Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist.
What Is Data Science?
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
The data used for analysis can come from many different sources and presented in various formats.
Now that you know what data science is, let’s see why data science is essential to today’s IT landscape.
The Data Science Lifecycle
Now that you know what is data science, next up let us focus on the data science lifecycle. Data science’s lifecycle consists of five distinct stages, each with its own tasks:
- Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
- Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
- Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
- Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.
- Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
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Prerequisites for Data Science
Here are some of the technical concepts you should know about before starting to learn what is data science.
1. Machine Learning
Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.
2. Modeling
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
3. Statistics
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
4. Programming
Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
5. Databases
A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.
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Who Oversees the Data Science Process?
Business Managers
The business managers are the people in charge of overseeing the data science training method. Their primary responsibility is to collaborate with the data science team to characterise the problem and establish an analytical method. A data scientist may oversee the marketing, finance, or sales department, and report to an executive in charge of the department. Their goal is to ensure projects are completed on time by collaborating closely with data scientists and IT managers.
IT Managers
Following them are the IT managers. If the member has been with the organisation for a long time, the responsibilities will undoubtedly be more important than any others. They are primarily responsible for developing the infrastructure and architecture to enable data science activities. Data science teams are constantly monitored and resourced accordingly to ensure that they operate efficiently and safely. They may also be in charge of creating and maintaining IT environments for data science teams.
Data Science Managers
The data science managers make up the final section of the tea. They primarily trace and supervise the working procedures of all data science team members. They also manage and keep track of the day-to-day activities of the three data science teams. They are team builders who can blend project planning and monitoring with team growth.
What is a Data Scientist?
Data scientists are among the most recent analytical data professionals who have the technical ability to handle complicated issues as well as the desire to investigate what questions need to be answered. They're a mix of mathematicians, computer scientists, and trend forecasters. They're also in high demand and well-paid because they work in both the business and IT sectors.
On a daily basis, a data scientist may do the following tasks:
- Discover patterns and trends in datasets to get insights.
- Create forecasting algorithms and data models.
- Improve the quality of data or product offerings by utilising machine learning techniques.
- Distribute suggestions to other teams and top management.
- In data analysis, use data tools such as R, SAS, Python, or SQL.
- Top the field of data science innovations.
What Does a Data Scientist Do?
You know what is data science, and you must be wondering what exactly is this job role like - here's the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:
- Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.
- The data scientist then determines the correct set of variables and data sets.
- The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.
- Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.
- After the data has been rendered into a usable form, it’s fed into the analytic system—ML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.
- When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.
- The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.
Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.
Why Become a Data Scientist?
You learnt what is data science. Did it sound exciting? Here's another solid reason why you should pursue data science as your work-field. According to Glassdoor and Forbes, demand for data scientists will increase by 28 percent by 2026, which speaks of the profession’s durability and longevity, so if you want a secure career, data science offers you that chance.
Furthermore, the profession of data scientist came in second place in the Best Jobs in America for 2021 survey, with an average base salary of USD 127,500.
So, if you’re looking for an exciting career that offers stability and generous compensation, then look no further!
Read more: Data Scientist Salary In India and US
Use of Data Science
- Data science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.
- Tech businesses that acquire user data can utilise strategies to transform that data into valuable or profitable information.
- Data Science has also made inroads into the transportation industry, such as with driverless cars. It is simple to lower the number of accidents with the use of driverless cars. For example, with driverless cars, training data is supplied to the algorithm, and the data is examined using data Science approaches, such as the speed limit on the highway, busy streets, etc.
- Data Science applications provide a better level of therapeutic customisation through genetics and genomics research.
Where Do You Fit in Data Science?
Data science offers you the opportunity to focus on and specialize in one aspect of the field. Here’s a sample of different ways you can fit into this exciting, fast-growing field.
Data Scientist
- Job role: Determine what the problem is, what questions need answers, and where to find the data. Also, they mine, clean, and present the relevant data.
