Lesson 1 of 14By Avijeet Biswal
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.
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 the data science lifestyle.
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:
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. Database: A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.
2. 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.
3. 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.
If learning what is data science sounded interesting, understanding what does this job roles is all about will me much more interesting to you. 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:
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:
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. 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
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Now that you know the uses of Data Science and what is data science in general, let's see all the opportunity that this feild offers 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.
The data science profession is challenging, but fortunately, there are plenty of tools available to help the data scientist succeed at their job. And now that we know what is data science, it's lifecycle and more about the role in general, let us dig into it's tools.
There are various applications of data science, including:
Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.
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.
Identifying patterns is one of the most commonly known applications of data science. in images and detecting objects in an image is one of the most popular data science applications.
Next up in the data science and its applications list comes Recommendation Systems. Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.
Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.
Fraud detection comes the next in the list of applications of data science. Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.
Internet comes the next in the list of applications of data science. 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.
Speech recognition is one of the most commonly known applications of data science. It is a technology that enables a computer to recognize and transcribe spoken language into text. It has a wide range of applications, from virtual assistants and voice-controlled devices to automated customer service systems and transcription services.
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.
Next up in the data science and its applications list comes 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.
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.
Here are some brief example of data science showing data science’s versatility.
Data science, in simple words, is the field of study that involves collecting, analyzing, and interpreting large sets of data to uncover insights, patterns, and trends that can be used to make informed decisions and solve real-world problems.
Data science is used for a wide range of applications, including predictive analytics, machine learning, data visualization, recommendation systems, fraud detection, sentiment analysis, and decision-making in various industries like healthcare, finance, marketing, and technology.
A data scientist analyzes business data to extract meaningful insights.
Data scientists solve issues like:
Sometimes they may be called upon to do so.
If you wish to know anything about our data science course, please check out Data Science Bootcamp and Data Science master’s program.
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.
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.
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
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