What is Scikit-Learn used for?<\/h2>\n\n\n\n
Scikit-Learn<\/a> is an open-source package for creating and evaluating machine learning models of all flavors in Python.<\/p>\n\n\n\n Scikit-Learn allows you to define machine learning algorithms and evaluate many different algorithms against one another; it also includes tools to help you preprocess your dataset. The Scikit-Learn includes a diverse cast of machine learning models including Support Vector Machines<\/a>, Random Forests<\/a>, K-means clustering<\/a>, and any model you want to implement yourself.<\/p>\n\n\n\n The real power of Scikit-Learn lies in its model evaluation and selection<\/a> framework, where you can cross-validate and perform various hyperparameter searches of models. You don\u2019t ever want to question whether you chose the best model possible for the job, but Scikit-Learn makes it easy to affirm that you did.<\/p>\n\n\n\n TensorFlow<\/a> is another open-source framework maintained by Google for prototyping and evaluating machine learning models with a primary focus on neural networks. TensorFlow is built to be available in numerous data science<\/a> friendly languages\u2014such as Python, Javascript, C++, Java, Go, and Swift\u2014and has community-built support for many other languages.<\/p>\n\n\n\n TensorFlow is heavily used by Data Scientists<\/a> and ML Engineers and is most commonly associated with neural networks, but in general, is highly optimized for any machine learning method that uses gradients (such as Boosted Trees). TensorFlow also includes a visualization tool known as TensorBoard<\/a> to track and compare your trained models.<\/p>\n\n\n\n The appeal of TensorFlow lies in its optimization and speed of neural networks. TensorFlow can run models on CPUs, GPUs, and even TPUs with an efficiency that few frameworks can match.<\/p>\n\n\n Pizon Shetu<\/p> Data Scientist at Whiterock AI<\/p><\/div> Read Story<\/a><\/p><\/div><\/div> Rane Najera-Wynne<\/p> Data Steward\/data Analyst at BRIDGE<\/p><\/div> Read Story<\/a><\/p><\/div><\/div> Nick Lenczewski<\/p> Data Scientist at Ovative Group<\/p><\/div> Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n Scikit-Learn and TensorFlow are both designed to help developers create and benchmark new models, so their functional implementations are quite similar with the key distinction that Scikit-Learn is used in practice with a wider scope of models as opposed to TensorFlow\u2019s implied use for neural networks.<\/p>\n\n\n\n Scikit-Learn implements all of its machine learning algorithms as a base estimator<\/a> and TensorFlow mirrors this terminology in its estimator<\/a> class. Both frameworks\u2019 estimators have abstract methods that are used by the framework to train and evaluate the estimator to ease head-to-head comparisons.<\/p>\n\n\n\n TensorFlow estimators and Scikit-Learn estimators are alike, but Scikit-Learn estimators are generally more flexible with other frameworks such as XGBoost<\/a>, while TensorFlow estimators are intended to be built using TensorFlow core functionality which is optimized for neural networks.<\/p>\n\n\n\n Scikit-Learn does implement some barebones neural network models, but off-the-shelf doesn\u2019t support more complex neural networks and the higher level of the customizability of TensorFlow.<\/p>\n\n\n\n In effect, Scikit-Learn often abstracts many of the details of the machine learning model away from the developer while the developer must implement details and inner-workings of their TensorFlow models. With this distinction comes a trade-off of speed, as the more flexible framework cannot achieve the performance of the specialized framework.<\/p>\n\n\n\n Scikit-Learn\u2019s generality makes it useful for comparing entirely different types of machine learning models against each other; TensorFlow\u2019s specialization enables under-the-hood optimizations, making it easier and more efficient to compare different TensorFlow and neural network models. For this reason, Scikit-Learn is often used to initially select the models you\u2019ll later improve in greater detail.<\/p>\n\n\n\n TensorFlow\u2019s availability in more languages and a greater focus on optimizations also makes it the preferred choice for deploying neural network models to production, as you can develop specifically for your target platform and squeeze out the greatest efficiency.<\/p>\n\n\n\n Since Scikit-Learn allows you to implement your own estimators, there\u2019s nothing stopping you from using TensorFlow within Scikit-Learn\u2019s framework to compare TensorFlow models against other Scikit-Learn models. This flexibility is extremely useful, as it allows you to determine whether to delve into the depths of TensorFlow models or to pursue a different Scikit-Learn model.<\/p>\n\n\n\n Both Scikit-Learn and TensorFlow are useful enough that they are likely to find a place in your development pipeline\u2014but you must be mindful to use them to their advantages.<\/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":" After reading an exciting paper or cleaning your data, what\u2019s the next step? You want to start building your machine learning models and testing them\u2014after all, that\u2019s the exciting part of machine learning. From prototyping new models to evaluating and ultimately deploying the best model(s), you need a consistent framework to keep track of your […]<\/p>\n","protected":false},"author":100,"featured_media":18986,"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":[1466],"class_list":{"0":"post-14628","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\/14628"}],"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\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=14628"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/14628\/revisions"}],"predecessor-version":[{"id":50107,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/14628\/revisions\/50107"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/18986"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=14628"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=14628"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=14628"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=14628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}What does TensorFlow do?<\/h2>\n\n\n\n
Get To Know Other\tData Science Students<\/h4>
![\"Pizon](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1651030560\/Student%20Success\/Pizon_Shetu.jpg\")
<\/a>![\"Rane](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1659153158\/Student%20Success\/Rane_Najera_Wynne.jpg\")
<\/a>![\"Nick](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1667235351\/Student%20Success\/Nick_Lenczewski.jpg\")
<\/a>Scikit-Learn vs. TensorFlow: How Do They Compare?<\/h2>\n\n\n\n
How can you use Scikit-Learn and TensorFlow together?<\/h2>\n\n\n\n
Is 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
Average Salary<\/h4>\n\n\n\n
Data Analyst<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Business Analyst<\/h3>\n\n\n\n
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Database Administrator<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
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Data Engineer<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
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Database Architect<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
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Machine Learning Engineer<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Quantitative Analyst<\/h3>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
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
Average Salary<\/h4>\n\n\n\n
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