize the Data: After the data sets have been tested, visualize the data so that the trends and patterns can be explained and understood.
The data is explored with the help of Exploratory Data Analysis (EDA). This is the stage, where the real work begins. Exploratory Data Analysis seeks to find the underlying trends and patterns in the data with the help of statistical tests and visualization techniques.
Some of the properties that are observed in this analysis are the features/attributes of variables, attribute distribution, class imbalance, etc. These details help the data scientists grab the first in-depth insight on the data, which in turn helps them choose the data models.
This analysis can be done using popular programming languages Python or R. Some of the non-programming tools used for Exploratory Data Analysis are SPSS, Excel, Tableau, etc.
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Stage 5: Data Modeling
After business understanding is done and the right data is acquired, the next stage is modeling in which a machine learning model is developed to make the predictions accurately. This is usually done by Feature Engineering, which indicates linking of variables to understand how the machine learning algorithm can use them.
This is the stage, where a Data Scientist is usually expected to have Domain Expertise. The purpose of this stage is to find and include new and significant variables that can answer the target questions. Now, it is time to choose the right modeling algorithms that can help make the predictions better with the given datasets. Usually a series of machine learning algorithms are used to determine the best solution.
The data can be modeled using various concepts like classification, linear regression, clustering, etc. As soon as the computations are done, you get output values that will help in the next phase.
Following are some of the most widely used Machine Learning Techniques:
1. Supervised Learning: In Supervised Learning, a known set of input data and known responses to the data are used to train a prediction model for reasonable predictions of unknown input data.
2. Regression Analysis: Regression Analysis is a predictive modeling technique that analyzes the relationship between two variables, dependent and independent, respectively. It is quite a reliable method of identifying the cause and effect of any variable on a dataset.
3. Classification: Classification is a Statistical technique that categorizes the data into various classes. The purpose of performing this analysis is to identify the category into which a particular variable falls. The two major classification techniques are Logistics Regression and Discriminant Analysis.
4. Unsupervised Learning: Unlike Supervised Learning, Unsupervised Learning seeks to find hidden trends and patterns in the datasets.
5. Clustering Analysis: Clustering Analysis is a form of unsupervised learning technique that seeks to analyze the similarities and differences in the attributes of data.
6. Association Analysis: Association Analysis is a technique used to identify hidden relationships within the large datasets. These associations are presented as Association Rules.
7. Semi-Supervised Learning: Semi-Supervised Learning is a combination of Supervised and Unsupervised Learning to produce the desired results.
8. Reinforcement Learning: The Reinforced Learning seeks to train a machine on the basis of past experience and feedback received.
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Stage 6: Data Deployment
Now that the data is acquired and the right machine learning model(s) is/are chosen, the next phase is to deploy the chosen model through data pipeline. The predictions can be made in batches or in real-time. To be able to consume the model appropriately by various business applications such as spreadsheets, dashboards, etc., the Data Scientists need to expose the models to open API.
At the end of the Data Science Life Cycle, it is evaluated that whether the model, the pipeline, and the deployment are meeting the customer needs. Not only this, the model should answer the customer’s questions with accuracy and reliability.
Data Science Venn Diagram
As explained above, Data Science is a multi-disciplinary domain which amalgamates three vast fields, i.e., Computer Science, Statistics & Mathematics, and Domain expertise. So, anyone who wishes to get into Data Science should acquire these three skills:
1. Computer Science
2. Statistics and Mathematics
3. Domain Knowledge
Data Science Vs Computer Science
Computer Science is a field which applies tools, techniques, logic, algorithms, and programming to get the computers to perform certain tasks. Computer Science basically involves core areas like programming, web development, Internet, networking, hardware, and a lot more.
In order to understand it in a better way, you need to look at the following table:
| Area of Difference | Computer Science | Data Science |
| Scope | Comprises of relatively narrower concepts like Algebra, Calculus, statistics, etc. | Comprises of broader concepts like Algorithms, Computer Architecture, Programming Languages, etc. |
| Category | Data Science is a Subset of Computer Science | Computer Science is the Superset. |
| Focus | Data Science focuses on using the raw data to extract meaningful insights. | Computer Science deals with Computer hardware and software and their applications. |
Data Science vs Software Engineering
The recent boom in Data Science and its demand in various industries have led people, especially from the IT industry, to scrutinize their career preferences as Software Engineers and consider Data Science for obvious reasons such as, higher paycheck and better scope. But rarely do people do look into the deeper aspects of these two professions. While Data Science is relatively a new domain with rather lesser educational pre-requisites, Software Engineering is an older field with stricter qualification and skills as criteria for selecting aspirants.
