Recommender systems offer a more intelligent approach to information filtering than a simple search algorithm by introducing users to items they might not have otherwise discovered. Recommender systems generally take either a collaborative or content-based approach to filtering. Collaborative filtering considers a user\u2019s previous behavior, as well as the behavior of similar users. Content-based filtering provides recommendations based on discrete attributes or assigned characteristics.<\/p>\n\n\n\n Data scientists at energy software company Tendril opted for a hybrid approach that combines both collaborative and content-based filtering. Tendril provides analytics and consumer solutions to energy suppliers, including which energy products consumers would most likely consider. \u201cWe use Support Vector Regression models to predict household energy consumption to provide our clients with in-depth, personalized information about their customers,\u201d explains Mark Gately, Data Analytics Manager at Tendril. \u201cThis detailed information is also used in recommendation models, which help match eligible customers with new or existing energy products.\u201d<\/p>\n\n\n Jasmine Kyung<\/p> Senior Operations Engineer at Raytheon Technologies<\/p><\/div> Read Story<\/a><\/p><\/div><\/div> Jonathan Orr<\/p> Data Scientist at Carlisle & Company<\/p><\/div> Read Story<\/a><\/p><\/div><\/div> Corey Wade<\/p> Founder And Director at Berkeley Coding Academy<\/p><\/div> Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n Application 2: Credit Scoring<\/strong><\/p>\n\n\n\n The first general purpose credit scoring algorithm, now known as the FICO score, was introduced in 1989. The FICO score is still one of the most widely used models in the United States today, though peer-to-peer and direct lending organizations have focused on developing new techniques over the past few years. These new machine learning models and algorithms capture innovative factors and relationships that traditional loan scorecards couldn\u2019t, like how applicants manage monthly cash flow or whether friends or community members would endorse the applicant.<\/p>\n\n\n\n One such company is Ferratum Bank, a pioneer in financial technology and mobile consumer lending since 2005. \u201cWe developed complex statistical and machine learning models to enable smarter lending decisions,\u201d explains Scott Donnelly, Director of Business Lending at Ferratum Bank. \u201cBy getting creative with our approach and adopting innovative technologies, we\u2019ve been able to reinvent how both consumers and businesses obtain loans. This has allowed us to reach prospective customers that in the past may have been overlooked by traditional banking institutions.\u201d<\/p>\n\n\n\n Application 3: Dynamic Pricing<\/strong><\/p>\n\n\n\n Businesses use dynamic pricing algorithms to model rates as a function of supply, demand, competitor pricing, and exogenous factors (e.g. weather or time). Many fields, from airline travel to athletics admission ticketing, employ dynamic pricing to maximize expected revenue. The nuts and bolts of dynamic pricing strategies vary widely, though generalized linear models and classification trees are popular techniques for estimating the \u201cright\u201d (lowest\/highest) price that consumers are willing to pay for a book, a flight, or a cab.<\/p>\n\n\n\n Data science<\/a> is bringing about a dramatic change in our daily lives. Simultaneously, credit must be given to all those data scientists<\/a>, machine learning engineers, and deep learning researchers that work around the clock to make our lives easier and more comfortable.<\/p>\n\n\n\n That’s it for today’s sneak preview of the whitepaper. If you liked what you read & want to learn about more practical applications of data science, like Customer Churn and Fraud Detection, download Yhat’s “Data Science in Practice” whitepaper<\/a>.<\/p>\n\n\n\n Companies are no longer just collecting data. They\u2019re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists \u2013 the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.<\/p>\n\n\n\n In this article, we\u2019ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you\u2019re more drawn out to the creative side or interested in the strategy planning part of data architecture, there\u2019s a niche for you. <\/p>\n\n\n\n Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It\u2019s an interdisciplinary field that keeps the work varied and interesting.<\/p>\n\n\n\n Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.<\/p>\n\n\n\n <\/p>\n\n\n\n Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research \u2013 all depending on the field they operate in.<\/p>\n\n\n\n They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. <\/p>\n\n\n\n A data scientist\u2019s career starts with a solid mathematical foundation, whether it\u2019s interpreting the results of an A\/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. <\/p>\n\n\n\n Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses<\/a> and certifications that demonstrate their expertise and willingness to learn.<\/p>\n\n\n\n The average salary of a data scientist in the US is $156,363<\/a> per year.<\/p>\n\n\n\n A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.<\/p>\n\n\n\n A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.<\/p>\n\n\n\n To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps<\/a> to acquire these skills. <\/p>\n\n\n\n For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.<\/p>\n\n\n\n The average base salary of a data analyst is $76,892<\/a> per year.<\/p>\n\n\n\n Business analysts often have an essential role in an organization, driving change and improvement. That\u2019s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. <\/p>\n\n\n\n A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.<\/p>\n\n\n\n Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master\u2019s degree, especially in more complex industries or corporate environments.<\/p>\n\n\n\n Employers also value a business analyst\u2019s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.<\/p>\n\n\n\n A business analyst can earn an average of $84,435<\/a> per year.<\/p>\n\n\n\n The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. <\/p>\n\n\n\n A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.<\/p>\n\n\n\n Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it\u2019s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. <\/p>\n\n\n\n Database administrators earn an average salary of $77,391<\/a> annually.<\/p>\n\n\n\n Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).<\/p>\n\n\n\n Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. <\/p>\n\n\n\n Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.<\/p>\n\n\n\n Data engineers earn a whooping average salary of $125,180<\/a> per year.