Since computers have entered everyone’s lives, data science has become the go-to method for using data to extract meaningful insights. By combining statistical analysis, computer science, and domain expertise, data scientists use data to find patterns and solve various problems. Most data scientists follow a process of collecting, cleaning, processing, and visualizing large datasets to support informed decision-making. Even though data science became popularized in the late 20th century with the advancement of computing technology, data science has become a leading field in the modern world.
Data science predates the invention of computers, with many early adopters utilizing statistical models to solve business and scientific problems. However, this long process of doing it by hand led to the development of modern practices. By the late 1990s, computers began using more advanced databases and complex analyses. Then, as computers began entering homes in the early 2000s, digital storage expanded vastly. Organizations realized the potential of harnessing this data to learn how to better attract their customers. This massive growth led to the development of specialized tools and techniques, including machine learning and data visualization, that further cement data science as a transformative field in modern times.
What Can You Do with Data Science Training?
Since data science is a highly technical discipline, data science offers various tools to tackle complex problems and uncover insights in new ways. By pursuing training in data science, you can use these tools in all aspects of your life. On a professional level, data scientists build predictive models to forecast trends in customer behavior or financial market fluctuations. Using machine learning algorithms, they can create systems that learn from consumers and recommend products or content based on behavior, like on Netflix or Amazon. These tools and skills make data more accessible and actionable for decision-makers. These projects drive efficiency and help organizations innovate and stay competitive.
Yet those who start learning data science quickly realize how useful it can be outside of a professional sphere. Adopting data science into your personal life can help you change your behaviors. Fitness buffs can utilize data science to track their workouts. This may involve collecting time on runs as you train for a marathon or tracking weight to monitor weight loss or muscle gain. Collecting this data allows you to identify patterns in your fitness journey and make educated decisions to see a different outcome. Others may use data science skills to track their finances to help them save or fix spending habits. Garden enthusiasts can even benefit, as you can track when you plant certain seeds, when they are produced, and how much they yield to help determine the best time to plant seeds in your garden.
What Will I Learn in a Data Science Class?
Data science has various applications that can change your professional and personal life. Now that you know how data science can help you, you need to know how to start learning data science. The best and quickest way to ensure you learn everything you need to know is to enroll in a data science course. Instructors design data science classes to cover various topics. This gives students a well-rounded education for their careers after the course. That said, courses can vary in content depending on the school and the instructor. As you begin your search for the perfect data science course for you, there are several key skills you need to learn in the class.
Data Analysis and Visualization
As one of the main parts of data science, data analysis and visualization allow you to examine raw data, identify patterns, trends, and anomalies and effectively communicate those findings. Visual tools like graphs, charts, and dashboards display these findings. Without these two skills, you are imputing data and hoping for information to pop out at you. With these two skills, you build the foundation for data science and ensure that data insights are actionable and accessible to a diverse audience.
Programming
While you may not think programming has a place within data science, you may not realize how versatile certain programming languages are. When dealing with large amounts of data, you may want to automate tasks or implement algorithms to make it easier. By utilizing Python and R, you can create code that does these things for you. These flexible programming languages have multiple data manipulation and statistics libraries, making them ideal for data science. Enrolling in a course that covers these programming languages, you learn the tools to clean and preprocess data, perform complex calculations, and build scalable solutions. While it may seem difficult and unnecessary initially, having strong programming skills on your tool belt is indispensable for solving complex problems and exploring data science applications.
Database Management
Databases are another key component of data science as they allow data scientists to store, organize and retrieve vast amounts of information. When looking at courses, you will want to ensure they cover tools like SQL and NoSQL databases, as these allow professionals to interact with large datasets. These tools allow you to access the precise data needed for analysis when you need it. Database management is another foundational skill critical for maintaining data integrity and enabling seamless workflows no matter what dataset you are looking through.
