why data science college essay

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Careers in STEM: Why Should I Study Data Science?

Careers in data science are in high demand and offer high salaries and advancement opportunities. Learn five reasons to consider a career in the field.

Valerie Kirk

Data is often referred to as the new gold because it has become an essential raw material.

From smartphones to traffic cameras to weather satellites, modern technology devices are collecting massive amounts of data that support everything from cancer research to city planning.

The importance of data in today’s world spans all industries including healthcare, education, travel, and government. Business decisions are made based on data and improving customer experiences relies on data. It is also critical for our national defense. Simply put, today’s world runs on data.

But unlike gold, data does not have value in its raw state. To tap into the power of data to make smart, data-driven decisions, it has to be collected, cleaned, organized, and analyzed.

This is why data is also called the new oil, which also needs to be extracted and refined in order to have value.

That’s where the field of data science comes in.

What is Data Science?

Data science is the study of data to extract meaningful insights for business and government.

People who pursue a degree in data science study math and computer science. Their career path includes jobs where they handle, organize, and interpret massive volumes of information with the goal of discerning patterns. They also construct complex algorithms to build predictive models. Data science tasks include data processing, data analytics, and data visualization.

Data scientists are on the leading edge of innovation and emerging technology, including machine learning and artificial intelligence, which relies on a significant amount of digital data to generate insights.

Careers in data science are growing fast. Data science jobs are in high demand and can be found in nearly every industry. A few of the most common data science jobs include:

• Chief Data Officer
• Artificial Intelligence Engineer
• Data Scientist
• Data Engineer
• Machine Learning Engineer
• Software Engineer
• Data Modeler
• Data Analyst
• Big Data Engineer

Why is Data Science Important?

Just as data is the new gold and the new oil, data is also the new currency. For businesses, the insights derived from data science are essential for data-driven decision-making. They guide everything from the product lifecycle to fulfillment to office or warehouse locations. Data scientists provide information that’s critical to a company’s growth.

The benefits of data science extend beyond business. Government agencies from the federal level down to state and local entities also rely on data insights for emergency planning and response, public safety, city planning, intelligence gathering, national defense, and many other services.

Another reason why data science is important? It taps into the potential of artificial intelligence, which can improve productivity and efficiencies, provide stronger cybersecurity, and personalize customer experiences. To be effective, artificial intelligence relies on a lot of data, which is often pulled from massive data repositories and organized and analyzed by data scientists.

Learn About Our Data Science Graduate Degree Program

5 Reasons to Study Data Science

The field of data science is a great career choice that offers high salaries, opportunities across several industries, and long-term job security. Here are five reasons to consider a career in data science.

1. Data Scientists Are in High Demand

According to the United States Bureau of Labor Statistics , data scientist jobs are projected to grow 36% by 2031, which is much faster than the average for all occupations. Data science careers also offer significant potential for advancement, with the relatively new role of chief data officer becoming a key C-suite position across all types of businesses.

Because the high-demand field requires a special skill set, professionals with data science degrees or certificates are more likely to land a desired position in a top company and enjoy more job security.

2. Careers in Data Science Have High Earning Potential

That high demand also leads to higher salaries relative to other careers. According to Glassdoor, the estimated total pay for a data scientist in the United States is $126,200 per year .

New data scientists can expect starting salaries of around $100,000 per year, with experienced data scientists earning more than $200,000 per year. The average annual salary for chief data officers is $636,000 , with top data executives clearing more than $1 million a year.

The salary potential is only expected to grow as data drives artificial intelligence innovations.

3. Data Science Skills are Going to Grow in Value

Think about this — smartphones, drones, satellites, sensors, security cameras, and other devices collect data 24 hours a day, seven days a week. Data is also being generated by organizations from every project, product launch, customer sale, employee action, and other business activities.

Then think about data that comes from every financial transaction, healthcare interaction, scholarly research project, and other initiatives outside of the business world. Data is continuously being generated from multiple sources for multiple uses — and that isn’t going to stop.

Turning all of that data into actionable insights is a unique, high-demand skill that will only grow in value as more data is generated. As technology advances, data scientists will be at the forefront of new breakthroughs and innovations. It’s an exciting and evolving career.

4. Data Science Provides a Wide Range of Job Opportunities

Every business, government agency, and educational institution generates data. They all need support in gaining insights from that data. Having a degree or certificate in data science gives people the flexibility to work in the industry that interests and inspires them.