<\/p>\n\n\n\n A database architect\u2019s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. <\/p>\n\n\n\n Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.<\/p>\n\n\n\n If you\u2019re aiming to excel as a database architect but don\u2019t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don\u2019t forget programming languages – SQL, Python, or Java. <\/p>\n\n\n\n If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard<\/a>.<\/p>\n\n\n\n Data architecture is a very lucrative career. A database architect can earn an average of $165,383<\/a> per year.<\/p>\n\n\n\n A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. <\/p>\n\n\n\n As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.<\/p>\n\n\n\n Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K<\/a> per year, one of the highest-paying AI careers<\/a>.<\/p>\n\n\n\n Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. <\/p>\n\n\n\n They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.<\/p>\n\n\n\n This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. <\/p>\n\n\n\n A quantitative analyst earns an average of $173,307<\/a> per year.<\/p>\n\n\n\n A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.<\/p>\n\n\n\n If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. <\/p>\n\n\n\n Data mining specialists earn $109,023<\/a> per year.<\/p>\n\n\n\n Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data\u2019s context. <\/p>\n\n\n\n Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.<\/p>\n\n\n\n The average annual pay of a data visualization engineer is $103,031<\/a>.<\/p>\n\n\n\n The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.<\/p>\n\n\n\n When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. <\/p>\n\n\n\n Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.<\/p>\n\n\n\n Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.<\/p>\n\n\n\n Don\u2019t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the \u201cEasy Apply\u201d options, as you\u2019ll be competing with thousands of applicants who bring nothing unique to the table.<\/p>\n\n\n\n We answer your most frequently asked questions.<\/p>\n\n\n\n A degree is not a set-in-stone requirement to become a data scientist<\/a>. It\u2019s true many data scientists hold a BA\u2019s or MA\u2019s degree, but these just provide foundational knowledge. It\u2019s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.<\/p>\n\n\n\n Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.<\/p>\n\n\n\n It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.<\/p>\n\n\n\n To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you\u2019re acing problem-solving, critical thinking, and effective communication.<\/p>\n\n\n\n Since you\u2019re here\u2026<\/strong>Are you interested in this career track? Investigate with our free guide to what a data professional actually<\/em> does<\/a>. When you\u2019re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp<\/a> which will help you land a job or your tuition back!<\/p>\n","protected":false},"excerpt":{"rendered":" This post on real-world data science applications was originally featured on the Yhat blog. In our very first whitepaper, \u201cApplied Data Science,\u201d we translated the hype-y term of data science into the plain english definition of \u201cusing data to make better decisions, optimize processes and improve products and services.\u201d We also described the central goal […]<\/p>\n","protected":false},"author":10,"featured_media":2106,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[],"class_list":{"0":"post-2101","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/2101"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=2101"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/2101\/revisions"}],"predecessor-version":[{"id":43187,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/2101\/revisions\/43187"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/2106"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=2101"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=2101"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=2101"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=2101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"data](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2016\/08\/Collaborative_Filtering_in_Recommender_Systems.jpg\")
<\/a>
Recommender systems, also known as recommender engines, are one of the most well known data science applications. Recommender systems are a subclass of information filtering systems, systems that cut through the noise of all options and present users with just the subset of options they\u2019ll find appealing. The data being filtered can range from products on an e-commerce site to dating matches that appear as you search for \u2018the one.\u2019<\/p>\n\n\n\nGet To Know Other\tData Science Students<\/h4>
<\/a><\/a><\/a>
If you have ever applied for a credit card or a loan, you\u2019re likely already familiar with the concept of credit scoring. What you may be less aware of is the set of decision management rules evaluating how likely an applicant is to repay debts behind the scenes.<\/p>\n\n\n\n
You walk out of the store, arms full of groceries, only to realize that a torrential downpour began as you perused the produce inside. You struggle to retrieve your phone, check your favorite ride app and are dismayed to find…a 2.1x surge!? Welcome to your first lesson on dynamic pricing.<\/p>\n\n\n\n
\n\n\n\nIs Data Science A Good Career?<\/h2>\n\n\n\n
10 Data Science Careers To Consider<\/h2>\n\n\n\n
Data Scientist<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
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Data Analyst<\/h3>\n\n\n\n
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Business Analyst<\/h3>\n\n\n\n
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Database Administrator<\/h3>\n\n\n\n
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Data Engineer<\/h3>\n\n\n\n
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Database Architect<\/h3>\n\n\n\n
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Machine Learning Engineer<\/h3>\n\n\n\n
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Quantitative Analyst<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
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Data Mining Specialist<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Data Visualisation Engineer<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
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Resources To Find Data Science Jobs<\/h2>\n\n\n\n
Job Boards<\/h3>\n\n\n\n
Online Communities<\/h3>\n\n\n\n
Network And LinkedIn<\/h3>\n\n\n\n
FAQs about Data Science Careers<\/h2>\n\n\n\n
Do I Need A Degree For Data Science?<\/h3>\n\n\n\n
Does Data Science Need Coding?<\/h3>\n\n\n\n
Is Data Science A Lot Of Math?<\/h3>\n\n\n\n
What Skills Do You Need To Land an Entry-Level Data Science Position?<\/h3>\n\n\n\n