Machine Learning
Machine Learning is a modern data science skill that empowers systems to learn from collected data and make predictions without explicit programming. It has its place in almost every industry, and people encounter it daily without even realizing it. With machine learning, you will train algorithms on various datasets to recognize patterns, classify the information, or forecast trends. A strong foundation in this area allows data scientists to create intelligent systems capable of solving complex, dynamic problems.
Problem-Solving
While you will primarily be looking for courses that cover technical skills, it is important to note that they will cover more skills than those. When dealing with large datasets, you must know how to effectively problem-solve. At any moment, something could go wrong within the database, and you must devise a way to fix it. However, data science and problem-solving go beyond the personal level. At its core, data science developed to solve problems. Whether it's optimizing a supply chain, identifying inefficiencies in a system, or uncovering insights to drive innovation, problem-solving is the ultimate goal. This skill involves critical thinking, creativity, and a systematic approach to breaking down challenges into manageable components. By learning how to approach problems, data scientists can apply their technical skills effectively, ensuring meaningful and impactful results across projects.
How Hard is It to Learn Data Science?
After seeing the skills almost all data science classes cover, you may wonder how difficult it is to learn data science. Several of those skills prove difficult on their own to learn. On the surface, data science involves several different disciplines, including mathematics, statistics, computer programming, and AI. While these areas may have challenges one must overcome, data science remains one of the easier technical skills to learn. Difficulty will depend on several factors, like previous experience, learning goals, and the depth of learning you want to achieve. The foundational skills like data analysis, visualization, and database management can be easily mastered with consistent practice. Programming and machine learning take longer, as they have a steep learning curve. However, within data science, you will mostly work with Python, one of the easier programming languages to learn. If you have previous programming experience, you will have an easier time mastering these skills. However, any learning journey can become easier. When you enroll in a data science course, you gain access to data science professionals who can help you tackle the concepts you are struggling with.
What Are the Most Challenging Parts of Learning Data Science?
All skills have challenges one must overcome during learning; data science is no different. The area that most students struggle with is programming. It is learning a new language that communicates with computers rather than others. It takes time, dedication, and practice to become skilled in programming. Luckily, data science only utilizes a few programming languages instead of most. Plus, the one commonly used is Python, which has quickly become one of the easier programming languages to learn because of its simple and versatile syntax. Once one becomes comfortable with Python, one can start introducing R, which is slightly more difficult. Other students find the mathematical concepts difficult to grasp. Data science utilizes multiple formulas and algorithms, making it difficult to keep track of, especially those who aren't mathematically inclined. However, with practice, these formulas and algorithms become secondhand.
Yet, with all of the challenges, how do students overcome them? Many students will break down the learning process into smaller, manageable steps to overcome each challenge. This allows them to focus on the more manageable foundational skills before jumping into the advanced topics. Some students even leverage other resources like courses, mentorship programs, or interactive projects that will provide efficient guidance while working through the difficult topic.
How Long Does It Take to Learn Data Science?
You know the skills involved with learning data science will not be the easiest, and you may encounter some bumps along the road, but how long should you expect this learning journey to take you? Most individuals can master the fundamentals of data science in six months or less, which makes it on the faster side of skills to learn. You can easily jump into a new data science position in less than a year. However, the time it takes you to learn can vary depending on your previous experience. Suppose you are coming into data science from an analytical position. In that case, you may already have many of the foundational tools and programs mastered and just need to spend time learning the more difficult skills. You will also want to consider how you plan to learn data science. Enrolling in a course can hone these skills in several weeks to a few months. However, if you opt for self-guided learning methods, it may take you several years. Even though it may only take a few months to grasp the basics, it can still take years of practice to fully master the skill set, including programming and machine learning. If you want to jump into a new career soon or start implementing data science into your life immediately, enrolling in a bootcamp or certificate course is the best way to ensure you promptly learn the most important skills.
Should I Learn Data Science in Person or Online?
