Data Science vs Computer Science vs Data Analytics: A Breakdown

With the pandemic steering the world towards technology-based solutions, the tech sector has experienced significant growth. Of the endless career possibilities in the sector, data science, computer science, and data analytics are three prominent fields currently taking center stage. These domains are now responsible for influencing critical decisions and driving success in both small-scale and large-scale organizations.

Now, how do these fields differ from each other? Are they subsets of a single discipline? 

Here is a breakdown of the three fields: data science vs. data analytics vs. computer science, the skills you need, what these fields entail, and how you can springboard your career in each.

What is Data Science?

As data is the new fuel that powers businesses across all industries, data science has emerged as something businesses can’t do without. Data science is the ultimate solution to unearth useful intelligence from the data available online; these insights are then used to add value to the business or the organization. Data science relies on algorithms and scientific methods to extract useful business insights from available data. 

What Are the Applications of Data Science?

The cutting-edge technology of data science has transformed and redefined the nature of solutions in a wide range of industries. Here are 5 major applications of data science:

1. Data Science in Business Logistics

Business logistics is one sector in which data science can have a significant influence on waste reduction. Optimizing the routes of delivery, choosing carriers that mitigate the effects of CO2 emissions, safeguarding hazardous materials, and predicting the supply and demand are some ways business logistics use data science.

2. Data Science in Finance

Data scientists have been saving financial entities from catastrophes for a while now. Be it the detection of fraudulent activities or monitoring risks, data science can shield financial institutions from potential threats. These organizations also rely on data scientists during economic recessions, where their analytic skills are used to arrive at make-or-break decisions.

Source: DataFlair

3. Data Science in Health

From medical imaging to predictive analytics in healthcare, data science has a lot of applications in the health sector. Pharmaceutical industries use machine learning and its algorithms to create models and find statistical relationships. The algorithms can then simulate the impacts of a drug on a patient’s body. 

4. Data Science in Education

Data science is used to understand the trends and patterns in industries and tweak the curriculum to accommodate them. Data science also powers student assessment by helping instructors capture the patterns of student performance. Based on the inference, teachers can alter their teaching methods. 

5. Data Science in Entertainment

It is no surprise that the entertainment industry is now data-driven; massive amounts of customer data are now available online, paving the way for the progress of data science in the sector. OTT (over-the-top) platforms like Netflix and Hulu depend on data to personalize the applications for users. The top picks you see on your device is the work of data science. 

The same data is also used for customer segmentation or the classification of customers based on the content they consume. Data science is also used to predict the viewership of content based on which shows can be scheduled. 

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Data Science Lifecycle: Definition and Steps

The data science lifecycle refers to a series of processes that are followed by data scientists to deliver a data science project. 

The following steps are involved:

Step 1 ─ Data Collection

This step is the foundation of your data science project. Incomplete data is unreliable data; failing to procure valid data from relevant data sources is a critical task. Since data storage is not expensive these days, the idea is to collect everything you can get your hands on and filter out relevant data. 

Step 2 ─ Data Preparation

The data that you acquire in the first step is not usable; it has to be organized. In this stage, the quality issues of data are identified and solved. There could even be semantic errors, missing entries, or inconsistencies. The data sets are integrated and structured for analysis.

Source: Sudeep.co

Step 3 ─ Exploratory Data Analysis

Data is analyzed at this stage to extract useful information. Based on your analysis of the information, decisions are made. There are no hard and fast rules in this process; but the more effort and time you dedicate to this process, the better your decisions will be.

Step 4 ─ Model Building

This is the core process in the data science lifecycle. After identifying the nature of the problem, an appropriate data model and algorithm are chosen. The parameters are then tweaked to achieve the desired outcome. 

Step 5 ─ Model Deployment

This is the stage where a data science project gets deployed in the real world through an iOS or Android app. Machine learning projects are first deployed in a test environment and later pushed into production. 

What Skills Does a Data Scientist Need?

The title ‘data scientist' was first used in 2008 to refer to professionals who were skilled at analyzing and organizing data. Today, data scientists are some of the most sought-after professionals in the tech sector. They are data-driven professionals who possess extraordinary domain expertise and technical skills. 

A data scientist is adept at filtering out relevant information, identifying important questions, procuring data from a variety of sources, and eventually translating it into positive results. With the growing application of data science in all industries, these professionals now enjoy great demand in the industry.