Let’s have a look at why and how do these domains differ!
| Basis of Difference | Software Engineering | Data Science |
| Definition | A process of designing, developing, and testing software applications based on user requirements. | A multi-disciplinary field that uses algorithms, technology and science to extract meaningful information from data. | Nature of Work | Development and Testing | Analytical |
| Final Product | Software is the end product. For example, Mobile Applications, Operating Systems, Software-as-a-Service, etc. | Data is the end product, such as Insights, Predictions, etc. | Tools Used | Databases, Programming Language Interfaces, Web Applications, Testing Software, etc. | ETL Tools, Data Visualization Tools, Data Analysis Tools, Programming Language Interfaces, etc. |
| Skills Required | Programming Languages, like Java, C++, PHP, Dot Net. Testing Tools like Maven, Ant, Gradle, etc. Database Skills like SQL. | Programming Language like Python, R, etc. Data Visualization Tools like, Tableau, Power BI, QlikView, etc. Knowledge of Mathematics and Statistics. |
Both Software Engineering and Data Science have different scopes and require different skill-sets to hone. Both have different nature of work and hence stating that one domain is better than the other will be uncalled for.
What are Math and Statistics Required for in Data Science?
Mathematics and Statistics cover a significant portion of Data Science. Mathematics, as the term implies, encompasses the computation of data using various concepts and theorems. On the other hand, Statistics involves methodologies and techniques to refine and model the raw data to make inferences and conclusions.
Statistical methods are used to test research hypotheses to prove certain correlations between events/phenomena.
Why Domain Knowledge is Important for Data Science?
Often signified as Business Acumen, domain expertise denotes the “business know-how” that forms the background for analyzing data. Knowledge of the market, the target customer, the customers’ needs, and the value to be delivered are the core segments of Domain Expertise.
A Data Scientist should also have excellent communication skills as it is a foundation for a thriving career in modern times.
5 Job Roles in Data Science
Data Science offers plenty of job opportunities to aspiring individuals. Whether it is someone who is a professional from the same domain, or someone who wishes to switch to Data Science, or someone who wants to launch her career as a beginner, Data Science has enormous lucrative job opportunities for everyone.
Some of the prominent job roles in Data Science are discussed below!
Data Scientist
Data Scientist is probably the first job roles that comes into your mind, when someone mentions ‘Data Science’ to you. So, who is a Data Scientist? Let’s know here!
If we go by the definition, then “Data Scientists are the professionals who wrangle with the data to solve a business problem.”
For example, a Sales firm gets a plethora of data from multiple sources, like Direct Customer interaction, Digital interaction, Feedback forms, bills, ledgers, cost sheets, balance sheets, and much more. Now if the firm wants to invest in various marketing strategies to attract more customers, it has to know what kind of activities would pay-off. For this, the firm has to analyze the past and on-going data to understand customer preferences, which would in turn suggest the best marketing strategy to be deployed.
Roles and Responsibilities of a Data Scientist
- Understanding business problems
- Identifying data sources
- Analyzing Data to find trends and patterns
- Building Predictive models
- Applying machine learning techniques
- Finding solution to a business problem
Data Scientist Salary
As per PayScale, 90% of the Data Scientists in the U.S. are earning $136,000 annually.
Data Engineer/Data Architect
“Data Engineers are the professionals who make sure that the data pipeline is created in a way that can handle the increasing speed, volume, and variability of big data, without any glitches.”
As the companies grow, their data infrastructure also grows, which results in slowdown of throughput. At this time, companies hire Data Engineers who not only create Data pipeline, but also make sure that the infrastructure is running smoothly.
Roles and Responsibilities of a Data Engineer
- Construct, test, and maintain scalable Data Management Systems.
- Improve existing systems.
- Develop custom analytics applications.
- Collect and store data to allow batch-processing and real-time processing.
- Handle errors.
- Ensure Seamless Integration.
Data Engineer Salary
On an average, Data Engineers earn $91,771 per year in U.S. –PayScale
Data Analyst
“A Data Analyst is someone who analyzes structured data.” Most of the times, the roles of a Data Scientist seems to be quite similar to that of a Data Analyst. Though it is true that the nature of the jobs is somewhat similar, there is a huge difference too. A Data Analyst can be considered as the basic stage of becoming a Data Scientist. The following roles and responsibilities will further clarify it!
Roles and Responsibilities of a Data Analyst
- Analyze data and interpret results using statistical techniques.
- Obtain Data from various sources.
- Prepare data to be analyzed.
- Maintain Data Systems.
Data Analyst Salary
The average salary of a Data Analyst in U.S. is $96,072 annually. – PayScale
Business Analyst
“A Business Analyst is a professional who is a bridge between Business and IT.” The profile of Business Analyst is very similar to Data Analyst. However, a Business Analyst tries to solve a business problem with the help of technology.
Roles and Responsibilities of a Business Analyst
- Requirement gathering
- Gap Analysis
- Knowledge Transfer to Developers
- Identify scope using optimal solutions
- Test Preparation
Business Analyst Salary
A Business Analyst earns around $68,731 in a year in U.S. –PayScale
Database Administrator
“A Database Administrator ensures that the Database is working smoothly and is properly accessible to everyone.”
Roles and Responsibilities of a Database Administrator
- Evaluation of Database Software
- Modify Database Systems as per the need
- Maintain the integrity and performance of Database
- Ensure the security of Data
Database Administrator Salary
A Database Administrator earns around $54,160 in a year in U.S. –PayScale
How to Learn Data Science?
If an aspirant wishes to master Data Science, she has to learn following Data Science Skills:
While Python is popular for being agile, its strong features and ease of learning, R is preferred for having rich libraries and extensive APIs that make writing codes easier and allows the data scientists to perform operations faster than Excel.