So, you've settled on learning data science through a course. That still leaves the door open to several different options. With the internet becoming ingrained in everyone's daily life, more training formats have become available in recent years. Your first thought may be of the traditional learning method of in-person training. With this method, you will meet with fellow students at a specific place and time to receive training. The instructor will take the class through the material and work with everyone individually to help them through difficult concepts. This traditional format allows for easy collaboration and immediate feedback. It also provides a structure that lets you stay focused and engaged with the content. Choosing this option may be best for those who have been out of a classroom for some time or have trouble concentrating when they are at home. It is also important to remember that in-person training does require you to commute to a specific location, which can cause inconveniences for those with a busy schedule or who live in areas without accessible programs.
Live online training eliminates the commute and opens the virtual door to many other remote classes. Students who opt for this learning format still receive the structure of a traditional in-person class without the commute. Students will still meet at a designated time and learn directly from their instructor; however, they can complete the course wherever it is most comfortable. They will still benefit from direct interaction with their instructor and classmates through a video conferencing tool. You need a stable internet connection to attend these classes. Some may struggle to concentrate outside a tradition.
Your options don't stop there, though. You can also enroll in a course that allows you to work at your speed, and when you have the time. Asynchronous classes provide students with the most flexibility when it comes to learning. Students will gain access to pre-recorded lectures and materials when they enroll, and they can complete the modules in a way that fits into their schedule. If you have a busy schedule filled with personal or professional obligations, taking a self-paced learning opportunity may provide the ideal way to still learn data science. It is important to note that students who choose this option will learn independently and will not always have access to an instructor. You may need to answer your questions as they arise, or you may have to wait several days to weeks to receive a response from an instructor. You will also miss networking opportunities from the other two learning formats. Success in asynchronous learning often depends on strong self-discipline and seeking additional support when needed. Each format offers distinct advantages, so the best choice depends on individual learning styles, goals, and logistical needs.
Can I Learn Data Science for Free Online?
With the various options for training available, you may not be in a position that allows you to enroll in a data science course. Or you may not be ready to commit to data science and still want to ensure it is the right way for you. You don't need to give up your learning journey. You can utilize the numerous online free resources to explore data science basics. Platforms like Noble Desktop's YouTube channel and other educational sites offer tutorials, workshops, and beginner-friendly content on Python programming, data visualization, and machine learning fundamentals. These resources are a great starting point for gaining a general understanding and experimenting with data science concepts. However, developing professional-level skills requires a more structured and comprehensive approach. Advanced topics need guided instruction, hands-on practice, and real-world projects. Free resources rarely provide these. For those aiming to pursue data science as a career, investing in formal training is often the best way to acquire the depth and expertise employers expect.
What Should I Learn Alongside Data Science?
Before applying for jobs after completing your data science training, you may want to consider what other skills you need to be proficient in these positions. While it is great to have your skill set focused in one area, every job requires individuals to have other skills. One such skill is statistics. This provides a foundation for making data-driven decisions. While data science relies on data to make decisions, understanding statistical models helps assess patterns, identify trends, and make predictions more accurately. Another area many data science professionals can develop skills is business acumen. Understanding business acumen helps data scientists see the bigger picture. It lets them align their analysis with business goals and communicate their findings.
Looking through positions, you may realize that most require strong communication skills. Since you must share your insights with several team and company members, you must have strong written and verbal communication skills. Not everyone easily understands technical insights, and effectively communicating in a way that makes sense to everyone ensures that your insights do not get lost or misunderstood.
Industries That Use Data Science
Portland reigns as Oregon's largest city by straddling the Columbia and Willamette rivers. Over the last decade, its population has grown by 13%, job growth by over 1%, and many industries have seen almost 5% growth. With this expansion expected to continue, Portland is a great location for any data science professional-looking to start their career. With every industry starting to depend on data to drive decisions, the demand for data science professionals has risen.
Technology
Rankers have placed Portland in the top 10 cities for tech jobs, making it a go-to city to pursue tech. While you commonly think of programmers and developers jumping into this industry, they still rely on data scientists to help them make software that consumers want. Data science is key to the city's tech scene. It's vital in software development, AI, and cybersecurity. Portland is home to major tech companies such as Intel and a variety of smaller firms specializing in cloud computing, app development, and data analytics. Companies use data science to optimize user experiences, analyze vast customer data, improve machine learning algorithms, and predict market trends. Using data science tools and techniques, tech companies can drive innovation, improve product development, and better serve their customer base.