5. Data Scientists Can Make the World a Better Place

While data scientists can offer insights to help businesses grow, they can also offer insights to help humanity. Data science careers include unique opportunities to make an impact on the world. Consider these initiatives where data science is playing a significant role:

• Climate change. To support climate control measures that could lower carbon dioxide emissions, the California Air Resources Board, Plant Labs, and the Environmental Defense Fund are working together on a Climate Data Partnership to track climate change from space.
• Medical research. The National Institutes of Health is working to improve biomedical research through its NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability Initiative , which enables access to rich datasets and breaking down data silos to support medical researchers.
• Rural planning. The U.S. Department of Agriculture launched a Rural Data Gateway to support farmers and ranchers in accessing the resources they need to support everything from sustainable farming practices to how to lower energy costs.

Other data-driven service-oriented initiatives include making cities safer for pedestrians and bikers, supporting affordable housing for underserved communities, and improving access to social services. Hear about other initiatives that are tapping into the power of AI for good in this fireside chat with Harvard Extension School’s director of IT programs, Bruce Huang.

Study Data Science at Harvard Extension School

If you are ready to start, advance, or pivot to a career in this exciting and growing field, Harvard Extension School offers a Data Science Master’s Degree Program .

The program focuses on mastering the technical, analytical, and practical skills needed to solve real-world, data-driven problems. The program covers predictive modeling, data mining, machine learning, artificial intelligence, data visualization, and big data. You will also learn how to apply data science and analytical methods to address data-rich problems and develop the skills for quantitative thought leadership, including the ethical and legal dimensions of data analytics.

The program includes 11 courses that can be taken online and one on-campus course, in which you develop a plan for a capstone project with peers and faculty. In the final capstone course, you will apply your new skills to a real-world challenge. Capstone project teams collaborate with industry, government, or academic institutions to explore the possibilities of using data science and analytics for good. Recent capstone projects include:

• Improving the climate change model used by NASA.
• Developing a tool that combines aerial imagery and advanced georeferencing techniques to assess damage in disaster-stricken areas.
• Using computer vision and video classification to develop a crime detection system for analyzing surveillance videos and identifying suspicious activities, contributing to enhanced public safety and crime prevention efforts.
• Predicting patient MRI scans in a hospital system to optimize resource allocation and ensure efficient patient care delivery.
• Streamlining the medical coding process to reduce errors and improve efficiencies.

You can also earn a Data Science Graduate Certificate through the Harvard Extension School . In this certificate program, you will:

• Master key facets of data investigation, including data wrangling, cleaning, sampling, management, exploratory analysis, regression and classification, prediction, and data communication
• Implement foundational concepts of data computation, such as data structure, algorithms, parallel computing, simulation, and analysis.
• Leverage your knowledge of key subject areas, such as game theory, statistical quality control, exponential smoothing, seasonally adjusted trend analysis, or data visualization.

Four courses are required for the program and vary based on the data science career path you are interested in pursuing.

If you are thinking about advancing your career or making a career change into the growing data science field, learn more about the Data Science Master’s Degree program or the Data Science Graduate Certificate program including class requirements, tuition, and how to apply.

About the Author

Valerie Kirk is a freelance writer and corporate storyteller specializing in customer and community outreach and topics and trends in education, technology, and healthcare. Based in Maryland near the Chesapeake Bay, she spends her free time exploring nature by bike, paddleboard, or on long hikes with her family.

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The Data Scientist

Optimal Structure for Data Science Essays

Writing a compelling data science essay requires a structured approach to convey complex information clearly. Aaron Henderson, a seasoned data science expert at StateOfWriting, offers strategic insights into structuring essays that are not only informative but also engaging. According to Henderson, a well-structured essay can distinguish between a reader grasping the essence of data findings and being overwhelmed by the technical details. Here’s how to structure your data science essays to enhance clarity and impact.

Introduction: Setting the Stage

The introduction of your data science essay should serve as the blueprint for your discussion, providing a clear outline of what the reader can expect. Henderson emphasizes the importance of starting with a hook that grabs the reader’s attention and then moving into a thesis statement that briefly presents the central argument or purpose of the essay.

• Start with a compelling fact or statistic highlighting your topic’s relevance.
• Introduce the central question or problem your essay will address.
• State your thesis clearly – this is your essay’s guiding light.
• Outline the structure of your essay briefly, letting the reader know how you will approach your topic.

This section sets the tone and direction for your essay, so it’s crucial to make it engaging and informative right from the start.

To further enhance the quality and effectiveness of your data science essays, Aaron Henderson advises considering professional writing services. The State Of Writing essay writing service supports organizing your thoughts, refining your arguments, and ensuring that your writing meets academic standards. Whether you’re struggling with the structure, need help with proofreading, or require feedback on your data interpretation, it can offer personalized assistance that complements your knowledge and boosts your essay’s clarity and impact. This additional support can be particularly beneficial for complex subjects like data science , where conveying information clearly and accurately is critical to the reader’s understanding.

