Develop practical data science skills through Python, statistics, and visualization bootcamps. You’ll uncover patterns and insights hidden within complex datasets.
Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Become proficient in Microsoft Excel and go from beginner to pro with our hands-on Excel Bootcamp. Learn functions, formulas, Pivot Tables, macros, and time-saving techniques to conquer all the key skills needed to excel in Excel.
Learn how to use Tableau, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. This course will teach you how to connect to datasets, analyze and structure data, customize visualizations, and publish your work.
Learn how to use Power BI, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. Learn how to connect to datasets, analyze and structure data, customize visualizations, and publish reports.
Master the industry-standard tools and programming languages used by Data Analysts, including Excel, SQL, and Tableau, in this comprehensive classroom training program. Gain hands-on experience working on real-world projects and learn to organize, analyze, summarize, and visualize data to present actionable insights.
Learn the fundamentals of data analytics, including descriptive and inferential statistics, and how different industries and organizations use data for forecasting and decision-making. Explore statistical analysis and modeling, including statistical algorithms, theorems, and models.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Data science is an interdisciplinary field of study that involves applying various scientific processes, methods, algorithms, and techniques to large volumes of data with the goal of unearthing patterns and locating meaningful insights. This information is then used to help organizations make informed business decisions. This robust field is a meeting point of disciplines like information science, statistics, data analysis, and computer science. Data Scientists work with structured and unstructured data and create predictive models with the help of machine learning algorithms.
The term “data science” originated in 1974 when Peter Naur coined it as a synonym for computer science. Although the origin of the phrase is modern, the field predates its naming. Major figures have helped data science transform from early manual computing tasks into its current version, which draws from AI and other cutting-edge technology. Over 180 years ago, Ada Lovelace was the first person to conceive of how a set of annotations could perform calculations and become the first computer program. Other major players such as Alan Turing, John Tukey, and Karen Jones have helped this field transform into its current state. Today, Data Scientists employ the data science lifecycle’s five stages when encountering volumes of data. The first stage involves collecting data. Stage two pertains to data maintenance, including warehousing, cleansing, and processing. In the third stage, Data Scientists process the data, which entails mining, modeling, and summarizing the information. The fourth stage of the life cycle involves various types of data analysis. In the final stage, the findings from the prior four stages are conveyed in an accessible format such as a data report or a chart.
Because data science requires extensive knowledge of a range of fields and skills, including advanced mathematics, data analysis, and statistics, those with a background in data science have many high-paying career options across locations and fields. Additionally, those who are interested in pursuing a profession in which they’re presented with unique challenges to solve will thrive as Data Scientists. Each day presents new data challenges that require expertise to solve. Not only is data science currently in-demand, but positions in this field are expected to increase 28% by 2026, which is well above the national projected job growth in other professions. To keep up with the demand for qualified Data Scientists, most who are employed in this field can expect to make a six-figure salary. Additionally, they often have the flexibility to relocate from one city to another or even work remotely for their employer. This flexibility makes data science an appealing career path for those who are interested in fully exploring data trends.
Since data science plays an integral role in all industries in which data is collected, those with a background in data science can apply their curiosity to many fields long after their formal education concludes. Data science has applications in creating search engines like Bing, Yahoo, and Google. This field also has real-time applications in the transportation industry, helping driverless cars manage training data. Within finance, Data Scientists rely on data science to automate the process of calculating the risk of loss so they can make important organizational decisions based on this information. Data science is the tool that helps ecommerce websites like Amazon improve users’ experience by offering them personalized recommendations. Even those who work in healthcare apply data science knowledge when performing tasks like creating predictive models for diagnosing disease, analyzing medical images, powering virtual medical bots, and detecting tumors.
When deciding whether to enroll in a data science course, you may wonder what skills you’ll encounter during training. Although the content of each program varies depending on the educational provider, the course’s duration, and its level of difficulty, most data science programs offer participants training in core skills and programming languages they’ll need when pursuing a data science career. The following sections will take a closer look at several data science skills and tools you will likely encounter during your studies, including machine learning, data analysis, SQL, Python, and critical thinking.