Biotech and Healthcare
In recent years, Portland has emerged as a center for new healthcare and bioscience companies. While healthcare centers call the city home, many pharmaceutical companies have also moved to Portland. Helping drive medical innovation and drug development lies data scientists. When it comes to drug studies, medical professionals need a way to track the effects and outcomes of the drugs. This is where data scientists shine. They can quickly collect the data from these trials and visualize common side effects or outcome patterns. Combining machine learning and data visualization tools lets researchers find hidden trends in massive datasets. Data science is critical in improving patient care, advancing medical research, and optimizing hospital operations. It is commonly used for predictive analytics, identifying patterns in patient data to predict disease outbreaks, improve diagnoses, and personalize treatment plans. As the city invests in healthcare innovation, data science will remain central to driving advancements in clinical and research settings.
Machinery
Portland's machinery industry relies on data science. It includes firms that make, automate, and precision-engineer machinery. They use data science to optimize production and maintain equipment. Companies in this sector use data science to track machine performance, reduce downtime, and improve the efficiency of manufacturing operations. Businesses can use Internet of Things (IoT) sensors and predictive analytics to monitor machinery in real-time. They can then predict failures and schedule maintenance before they occur. Data science is also crucial for optimizing supply chains and managing inventory, ensuring that the right parts are available when needed. This reduces costs and enhances production timelines, making Portland's machinery sector more competitive nationally and globally.
Cleantech
People know Portland for its commitment to sustainability. The cleantech industry is a natural extension of this ethos. Companies in the city are leveraging data science to drive innovations in renewable energy, waste management, and water conservation. Companies use data science in cleantech for many purposes. These include optimizing energy use, monitoring environmental impacts, and improving renewable energy, like solar and wind power. Cleantech firms can analyze weather, energy use, and system data. They can then predict energy demand, cut waste, and improve green tech. Portland's emphasis on sustainability has led to data-driven solutions in urban planning, where data science helps design smarter, more efficient cities.
Data Science Job Titles and Salaries
With almost every industry in Portland benefitting from data science, there are countless career opportunities for those with that skill set. No matter which industry you want to enter, there are various positions that you can pursue.
Data Scientist
Data scientists extract meaningful insights from complex datasets to help inform business decisions. They will use machine learning, statistical analysis, and data visualization to help identify patterns. They often develop predictive models, optimize processes, and create data-driven strategies that enhance operational efficiency. In Portland, a Data Scientist can earn an annual salary of around $135,000, depending on experience and industry.
Financial Analyst
Financial analysts apply data science skills to analyze financial trends, forecast future market behavior, and help organizations make informed investment decisions. They utilize Excel, Python, and specialized financial modeling software to collect, clean, and analyze financial data. Their work involves producing reports, assessing risks, and providing insights that drive strategic financial planning. This role requires a strong grasp of finance and economics. In Portland, financial analysts typically earn around $80,000 annually, with salary variations depending on the specific sector and the complexity of the role.
Data Engineer
Data engineers craft the backbone of information flow from data pipelines to analysis-ready insights. They Architect systems to handle massive datasets, collaborating with scientists and analysts. Their expertise ensures seamless access to crucial data, empowering organizations to extract valuable knowledge efficiently and reliably. Data engineers must be proficient in SQL, Python, and data architecture. They should also have experience with cloud tech and big data platforms like Hadoop or Spark. Data engineers in Portland can expect to earn around $110,000, with salaries varying based on experience and the role's technical demands.
Machine Learning Engineer
Machine learning engineers design, build, and deploy machine learning models. These models automate tasks, improve decisions, and enable predictions in organizations. In Charlotte, they often work in finance and healthcare, developing algorithms for credit scoring, fraud detection, patient risk assessment, and more. Machine learning engineers use skills in programming (Python or Java), data modeling, and algorithm design to build robust, scalable systems. In Portland, machine learning engineers command salaries near $125,000. Industry and expertise shape exact figures, with top talent earning more in competitive sectors.