Body: Discussing the Core Elements

The body of your essay is where the heavy lifting occurs. This section should be divided into clear, logical paragraphs, each focusing on a specific aspect of your thesis. Aaron Henderson suggests structuring each paragraph around a single main idea supported by data, examples, and analysis. This approach ensures that your essay remains coherent and focused.

• Begin each paragraph with a topic sentence that introduces the paragraph’s main idea.
• Include relevant data and figures to support your points, ensuring they are appropriately cited.
• Provide analysis and interpretation of the data, explaining how it supports your thesis.
• Conclude each paragraph by linking to the central thesis, showing how the information contributes to your argument.

This systematic approach helps maintain a clear narrative flow , making complex data more accessible and understandable to the reader.

Conclusion: Wrapping It Up

The conclusion of your data science essay is your opportunity to summarize your findings and reinforce your thesis. Aaron Henderson advises against introducing new information in this section; instead, focus on distilling the main points you’ve covered and their implications. It is your chance to leave a lasting impression on your reader, so make it count.

• Restate your thesis in a new way, reflecting the insights gained from your essay.
• Summarize the key points you’ve discussed, emphasizing their relevance and impact.
• Discuss the implications of your findings, especially any future research or applications.
• End with a strong closing statement that highlights the significance of your analysis.

A well-crafted conclusion not only neatly wraps up your essay but also underscores the importance of your research and findings, making it an indispensable aspect of any academic or professional discourse, especially in the context of the ever-evolving landscape of essay service demands.

Additional Tips for Effective Data Science Writing

Beyond structure, Aaron Henderson highlights several vital elements that can enhance the quality and readability of data science essays:

• Clarity and Simplicity: Use simple language to explain complex concepts. Avoid jargon unless necessary, and constantly define terms that may not be familiar to your audience.
• Logical Flow: Ensure that each section of your essay flows logically into the next. Use transition words and phrases to help guide the reader through your arguments.
• Visuals and Diagrams: When appropriate, include charts, graphs, or diagrams to help illustrate your points. Visual aids can make data more digestible and engaging.
• Revision and Feedback: Don’t underestimate the importance of revising your essay and seeking feedback from others. Fresh eyes can catch inconsistencies and areas that need more clarification.

By embracing these additional tips from Aaron Henderson, you can elevate the precision and appeal of your data science essays, ensuring they are insightful and a pleasure to read.

Following these guidelines from Aaron Henderson will help you craft data science essays that are informative, engaging, and accessible. Whether you’re a student aiming for academic excellence or a professional sharing insights in the field, a well-structured essay is the key to effectively communicating complex data-driven topics. By adhering to a clear, logical structure and integrating advanced writing techniques, your essays can stand out in academic or professional settings. The ability to present data and analyses enhances comprehension and demonstrates mastery of the subject matter, paving the way for further discussion and exploration within the data science community.

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October 29, 2018

Application Advice Part 4: The Essays

Most graduate school applicants will agree – writing application essays is the most difficult and stressful part. While writing your essays may seem daunting, this is your chance to differentiate yourself from other applicants with comparable qualifications. It is likely that dozens of applicants, if not more, have academic records and professional backgrounds that are similar to yours, but strong essays may put you ahead of the competition.

Before you write:

• Read the questions.  Before you begin writing, take the time to carefully read the questions in each essay prompt. Make you sure you understand everything that is being asked of you.
• Reflect on the purpose.  Ask yourself: What purpose does this essay serve? For example, Essay 1: Why UW MSDS? should give the admissions committee insight into your short-term and long-term goals, why you want to attend our program specifically, and what kind of career you envision after graduation.
• Create an outline.  Drafting an outline will help you organize your thoughts and identify the building blocks of your essay. An outline will also help you better understand what you are trying to communicate to the admissions committee.

Writing your essays:

• Write a compelling introduction . Do not start with “My name is…” or “I am applying because…”. Your name or the fact that you are applying are not the most interesting things about you! You should choose to begin your essay, for example, with an anecdote, a question, or an attention-grabbing statement, but make sure it is directly relevant to your essay.
• Don’t repeat your resume.  If you do talk about your work experience, do so briefly and only to make a larger point.
• Show, don’t tell.  Offer examples, stories, or descriptions when writing your essays. For example, rather than just stating that you want to work as a data scientist in the healthcare sector after graduation, describe what you find the most compelling about the industry or job role.