When you enroll in a data science course, you’ll likely be introduced to machine learning. Machine learning is a form of data analysis in which analytical model building is automated. This branch of computer science and artificial intelligence involves using algorithms and data to mimic the learning patterns of a human, which becomes more accurate the more it learns. Eventually, the system can learn from data, spot patterns, and perform decision-making, all with minimal human intervention. Data Scientists learn machine learning to continue to build and train their data models to offer increasingly accurate, real-time predictions.
SQL, which is short for structured query language, is a programming language that Data Scientists use to store information on relational databases and communicate with the data that’s kept there. SQL is considered relatively easy to learn because it utilizes common English keywords for statements. In relational databases, information is stored in rows and columns in tables; each row and column represents a specific data attribute and its relationships between data values. By using SQL statements, data professionals can perform tasks like searching, retrieving, updating, storing, or removing information from databases. SQL has been the premiere data manipulation language since the 1970s. It’s a popular programming language that’s used in many types of applications. Companies like Instagram, Amazon, Netflix, and LinkedIn use this language for data modification, extraction, and management, and to perform data analysis. One of the main benefits of learning SQL is it integrates with various other programming languages, like Java.
Python is an object-oriented, interpreted programming language that’s commonly used to analyze data, automate tasks, and construct software and websites. This general-purpose language has a simple, readable syntax, which makes it relatively easy to learn. Data Scientists rely on this versatile language for tasks like natural language processing, data analysis and visualization, image processing, machine learning, computer vision, and deep learning. This language’s many packages and libraries such as Pandas, NumPy, and Matplotlib are considered staple tools in the data science lifecycle.
When you enroll in a data science class, another skill you’re likely to encounter is data analysis. Data analysis involves gathering, organizing, cleaning, and studying data so that important insights can be taken from the information. When analyzing data, Data Scientists are attempting to figure out what the data is trying to communicate and use this information to make decisions. Data analysis is an important aspect of the data-driven world. With it, organizations can get the most out of the data they collect, which can lead to more sound decisions and optimized processes. Those with the skills to transform raw data into meaningful insight have a competitive edge over others because they have the power to spot promising opportunities, reduce risk, and improve overall performance.
In addition to the hard skills you’ll acquire when studying data science, you’ll also have the opportunity to develop soft skills through your coursework. One such soft skill is critical thinking. In order for a Data Scientist to be able to fully and accurately analyze a problem, they must be as objective as possible so as to correctly frame questions and evaluate whether their findings are useful in helping their organization progress toward a viable course of action. These professionals must be able to objectively analyze data in the interpretation phase before they offer an opinion on their findings. This requires deeply examining the source of the data, studying the problem from every angle possible, and maintaining a curious mindset, else bias will creep into the final output.
The difficulty associated with learning data science is largely objective. While some learners approach their data science studies drawing from prior training with Python, SQL, data analytics, or machine learning, others may lack this knowledge base and have to start from scratch when acquiring data training. While some learners may be able to learn fundamental data science concepts in as little as six months, it can take others several years to acquire expertise in this field.
One of the contributing components to the difficulty of learning data science is that it requires expertise in a range of skills, including industry-specific knowledge pertinent to the sector in which it will be applied. Data science skills and tools vary from healthcare-related positions to financial roles, which means it’s imperative for Data Scientists to have more than just a solid background in data science; they must also be familiar with the most current best practices and software used in their industry of focus.