You need the following skills to handle data science projects:

1. Data Manipulation and Data Integration

Data manipulation is the synthesizing and organizing of data to make it more comprehensible. To map data the right way, a data scientist needs to master a programming language called data manipulation language (DML). This language is used to manipulate data by adding, deleting, and modifying it. 

Data integration is the process of sourcing data from different locations and combining it to provide a holistic view. This skill is vital to gather business intelligence. Being exceptional at data integration can help you land a job in highly reputable organizations.

2. Machine Learning (ML)

Organizations that are grounded in data-centric decisions are always on the lookout for data scientists who are good at machine learning. Machine Learning is a subset of artificial intelligence and contributes heavily to data modeling by employing algorithms. A thorough understanding of machine learning techniques, such as logistic regression and decision trees, will help you stand out in the industry.

3. Data Visualization

The online business world is flooded with data that has implicit information. This data needs to be gathered first and then presented in a comprehensible visual format. Data visualization is one of the primary tasks a data scientist performs in which he or she creates interactive visualizations to learn trends, patterns, and variations. The inferences made from these visualizations are then shared with stakeholders to make decisions. Tableau, d3.js, and ggplot are some common data visualization tools.

Source: Cleanpng

4. Statistics

Statistics is an inevitable part of a data scientist’s toolkit. Being well-versed in statistics enables you to effortlessly procure, organize, analyze, and filter out information from the data. You need a thorough understanding of the fundamental concepts and principles of statistics, such as percentiles and outliers, cumulative distribution function (CDF), exploratory data analysis, Bayes theorem, probability theory, random variables, and descriptive statistics.

5. Programming

Programming is a must-have skill for data scientists. Though Python is regarded as the gold standard in programming languages, data scientists also need to be proficient in R programming languages. Python is an object-oriented language that is very easy to deploy, while R is an open-source language that is used for statistical analysis. 

6. Business Understanding

An in-depth understanding of the business, as well as the industry, is a mandatory requirement in data science. To be able to identify the right questions, analyze problems, and seize business opportunities, a data scientist must have refined business acumen. 

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Salary Trends in Data Science

Data science is one of the highest-paying jobs in the world. 68 percent of data scientists reported that their pay scale was enough to cover their living expenses. In the United States, the average annual salary of a data scientist is $96,491. 

There could be slight variations in remuneration depending on your organization’s location and your experience. Remember that data scientists with advanced educational qualifications earn more. With a Master’s or a Ph.D., you can easily land a senior role in a reputable organization.

What Tools Are Used in Data Science?

Data scientists need tools to apply their operational knowledge and methods to data. Here are some of the popular data science tools used by data scientists.

1. Data Collection Tools

While interviews and surveys remain conventional methods of data collection, the following are two favorite data collection tools of data scientists:

• Trackur ─ It collects data on social media platforms from feedback on products or services. It serves as a monitoring tool for marketing companies. 
• Semantria ─ It analyzes texts and sentiments to extract useful data and information. It is cloud-based which uses top-notch neuro-linguistic programming (NLP). 

2. Data Storage Tools

Data storage tools are used to store data in large amounts. These tools allow data scientists to unite servers and assess for easy access to data. The following are data storage tools:

• MongoDB ─ It is a free-to-use, document-oriented tool that is available on Linux, Solaris, and Windows. 
• Apache Hadoop ─ It is a software framework that can handle large amounts of data and its computation. It allows easy distribution of data for processing.
• Apache Cassandra ─ It is an open-source, free tool that uses CSL and SQL to communicate with databases. It allows easy access to data that is stored on various servers.

Source: DataFlair

3. Data Extraction Tools

Data extraction tools or web scraping tools are automated tools that are used to extract useful data from websites. The following are two popular data extraction tools:

• Content Grabber ─ It is a data extraction tool that comes with added features like error handling and debugging. It can extract data and provide it in structured forms in your preferred format. 
• OctoParse ─ Available in both paid and free versions, OctoParse is a web scraping tool that extracts and delivers data in spreadsheets. The data is presented in easy-to-read formats. 