Revising your essays:

• Edit, edit, edit!  Give yourself enough time to write multiple drafts of your essays. One of the most common mistakes applicants make is to leave too little time for writing their essays.
• Proofread.  Proofread your essays carefully and try reading your essays out loud to help catch any awkward phrasing. Ask your classmates, instructors, or colleagues to proofread your essays. Make sure your work is free of spelling mistakes and grammatical errors.
• Stick to the word limit.  It might be tempting to ignore the word limit, but we want to see that you can get your point across clearly and concisely.

Last, but not least, make sure you do not duplicate the content of your essays. Each essay has its own purpose. There is no reason to repeat the same ideas in your writings.

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Why Study Data Science and Data Analytics

Whatever your career goals, data expertise can help you.

Data scientists and analysts use their technical knowledge to discover answers embedded in large data sets that can solve a variety of questions. But you don’t need to be a full-fledged data scientist to reap the benefits of becoming data literate.

“Data isn’t used in a vacuum,” says journalist Laurence Bradford in a Forbes.com article . “It touches many other roles, and those employees need the literacy to handle it effectively.”

A case study produced by the Business Higher Education Forum (BHEF) emphasized the importance of becoming a data-enabled professional. “Companies of all sizes rely on data-driven decision making as a transformational component of their core operations,” the study reports. “They have defined a need for the data-enabled professional who can marry a deep background in a particular field (e.g., the arts, agronomy, economics, finance, health or business) with the ability to apply analytics and visualization tools.”

The Value of Having Data Analysis As a Skill on Your Résumé

When you put data analysis on your résumé, you signal to potential employers that you can solve problems. This key skill can help you in any career. A data analyst in Towards Data Science explains how they used data to identify and solve a project management problem. “Data crunching, business analysis and finding unique insights is a very essential part of management analysis and decision making,” the analyst writes. “There are several tools and techniques that are used. But what I have realized is that more than the tools, what is important is how you think and approach problem solving using data. I can’t emphasize how important it is to talk with data and have the skill to analyze it, visualize it exactly, which yields some insights.”

Becoming data literate also helps you communicate complex information. An article in SAS Insight makes it clear that enhancing your data-communication skills can help you in a variety of ways. “These communications skills are a vital part of any career,” the article states, “and with the added benefit of being a central part of an organization's decision-making processes, analytics experts often pick up strong leadership skills, as well.”

How to Employ Data Analysis to Your Work—Without the Data Analyst Title

You don’t have to be a full-fledged data scientist to take advantage of data-literacy skills. Many departments and positions can benefit from increased data knowledge.

Bradford says that with increasing automation “those in more ‘standard’ business roles need to understand what data is being collected, what automatic processes are being performed, and how that affects their individual roles and the broader company.”

A recent Indeed.com article lays out 18 skills that data analysts need to have—such as public speaking, data visualization, critical thinking and spreadsheets. Besides the usual technical subjects like machine learning and SQL , there are some softer skills that many employees already have such as domain knowledge (i.e., basic subject expertise) and project management experience.

A Monster.com article outlines a few careers where your data skills could be helpful, including reporting and human resources .

How to Get Started

Take a basic statistics course . Statistics is the discipline of using data to solve problems. If you progress no further in your learning, you’ll still be able to apply these statistical concepts on the job and enhance your understanding of the world.

Take a Structured Program

Data professional Allan Miller is an instructor in our Certificate Program in Data Science . He sees all sorts of opportunities for data scientists, ranging from social science to pure theory. Allan refers to the field as a “spectrum,” morphing from business analysis to data analysis to data science.

We offer a variety of certificate programs that can help you on your journey to getting more data literate—no matter your field—or becoming a data professional.

Certificate Program in Business Analysis

Business success is one of the main drivers for data professionals and those who employ them. Data analysts look at the key performance indicators to quantify success, develop new businesses and markets, and hone day-to-day practices to overcome future challenges.

As a business analyst, your data expertise will help you contribute to the ongoing health of your company. As a business analyst, you most likely will be asked to present data findings to team members and associates who don’t have a data background. A course like Presenting Quantitative Data Effectively can be an asset in your career.

Professional Program in Data Analysis

As a data analyst, you delve more into the technical side of data and work with larger data sets and apply more sophisticated analysis on the information. Even if your expertise is more centered on programming, the applications vary over a wide range of careers.

Certificate Program in Data Science

The complexity of the data sets and your analysis increases when you become a full-fledged data scientist. In this position, you will be delving deep into programming languages, such as R, that grew out of statistical analysis.

Discover Your New Career

Looking for inspiration? Data scientist Allan Miller has some words of advice. “I think a lot of people are attracted to this field because it's such a hot career area right now,” Allan says. “There's such a huge demand for it at the level of business analyst, data analyst and data scientist, so that's certainly a good thing. There's nothing wrong with getting a skill that you can earn a living on.”

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