One of the most challenging parts of learning data science is keeping up with this field’s rapid, constant evolution. To succeed as a Data Scientist, you must stay on top of current innovations and industry-specific tools. Over the past decade, this field has transformed significantly due to innovations like machine learning, artificial intelligence, and machine learning. Aspiring Data Scientists must stay current with these innovations and be prepared to embrace future changes. Another challenge to studying data science is that each field in which data science is used has its own unique tools. It’s the responsibility of the Data Scientists to learn the tools needed in their field, whether it’s Tableau or Microsoft Power BI. Additionally, to succeed in data science, it’s essential to be well-versed in handling data. This requires extensive training in data mining and analysis, as well as how to communicate data findings to relevant stakeholders.
Data science is a broad, encompassing field, which means that the length of time required to learn it is dependent on a host of factors. Those who already come from a programming-heavy background or have prior experience with data, AI, or machine learning techniques may find it easier to learn data science than those who are new to these fields. Most individuals can learn data science basics in between six months and one year, depending on their educational background. However, to become truly proficient in data science will likely require several years of study and real-world training.
When studying data science, it’s important to consider that the training you’ll require is dependent on the field in which you hope to apply your skills. If you’re interested in pursuing a junior position as a Python Developer or Data Analysts, you may only need to train for a few months to learn the basics. Enrolling in a live bootcamp or certificate in data science such as that which is offered by Noble Desktop, is an excellent way to expedite your learning process and ensure that you’re acquiring the skills needed to pursue a data career. These programs take just several weeks or months to complete and offer a more structured and hands-on learning environment than self-guided data science coursework.
One important decision you’ll need to make when you enroll in a data science class is whether to study in-person or online. Both training options have their unique strengths and considerations. In-person data science programs are available for beginners as well as those who already have prior experience in data science and want to master advanced techniques. During in-person coursework, students have the chance to connect with an expert instructor who has industry experience in data science. Classrooms provide learners with computers that have the most current software and tools for data science already installed. These classes are also a great way to connect with other learners on a similar educational path, which can be a valuable way to network and benefit from a sense of community while you study. When opting for in-person training, it’s important to remember that these classes require students to commute to and from class for regularly scheduled meetings. This training format, therefore, may not be possible for those who don’t have access to reliable transportation or who live in remote areas.
An excellent alternative to in-person training is live online study. Just like in the classroom setting, participants in live online data science classes can take advantage of the same learning benefits as those who study in-person. All learners have access to a live instructor who’s leading the class in real-time and is available to field questions, clarify difficult concepts, and even share your screen (with permission) if you need additional help. Live online classes take place using a teleconferencing platform like Zoom, which means that all coursework can be completed remotely without ever having to commute to a training facility. If you’re interested in pursuing live online data science coursework, keep in mind that you’ll still need to attend classes that occur for regularly scheduled meetings. This might mean that you will need to take time off of work or adjust your schedule accordingly to make it to meetings.
The most flexible training option available in data science is on-demand coursework. On-demand content such as tutorials, videos, or lessons, are pre-recorded and placed online. Unlike live online or in-person data science programs, which take place in real-time, on-demand content is asynchronous, which means that students can opt for when to complete their lessons. This type of training also provides students with control over their learning pace. Whereas classes that meet in real-time are guided by an instructor who decides how much time to spend on each skill or lesson, self-paced content allows the learner to decide when to pause lessons to take notes, rewind them to revisit complicated material, or even rewatch them completely to ensure maximum retention. Asynchronous data science training can be completed from any location with a stable internet connection. Learners can opt to fully immerse themselves in a Python for data science program over the course of a weekend or devote fifteen minutes each night after work to learning core SQL skills. Another appeal of on-demand content is cost. Since the content is pre-recorded, it’s often a much more affordable learning alternative than live training. However, it’s important to consider that no instructor will be present in this type of data science training. Data science is a broad, interdisciplinary field that requires mastering a range of related skills and tools. Some learners may find it challenging to fully grasp complex data science concepts in the asynchronous format since they will be on their own to find answers to questions. For this reason, some students opt to start their data science learning journey with on-demand content but ultimately switch to live coursework such as a bootcamp or certificate program, to learn more advanced data science skills.
