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What is Data Science: Lifecycle, Applications, Prerequisites and Tools

Lesson 1 of 14By Avijeet Biswal

Last updated on Dec 16, 20244579438
What Is Data Science: Lifecycle, Applications, Prerequisites and Tools

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Data science is using data to solve problems, answer questions, and make decisions. It combines skills like math, programming, and understanding data to find patterns, make predictions, and uncover insights.

Introduction

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 is data science, its applications, 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, including essential data science skills, 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.

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:

  1. Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
  2. 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.
  3. 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.
  4. 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.
  5. 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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Data Science Prerequisites

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.

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Who Oversees the Data Science Process?

1. 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.

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.

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What is a Data Scientist?

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:

  1. Discover patterns and trends in datasets to get insights
  2. Create forecasting algorithms and data models
  3. Improve the quality of data or product offerings by utilising machine learning techniques
  4. Distribute suggestions to other teams and top management
  5. In data analysis, use data tools such as R, SAS, Python, or SQL
  6. 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.

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. So, if you’re looking for an exciting career that offers stability and generous compensation, then look no further!

Did You Know?🔎
According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 36% from 2023 to 2033, significantly outpacing the average for all occupations.

Uses of Data Science

  1. Data science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.
  2. Tech businesses that acquire user data can utilise strategies to transform that data into valuable or profitable information.
  3. 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.
  4. Data Science applications provide a better level of therapeutic customisation through genetics and genomics research.

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Where Do You Fit in Data Science?

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.

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).

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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. 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.

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Applications of Data Science

There are various applications of data science, including:

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 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.

4. Recommendation Systems

Next up in the data science 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.

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

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.   

7. Internet Search

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.

8. Speech recognition

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.

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

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.

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 example of data science 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.

Challenges of a Data Scientist

Some of the common challenges that a data scientist faces, include:

  • Handling large and messy datasets that require cleaning and organization.
  • Selecting the right tools and techniques for analysis.
  • Ensuring accurate and unbiased results.
  • Communicating complex findings to non-technical stakeholders.
  • Aligning data projects with business goals.
  • Keeping up with rapidly evolving technologies.
  • Managing data privacy and security concerns.

Data Science vs Business Intelligence

Data Science and Business Intelligence (BI) are both data-driven fields but differ in focus and approach. Data Science emphasizes predictive and prescriptive analytics, using advanced techniques like machine learning and AI to forecast trends and provide actionable recommendations. It deals with raw, unstructured, and large datasets to solve complex problems and discover new opportunities.

On the other hand, Business Intelligence focuses on descriptive analytics, analyzing structured data from databases to generate reports, KPIs, and dashboards that summarize past and present performance. While Data Science is exploratory and future-oriented, BI is analytical and operational, helping business managers and executives make informed decisions based on historical data insights.

Wrapping It All Up

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. Explore and enroll in Simplilearn's unique Data Scientist program to master the most in-demand data science tools and skills, and ultimately scale up your career. Start today!

FAQs

1. What is data science in simple words?

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.

2. What is data science used for?

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.

3. 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.

4. What does a data scientist do?

A data scientist analyzes business data to extract meaningful insights.

5. What kinds of problems do data scientists solve?

Data scientists solve issues like:

  1. Loan risk mitigation
  2. Pandemic trajectories and contagion patterns
  3. Effectiveness of various types of online advertisement
  4. Resource allocation

6. Do data scientists code?

Sometimes they may be called upon to do so.

7. What is the data science course eligibility?

If you wish to know anything about our data science course, please check out Data Science Bootcamp and Data Science master’s program.

8. Can I learn data science on my own?

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.

About the Author

Avijeet BiswalAvijeet Biswal

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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