- Skills needed: Programming skills (SAS, R, Python), storytelling and data visualization, statistical and mathematical skills, knowledge of Hadoop, SQL, and Machine Learning.
Data Analyst
- Job role: Analysts bridge the gap between the data scientists and the business analysts, organizing and analyzing data to answer the questions the organization poses. They take the technical analyses and turn them into qualitative action items.
- Skills needed: Statistical and mathematical skills, programming skills (SAS, R, Python), plus experience in data wrangling and data visualization.
Data Engineer
- Job role: Data engineers focus on developing, deploying, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers support data scientists by helping to transfer and transform data for queries.
- Skills needed: NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages such as Java and Scala, and frameworks (Apache Hadoop).
Data Science Tools
The data science profession is challenging, but fortunately, there are plenty of tools available to help the data scientist succeed at their job.
- Data Analysis: SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
- Data Warehousing: Informatica/ Talend, AWS Redshift
- Data Visualization: Jupyter, Tableau, Cognos, RAW
- Machine Learning: Spark MLib, Mahout, Azure ML studio
Difference Between Business Intelligence and Data Science
You know what is data science, next up know the difference between business intelligence and data science, and know why you can't use it interchangeably. Business intelligence is a combination of the strategies and technologies used for the analysis of business data/information. Like data science, it can provide historical, current, and predictive views of business operations. However, there are some key differences.
|
Business Intelligence |
Data Science |
|---|---|
|
Uses structured data |
Uses both structured and unstructured data |
|
Analytical in nature - provides a historical report of the data |
Scientific in nature - perform an in-depth statistical analysis on the data |
|
Use of basic statistics with emphasis on visualization (dashboards, reports) |
Leverages more sophisticated statistical and predictive analysis and machine learning (ML) |
|
Compares historical data to current data to identify trends |
Combines historical and current data to predict future performance and outcomes |
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Applications of Data Science
Data science has found its applications in almost every industry.
1. Healthcare
Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.
2. Gaming
Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.
3. Image Recognition
Identifying patterns in images and detecting objects in an image is one of the most popular data science applications.
4. Recommendation Systems
Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.
5. Logistics
Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.
6. Fraud Detection
Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.
7. Internet Search
When we think of search, we immediately think of Google. Right? However, there are other search engines, such as Yahoo, Duckduckgo, Bing, AOL, Ask, and others, that employ data science algorithms to offer the best results for our searched query in a matter of seconds. Given that Google handles more than 20 petabytes of data per day. Google would not be the 'Google' we know today if data science did not exist.
8. Speech recognition
Speech recognition is dominated by data science techniques. We may see the excellent work of these algorithms in our daily lives. Have you ever needed the help of a virtual speech assistant like Google Assistant, Alexa, or Siri? Well, its voice recognition technology is operating behind the scenes, attempting to interpret and evaluate your words and delivering useful results from your use. Image recognition may also be seen on social media platforms such as Facebook, Instagram, and Twitter. When you submit a picture of yourself with someone on your list, these applications will recognise them and tag them.
9. Targeted Advertising
If you thought Search was the most essential data science use, consider this: the whole digital marketing spectrum. From display banners on various websites to digital billboards at airports, data science algorithms are utilised to identify almost anything. This is why digital advertisements have a far higher CTR (Call-Through Rate) than traditional marketing. They can be customised based on a user's prior behaviour. That is why you may see adverts for Data Science Training Programs while another person sees an advertisement for clothes in the same region at the same time.
10. Airline Route Planning
As a result of data science, it is easier to predict flight delays for the airline industry, which is helping it grow. It also helps to determine whether to land immediately at the destination or to make a stop in between, such as a flight from Delhi to the United States of America or to stop in between and then arrive at the destination.