In addition to live data science coursework and self-paced study options that are available from educators around the globe, you can also learn about data science for free online by exploring free resources like blogs, online tutorials, and video content. Coursera currently offers free data science classes that cover skills like how to apply AI, Python, computer science, and math when handling data. Harvard University’sData Science: Linear Regression provides free, introductory-level content on how to work with linear regression in R. Students can also learn the basics of data mining and data science by enrolling in UC Irvine’sIntro to Analytic Thinking, Data Science, and Data Mining, which is part of its four-course data science fundamentals specialization. Students interested in exploring the intersection of math and data science can do so in Duke University’sData Science Math Skills program, a 13-hour, introductory-level class that provides training in probability theory and Bayesian probability.
You can also learn more about data science by readingNoble Desktop’s online learning resources, which include dozens of articles on the tools, software, and best practices currently employed in this field. Noble also has a YouTube channel where topics like Python, SQL, and data science are explored.
If you’re interested in learning data science, you may wonder what skills are related that you can learn at the same time. Most individuals who pursue data science rely on a set of related skills, including computer science, mathematics, statistics, and information technology. Ensuring you have a solid background in mathematics, including knowledge of algebra, calculus, and probability, is a good place to start. Aspiring Data Scientists who don’t have a strong programming background or who are new to working with data may also wish to brush up on commonly used data science languages and tools like Python, SQL, and relational databases. Object-oriented programming languages such as Java, C, or Python, are integral to many data science career paths. Python is considered a good “first language” in programming to acquire because it’s relatively easy to learn and ubiquitous in data science. Since Data Scientists typically have to use structured query language (SQL) to write queries to communicate with relational databases and retrieve the information contained in them, learners also often find it useful to study this language alongside data science.
Since both data analytics and data science prepare students to work with large volumes of data, these two disciplines are often taught alongside one another. Data analytics involves gathering, organizing, transforming, and evaluating data so that conclusions can be drawn about this information and predictions can be offered that will drive the organization’s decision-making process. While both data science and data analytics require managing data to derive insights, these fields differ from one another in several ways. Data science entails working with data to create models capable of predicting various outcomes, whereas data analytics is more concerned with how past data can be leveraged to shed insight into current decisions.
Data science is still one of the most in-demand skill sets across industries and professional roles. The US Bureau of Labor Statistics predicts that approximately 11.5 million jobs will be opened in data science by 2026. Those who have a background in gleaning insights from big data can put their skills to use in many sectors in Washington, DC. The following sections will briefly explore how data science is used in popular local industries like cybersecurity, healthcare, and telecommunication, as well as its range of applications for nonprofit career paths.
According to Cyber, Washington, DC was ranked the best city in the US for cybersecurity jobs in 2021. Those with a background in cybersecurity are hired to protect networks, programs, and systems from a range of digital attacks. These threats attempt to access, alter, and destroy sensitive information, disrupt business processes, or use ransomware to extort funds from users. Cybersecurity professionals with a background in data science are hired to safeguard systems from future threats. They analyze security logs, which store details about login attempts, system errors, file transfers, and network activity, and create machine learning models capable of detecting threats in real-time. The nation’s capital offers competitive pay rates and ample job opportunities for those who have a background in cybersecurity because many top government agencies are headquartered in the city.
Another popular industry in which data science skills are currently in-demand in Washington, DC is healthcare. The city and surrounding area are home to 16 hospitals and medical centers, as well as a range of research centers like the National Institutes of Health. Within the healthcare sector, data is an essential component of medicine. Nearly one-third of the global data volume is related to health information. This data includes e-health records, information from clinical trials, and disease registries. Those who have data science training can put their skills to use in the city’s healthcare industry by identifying anomalies in patient data or designing wearable health devices. Additionally, Data Scientists assist with disease prevention, predictive analytics and modeling, pharmaceutical or vaccine development, and digital security-related tasks.