4. Data Cleaning Tools 

Data cleaning tools are used to search, filter, and organize data. The following are data cleaning tools:

• OpenRefine ─ This tool refines tangled data and sorts it out before transforming it into a usable format. 
• DataCleaner ─ This is a data indexing tool that works alongside the Hadoop database. It prevents duplication of data and finds missing patterns.

5. Data Analysis Tools

These tools are inevitable for decision-making; they analyze and inspect data to derive insights. The following are common data analysis tools:

• Tableau ─ This is an open-source tool that is used for the visualization of data in maps, charts, bars, etc. 
• Python ─ Python is a general-purpose language that is also used for data visualization. It contains graphical libraries to enable visualization. 
• Orange ─ It is an open-source tool that is used for analyzing data. It is user-friendly and has a graphical interface to support data visualization for analysis. 

What is Computer Science?

Computer science is the scientific study of computers, software, hardware, and their algorithmic and theoretical foundations. It also involves disciplines such as artificial intelligence, network design, data modeling, and information processes. Grounded in mathematics and engineering, computer science has multiple subfields that overlap. 

What Are the Applications of Computer Science?

It goes without saying that the world would come to a standstill in the absence of computers. There is hardly any industry that does not utilize the fruits of this invention. Initially used to handle complex calculations, computers are now ubiquitously applied across all industries to simplify our lives. 

Let’s dive into some applications of computer science.

1. Computer Science in Banking

Automated account management revolutionized the banking sector. Thanks to computer science, bank personnel can now create and maintain accounts easily. Customer transactions now involve a centralized record, helping banks to track things easily. Computer science has also made it possible for smaller financial entities to access the same technology as larger financial institutions, enabling fair business competition. 

2. Computer Science in Business

Be it the IT support in business organizations or the marketing tactics employed by the marketing team, computer science is inevitable in the world of business. With online businesses taking the world by storm, having an online store pays off more than an actual store. Not only do businesses appear more streamlined when you have the know-how to maintain an online store, it saves time and cuts costs. 

3. Computer Science in Medicine

The most remarkable social impact of computer science is in the field of medicine and healthcare. Taking care of patients in critical situations, medical imaging, decision-making, and maintaining hospital information systems are some prominent applications of computer science in medicine. 

4. Computer Science in Government

Computer science has an array of applications in government. With the world depending on computer science now more than ever, governments need computer scientists to run smoothly. They maintain government websites, safeguard them from security breaches, keep records of the country and its population, and support decision-making.

5. Computer Science in Entertainment

Computer science creates, maintains, and troubleshoots all systems that make entertainment; it is the foundation of the modern entertainment industry. The demand for computer science professionals are reportedly increasing in industries like Hollywood. Professional editing software programs paved the way for efficient non-linear editing of movies. Special effects are now an important part of the industry; VFX artists add the desired graphics that can blend in easily with the scenes.

What Skills Do You Need to be a Computer Science Professional?

You need the following skills to be a computer science professional:

1. Software Development Skills

Having software development skills on your resume is one of the easiest ways to catch the attention of your potential employers. It tells them that you can write codes and programs, test, and deliver functional software programs. 

2. Math Skills

Computer science is founded upon mathematics. You need a fair understanding of math to grasp computing formulas. Being good at math comes in handy when you encounter complex problems. 

3. Technical Writing Skills

Skills to create computer-based solutions are not enough; you also need to be an effective communicator to explain the challenges to other team members. With good technical writing skills, you can meticulously write reports, give presentations, and even train others.

4. Computer and Technology skills

Basic computer and technology skills are necessary to do well in any IT role. This includes hardware, software, spreadsheets, and operating systems. An IT professional cannot be relied on if he or she lacks the foundational knowledge of these aspects.

5. Programming Skills

You need programming and coding languages to frame instructions and design your software program. C++, C#, Java, and Python are common programming and coding languages you can add to your resume. This shows your technical expertise to your potential employers.

Salary Trends in Computer Science

What is the salary trend in the computer science market? According to NACE’s survey of 2021, computer science majors are of great value in today’s virtual world; the pandemic has only increased this. While most roles only need a Bachelor’s, you need a Master’s to land managerial positions.

Source: PayScale

$72,173 is the average annual salary of computer science graduates. All three computer science majors (computer science, information sciences and systems, and software applications) saw a considerable increase in the average salary projection (6.8 to 7.1 percent) in recent years.