11. Augmented Reality
Last but not least, the final data science applications appear to be the most fascinating in the future. Yes, we are discussing something other than augmented reality. Do you realise there's a fascinating relationship between data science and virtual reality? A virtual reality headset incorporates computer expertise, algorithms, and data to create the greatest viewing experience possible. The popular game Pokemon GO is a minor step in that direction. The ability to wander about and look at Pokemon on walls, streets, and other non-existent surfaces. The makers of this game chose the locations of the Pokemon and gyms using data from Ingress, the previous app from the same business.
Example of Data Science
Here are some brief overviews of a couple of use cases, showing data science’s versatility.
- Law Enforcement: In this scenario, data science is used to help police in Belgium to better understand where and when to deploy personnel to prevent crime. With only limited resources and a large area to cover data science used dashboards and reports to increase the officers’ situational awareness, allowing a police force that’s spread thin to maintain order and anticipate criminal activity.
- Pandemic Fighting: The state of Rhode Island wanted to reopen schools, but was naturally cautious, considering the ongoing COVID-19 pandemic. The state used data science to expedite case investigations and contact tracing, enabling a small staff to handle an overwhelming number of concerned calls from citizens. This information helped the state set up a call center and coordinate preventative measures.
- Driverless Vehicles: Lunewave, a sensor manufacturing company, was looking for a way to make sensor technology more cost-effective and accurate. They turned to data science and machine learning to train their sensors to be safer and more reliable, as well as using data to improve their 3D-printed sensor manufacturing process.
- Entertainment: Data science enables streaming services to follow and evaluate what consumers view, which aids in the creation of new TV series and films. Data-driven algorithms are also utilised to provide tailored suggestions based on the watching history of a user.
- Finance: Banks and credit card firms mine and analyse data in order to detect fraudulent activities, manage financial risks on loans and credit lines, and assess client portfolios in order to uncover upselling possibilities.
- Manufacturing: Data science applications in manufacturing include supply chain management and distribution optimization, as well as predictive maintenance to anticipate probable equipment faults in facilities before they occur.
- Healthcare: Machine learning models and other data science components are used by hospitals and other healthcare providers to automate X-ray analysis and assist doctors in diagnosing illnesses and planning treatments based on previous patient outcomes.
- Retail: Retailers evaluate client behaviour and purchasing trends in order to provide individualised product suggestions as well as targeted advertising, marketing, and promotions. Data science also assists them in managing product inventories and supply chains in order to keep items in stock.
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FAQs
1. What’s the difference between data science, artificial intelligence, and machine learning?
Artificial Intelligence makes a computer act/think like a human. Data science is an AI subset that deals with data methods, scientific analysis, and statistics, all used to gain insight and meaning from data. Machine learning is a subset of AI that teaches computers to learn things from provided data.
2. What is Data Science in simple words?
Data science is an AI subset that deals with data methods, scientific analysis, and statistics, all used to gain insight and meaning from data.
3. What does a Data Scientist do?
A data scientist analyzes business data to extract meaningful insights.
4. What is Data Science with an example?
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. For example, finance companies can use a customer’s banking and bill-paying history to assess creditworthiness and loan risk.
5. What kinds of problems do data scientists solve?
Data scientists solve issues like:
- Loan risk mitigation
- Pandemic trajectories and contagion patterns
- Effectiveness of various types of online advertisement
- Resource allocation
- Do data scientists code?
A: Sometimes they may be called upon to do so.
6. What is the data science course eligibility?
A: Check out Simplilearn’s Data Science master’s program for all the details you need.
7. Can I learn Data Science on my own?
A: Data science is a complex field with many difficult technical requirements. It’s not advisable to try learning data science without the help of a structured learning program.
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Wrapping It All Up
Data will be the lifeblood of the business world for the foreseeable future. Knowledge is power, and data is actionable knowledge that can mean the difference between corporate success and failure. By incorporating data science techniques into their business, companies can now forecast future growth, predict potential problems, and devise informed strategies for success. This is the perfect time for you to start your career in data science with Simplilearn's Data Science course.
Do you have any questions regarding this ‘What is Data Science’ article? If so, then please put it in the comments section of the article. Our team will help you solve your queries at the earliest.