There are currently over 15,000 nonprofits located in Washington, DC. The city’s nonprofit sector employs more than 258,000 residents. With the growing importance of big data over the past several years, nonprofits have been increasingly turning to data science to streamline operations and ensure they have the greatest impact possible. Data Scientists who work in the nonprofit sector create predictive models capable of spotting data patterns that may have gone otherwise undetected. With the help of machine learning models, nonprofits can study past data to make predictions about how many people may require food assistance in a month. This can help with resource or budget allocations. Those who work in the city’s nonprofit sector also turn to data science to evaluate which campaigns are connecting most with their target audience and which can be discontinued. Data science insights also shed light on how a nonprofit can improve its website to ensure users remain engaged and potential donors can easily navigate their space to make charitable donations.
Telecommunication is a broad term that describes how information is exchanged over vast distances using a transmitter and receiver. Communications infrastructures and information-transmitting technologies like the internet, cell phones, fiber optics, and satellites are just a few examples of telecommunication. This sector creates huge amounts of data every minute, including information on call details, customer information, and network information. It’s up to Data Scientists in DC to manage that information. Because telecom companies must handle huge amounts of data every day, they turn to Data Scientists to provide a comprehensive view of customer profiles, traffic and usage patterns and locations, and real-time analytics updates.
If you live in Washington, DC and are interested in a data science career, many professional options are available with competitive salaries. The following sections will take a closer look at several popular career paths in data that are currently available in DC, as well as the basic job descriptions and corresponding salary ranges for each role.
Data Scientists are hired to gather, analyze, and interpret data to assist their organization with its decision-making process. These professionals create algorithms that tell computers what to do and develop machine learning models. Data Scientists who work in the DC area earn approximately $144,000-$154,000 a year.
In Washington, DC, Data Analysts are hired to study datasets to provide answers to questions or solve problems. These professionals are hired to work in a range of industries within the city, from government roles to healthcare to criminal justice. The insights they discover are presented to internal or external stakeholders like those in senior leadership roles to help inform the organization’s decision-making process. Those who focus on data analytics in DC make a yearly salary of about $78,000-$88,000.
Data Architects are IT professionals who study and evaluate their organization’s data infrastructure. Their daily tasks involve database planning and devising methods to better manage and store data for users. They also develop procedures to make sure data is accessible and kept secure. Because of how many organizations rely on data, Data Architects are in-demand in DC in many industries, including finance, healthcare, entertainment, and IT. DC-based Data Architects can expect to make around $156,000-$166,000 annually.
In Washington, DC, Machine Learning Engineers are hired to perform tasks like researching and crafting the AI systems that are used for machine learning. They draw from a background in math, statistics, computer programming, model validation, data processing, and data visualization. Machine Learning Engineers in the city make a yearly salary of $135,000-$145,000.
If you live in the DC metro area, you can search for data science classes in Washington DC to find coursework that’s in line with your learning needs.
DC residents can receive hands-on data science training in Learning Tree International’s
Introduction to Data Science, Machine Learning & AI. This short class covers the foundational techniques and skills necessary to succeed in data science. Coursework begins with an overview of the data science lifecycle. Students then acquire technical skills like how to create machine learning and artificial intelligence models, work with Python and its libraries to preprocess unstructured data, and perform data analysis and visualization. This class also teaches learners machine learning algorithms, including decision tree classifiers, linear regression, and clustering, and how each of these techniques can be applied to real-world problems. This program is available in-person in Herndon, Virginia.
Noble Desktop’s||CPN411|| program is a great option to consider if you’re looking to learn how to analyze and manipulate data and gain the training needed to pursue an entry-level data science or Python engineering role. Participants in this hands-on, live online course explore Python’s data science libraries, learn how to clean data, read and write database queries, and automate repetitive tasks with Python. Instruction is provided on how to create machine learning models and evaluate their performance. By the end of this comprehensive program, you’ll be familiar with how to use NumPy, Matplotlib, Seaborn, and Dash Enterprise to create and present dashboards and data visualizations. In addition to comprehensive live instruction, all learners receive six one-on-one mentoring sessions that can be used for professional development purposes or to revisit complex class material. Tuition includes a free course retake for a full year.