Data Science Classes Near Me
After looking into the various industries and positions you can pursue with data science training, the next step is deciding on the type of training you want to enroll in. Enrolling in an in-person data science class is the best and fastest way to ensure you're prepared for the position you want. You can utilize Noble Desktop's Classes Near Me tool to help find the perfect in-person data science course or other live online options that may better fit your schedule.
Noble Desktop has several options for those interested in data science. The Data Science & AI Certificate is an extensive program that covers a wide range of skills needed for a career in data science, including data analysis, machine learning, and artificial intelligence. Students learn to use tools like Python, SQL, and Tableau to analyze large datasets, build predictive models, and implement machine learning algorithms. The Python for Data Science Bootcamp is for Python-focused learners. It teaches data analysis with Python. It covers basic syntax and advanced topics, like using Pandas and Matplotlib. The Data Analytics Certificate provides an overview of data analytics. Students will focus on developing strong data visualization skills by using programs like Tableau. They will also learn statistical analysis and how to create dashboards to communicate their insights. These courses are available in-person and live online, providing flexibility for students based on their location and preferences.
Oregon State University offers an Online Data Analytics Bootcamp designed for those who prefer a fully online learning experience. This bootcamp provides a deep dive into data analytics, covering everything from data wrangling and visualization to applying statistical techniques to solve complex business problems. The program also includes practical experience with Python and SQL, two essential tools for any aspiring Data Analyst or scientist. The bootcamp structures itself to be immersive and hands-on, ensuring students can apply the concepts they learn to real-world scenarios.
New Horizons offers a specialized course on Using Data Science Tools in Python. This course is a great option for students who want to focus specifically on Python, a key programming language used in data science. It covers Python programming fundamentals and introduces students to important data science libraries like NumPy and Pandas. Students learn how to clean and manipulate data, visualize it, and use Python to perform basic data analysis tasks.
The Tech Academy offers a Data Science Bootcamp for those interested in a comprehensive, immersive learning experience. This bootcamp teaches students all the essential skills needed to become a Data Scientist, including data analysis, programming, machine learning, and working with large datasets. Students learn to use Python, SQL, and machine learning frameworks like TensorFlow and scikit-learn. The project-based program allows students to apply their knowledge through hands-on projects, demonstrating their abilities to future employers. This bootcamp is ideal for those looking to transition into data science quickly and efficiently, with the added benefit of personalized mentorship throughout the program.
At the University of Oregon, the Data Analytics Boot Camp is another excellent option for those looking to dive deep into data science in a structured, boot camp-style format. This program covers essential topics such as data analysis, statistical modeling, and visualization. Students will gain hands-on experience with Python, SQL, and Excel tools and learn to use these tools on real-world data challenges. The boot camp emphasizes practical skills directly applicable to the workforce, helping students build a portfolio of work to showcase to potential employers.
Data Science Corporate Training
If you already belong to a team of trained data scientists but are looking for a way to upskill or reskill your team, Noble Desktop has a way to do that. Noble Desktop provides corporate training solutions tailored to your team to help them develop essential skills in data science, programming, or any area they need training in. Companies can choose from various formats, including in-person training sessions held at their office or Noble Desktop's facilities and live online options that allow teams to learn together from anywhere in the world. The team customizes each training program to align with organizational goals and includes practical exercises relevant to real-world projects. This ensures that employees gain theoretical knowledge and hands-on experience to immediately apply to their roles.
Noble also offers discounted bulk vouchers for businesses seeking flexibility, allowing team members to attend regularly scheduled classes. These vouchers make it easy to accommodate varying schedules while still providing access to top-tier instruction. Whether it's a single department or an entire company looking to upskill, Noble's corporate training programs deliver impactful results. To explore customized training options or learn more about bulk voucher discounts, contact Noble Desktop.