“Freshers need to start with Python programming for Statistics & Probability, Data analysis & Visualization, Machine Learning, Deep Learning”
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- Data Visualization: Data visualization is an important stage of Data Science, and hence, learning and mastering the top data visualization tools are somewhat necessary for aspiring Data Scientists. Data visualization tools like Tableau, PowerBI, Qlikview, D3.js, etc., accumulate the complex data sets and compile them to represent in interactive graphs, charts and trends that provide data scientists with eye-opening insights and help take actions quicker.
- Statistics: Statistical methodologies are applied at various stages of the Data Science Process and making it big in Data Science without Statistics is not easy. Mean, Mode, Median, Variance, Kurtosis, ANOVA, Quartile, Regression, Correlation, Logistic Regression, etc., are some of the statistical techniques learning which is always good for an aspirant.
- Mathematics: Mathematics is usually applied in analyzing data. Since Data Science deals with bulks of complicated datasets, having good knowledge of Mathematics helps in refining the data in a better way.
- Database: Acquiring the right data to be analyzed is possible only when the Data Scientist knows Database technologies. Knowledge of databases and query languages is necessary for a Data Scientist to discern how to extract the right data in the right quantity and in a manner that helps her create statistical models and perform various operations.
- Business Acumen: Business acumen refers to the domain knowledge that a Data Scientist must have to understand a business challenge and think through the right ways to overcome it.
- Communication Skill: In the modern industry settings, the importance of strong communication skill is unavoidable. Any professional working with team of people from cross-cultural backgrounds has to possess communication skills so that information flow remains smooth and coordination among teams is improved.
If you are looking for ways to get a head-start in Data Science, this blog will help you do just that, but smartly!
Is It Compulsory to Pursue Post-Graduation to Get into Data Science?
A bachelor’s degree in IT, computer science, math, physics, or another related field would be a great foundation for learning Data science. In most of the cases, a bachelor’s degree is required for most entry-level jobs, and a master’s degree gives an edge for many upper-level jobs.
With current available learning resources, anyone can make a career in the field of Data Science. If you have a technical or computer background, it would be easier for you to understand the Data Science concepts. However, it is not mandatory for anyone to be from Computer Science background.
It is recommended to achieve Data Science Certification. It shows a potential employer a bona fide level of interest in the industry and the skill set. Continued education also demonstrates work ethic and commitment on your part.
In general, there are 3 common steps to make a career in Data Science field:
1. Earn a bachelor’s degree in IT, computer science, math, physics, or another related field.
2. Earn a master’s degree in data or related field.
3. Gain experience in the field you intend to work in (ex: healthcare, physics, business).
Note: Keen interest to keep learning, an approach of a detective, ready to deal with complex problems & Time Management – These are the most important skills rated by the industry professionals!
If you find these as a part of your personality then go ahead, the world of Data Science needs you!
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3 Steps to Make a Career in Data Science Field
Modern employment world is all about finding the right candidate for any position. When it comes to Data Science, companies look for people who are not only masters in this domain, but are forward thinkers who add value to the business.
Following three concrete steps would help an aspirant kick-start a career in Data Science:
Step 1: Choose the Right Data Science Course
In order to know what Data Science course is right for you, you need to understand the different job roles offered by Data Science, What are the roles and responsibilities of a job role, and what skills you need to acquire for that. To understand it better, refer to the Section “5 Job Roles in Data Science” in this page.
As soon as you are able to decide on the Job Role you want to enter as, you are all set to take up the right Data Science Course. Mostly, there are two ways out:
1. Start your project as a freelancer Data Scientist: There are open data sources available on the web, decide on a project and start building on that.
2. Take up an Online Training Course: This is becoming very popular among the aspirants. The major benefit you can get by selecting this option is that you get a certification at the end, you get to work on case studies, and you acquire knowledge that only experienced industry professionals can impart.
Step 2: Build the Necessary Data Science Skills
As soon as you start learning the Data Science fundamentals, start working on the core skills that you would want to showcase as your strength. There are basically 7 core skills that you would need to become a Data Scientist. Read the Section “How to Learn Data Science?” to get a clear about the necessary Data Science skills.
However, there are different skill-sets required for different Job roles. For an instance, a Data Scientist has to be stronger in terms of Programming and analytics, on the other hand, a Data Engineer has to be stronger in terms of Big Data infrastructures, Relational, and non-relational databases.
Step 3: Obtain a Certification
Enrolling into an online training course also helps the aspirants prepare themselves for a certification. There are various certifications which will help you fast-track your career in Data Science.
1. Cloudera Certified Associate: Data Analyst
2. SAS Certified Advanced Analytics Professional
3. SAS Certified Big Data Professional
4. Dell Technologies Data Scientist Associate (DCA-DS)
5. Dell Technologies Data Scientist Advanced Analytics Specialist (DCS-DS)
So, having a course completion certificate combined with a renowned certification will surely help you stand out from the crowd.
Interested in knowing more about Data Science Certifications and their impact on your career, then read this enthralling blog on Simpliv.