Noble Desktop also offers a Python for Data Science Bootcamp. This is a great option to consider if you’re interested in acquiring hands-on training in how Python can be used for data science to manipulate databases and analyze data. In this bootcamp program, you’ll have the chance to work with core data science libraries like NumPy and Pandas for data analysis. Instruction is provided on how to use scikit-learn and other machine-learning packages to create predictive models. By the end of this program, all participants will be able to use Python to automate repetitive tasks, including formatting, updating, and aggregating data. This class is intended for those who want to apply for entry-level Python engineering or data science careers. All participants also receive a supplemental one-on-one mentoring session in which they can revisit complicated class material or ask professional development-related questions.
Flatiron School’sData Science Bootcamp is available for DC residents who wish to explore career options in data science. This intensive class prepares learners to work with key data science concepts and skills such as statistics, SQL, Python, and machine learning. Those enrolled use Jupyter Notebooks for Python coding and study the statistics involved with machine learning. Students complete several data science projects during this program and also receive job support and career coaching as they study. This beginner-friendly course comes with a money-back guarantee. Instruction is provided in-person in DC.
Python for ||CPN415||, which is available from Practical Programming, is a fast-paced course that covers various practical Python applications in the field of data science. Those enrolled explore foundational programming concepts, including loops, objects, and functions. They also work with different types of data, like lists, strings, and integers. Those enrolled learn about how to work with conditional statements to selectively alter control flow while programming. Those enrolled in this hands-on program become familiar with how to work with Python libraries Pandas and NumPy to analyze tabular data and use Matplotlib to visualize data. By course completion, students will be able to use scikit-learn to predict outcomes. This class includes a free course retake for one full year.
DC residents can also learn about data science by enrolling in General Assembly’sData Science Bootcamp. This intensive course provides intermediate-level instruction for those interested in learning how to think, communicate, and solve problems like an Analyst. Those enrolled in this course work with tools and software like Tableau, SQL, Excel, Python, and Power BI. This program is available live online and in-person in Washington, DC. General Assembly also offers a Data Science Short Course, which is available to those who live in the DC area and are interested in learning how Python can be used to create robust predictive models. Students in this hands-on program receive real-world training in how to handle machine learning problems.
In Georgetown School of Continuing EducationCertificate in Data Science, participants learn the basics of data science. Instruction is provided on topics like data storage and data sources, statistics, visual analytics, machine learning, and data wrangling. All learners complete a capstone project, which can be included in their professional portfolio and shared with prospective employers. This course is taught in-person in Washington, DC, and is open to learners at all levels. This provider also offers Python Basics for Data Analysis, which offers introductory-level instruction in how to get started working with Python for data analysis. Coursework covers how to scrape webpages, use alternative data sources, and calculate descriptive statistics. Students also become familiar with Pandas, NumPy, and Matplotlib. This short course is available in-person in DC.
Would those at your workplace benefit from learning more about data science? If so, let Noble Desktop help. Noble can provide corporate and online data science training for DC residents. Instruction is available on topics like Python machine learning, SQL, automation, and data science. Coursework can be provided onsite at your location or in the live online training format using a teleconferencing platform like Zoom. Study options can also be customized to ensure that your employees get the most out of their training and that their skill gaps are met. If you’re interested in providing your workers with the data science skills needed to stand out from other data professionals in DC, Noble’s expert-led instruction is a great learning option. Training can be scheduled on weekends or at night to accommodate your employees’ working schedules. Additionally, you can enroll those at your workplace in open-enrollment data science courses. Vouchers can be purchased in bulk and for a discounted price.
To find out more about the exciting data science training options available from Noble Desktop, don’t hesitate to email Noble today to learn more.