Explore Python as a versatile tool for data science, learning to clean, model, and visualize data that supports smarter decisions.
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 a field that incorporates a variety of disciplines such as mathematics, computer programming, algorithms, and artificial intelligence to draw insights. Professionals use the multifaceted field across industries to analyze data and make data-driven decisions, technological advancements, and solve problems.
Aspiring data science professionals such as Data Scientists and Data Analysts, work to gain skills in alignment with the data science life cycle that will help them deliver projects and analysis to the companies or clients they work for.
The scope of data science is expansive, with its uses applicable in healthcare, education, financial services, and other industries. Aside from Data Scientists, other roles use data science in their day-to-day responsibilities.
Professionals in the healthcare industry create algorithms to create treatment plans for their patients. Data is also used to assess services offered by physicians and to manage the supply chain of offices and hospitals. In the financial industry, analysts not only use data to provide clients with financial advice; they also depend on data to detect fraud, draft reports, and create forecasts.
If you’re interested in a data science career and would like to attend a data science class, it’s important to know what you’d like to do with your data science training. Data Scientists and Data Analysts work extensively with data, creating visualizations and models, and analyzing data to provide effective business decisions. This requires a heavy background in mathematics, computer science, and statistics. Web Developers work with data as well, but their day-to-day work draws heavily on skills like programming and problem-solving.
What you learn in a data science class depends on the type of class you enroll in. Data Science classes tend to focus on a specific area of skills that data science professionals need such as machine learning, programming, or data analysis. This may require you to take several classes to advance your knowledge of data science. Data Science bootcamps provide students a great way to cover a wider range of skills during their training.
Machine learning involves the use of data and algorithms to help artificial intelligence (AI) learn human behavior. Machine learning serves the general purpose of creating adaptable models that can perform complex tasks across industries. Common examples of machine learning include facial recognition, product recommendations, and email spam filtering.
To enroll in a data science course that covers machine learning, look for a data science bootcamp or a data science for machine learning class. Here, you’ll learn the fundamentals of machine learning, popular machine learning algorithms, and how to perform cross-validation.
SQL and Python are programming languages commonly used for back-end web development. Both languages have several primary functions. SQL is a querying language that allows users to process and store information in relational databases, while Python is crucial for machine learning.
Data science courses that focus on web development and programming will teach you how to use these languages on the server-side, courses focused on data analytics will enable you to create data visualizations with the languages, and machine learning courses will cover how to run scripts with relational data.
Predictive modeling is a statistical technique used to predict future behavior or outcomes. Predictive modeling finds applications in archaeology, the insurance and healthcare industries, and investment firms. Most data science classes cover the basics of predictive modeling. With this skill set, you can help companies improve their analytical capabilities.
Data science professionals need to have excellent communication skills. As part of their responsibilities, most must communicate their findings to colleagues. Their colleagues likely include individuals with little to no technical knowledge, so their communication skills must include the ability to disseminate information in layman’s terms. This requires clarity and concision, as well as efficiency when explaining.
Data science bootcamps often include hands-on group projects where students must work with their classmates. This hands-on opportunity provides you with the perfect chance to work on communicating in a team environment and then sharing your team’s findings with others.
The difficulty level you face when learning data science depends on why you want to learn data science. Web development, machine learning, business analysis, marketing analysis, and financial analysis are all different aspects of data science that require different skills and knowledge. For the sake of efficiency, it’s important going into data science that you consider which aspects you’re most interested in learning.
The field you enter and the skills you start with when learning data science greatly impact how challenging training is.
If you plan to work in web development, much of your training will consist of learning the appropriate programming languages and developing problem-solving skills. Aspiring Business Analysts and Marketing Analysts need to understand how to use data visualization tools such as Tableau and how to interpret data. If you want to work with AI and machine learning, you’ll need to focus heavily on developing strong skills in high-level mathematics and might only need basic skills in Python, Tableau, and similar programs.
Starting with little to no background in mathematics may make it challenging to learn machine learning. Likewise, if you have no experience analyzing data, a variety of fields that involve data analysis will take more time to learn. It’s important to know what prerequisites the field you’re interested in has before diving right into data science. It generally takes around six months to develop beginner-level data science skills, including the time you may require to learn the prerequisites of your field such as statistics or probability.
When enrolling in a data science course, you typically have the option of in-person, live online, or asynchronous classes.
In-person classes follow a traditional learning style where students meet in a classroom and learn face-to-face. This setting encourages feedback from instructors and interaction between students. To enroll in an in-person class, you need to consider your schedule and see if schools in your area offer data science classes that you can attend.
Live online classes allow students to learn in a remote setting from a live instructor via a web conferencing platform. Live online classes follow a schedule like in-person classes do, but they remove the need to commute. Another type of online class modality is asynchronous classes. These classes allow students to access course material online and, more or less, self-teach. No instructor guides the class along, although class material may include recorded video lectures. This class type allows students to learn without having to disrupt their schedule, but the hands-off approach is best for highly motivated students who don’t need an instructor to teach the material as they would in a traditional classroom setting.
You can learn the basics of data science online for free using resources like YouTube and online guides and forums posted by data science professionals and those learning. You can find online books on data science at your local library. These provide great resources for troubleshooting common problems. Data science is an extensive field, so it’s recommended to turn to the internet to learn basic information before enrolling in a course taught by an instructor who can answer your questions and provide demonstrations.
To take your knowledge of data science to the next level, it’s recommended to learn several programming languages and data visualization tools, as well as brush up on statistical analysis. When learning data science, it helps to have a strong theoretical foundation, which you can build by learning statistics and probability. Programming languages like Python and R will help you take your mathematical knowledge and turn it into scalable computer programs.
Several industries in Phoenix depend on data science professionals to innovate and grow. If you have data science skills that you’re looking to put into use, consider the career opportunities these industries offer.
Phoenix is home to over 19 postsecondary institutions that serve as major employers in the city. Educational data science in K-12 and postsecondary schools is responsible for the keeping of educational records, offers potential modeling tasks, and provides educators with the tools necessary to create data visualizations for easily digesting information.
Known as the country’s sixth-largest financial capital, the financial services industry dominates the local economy of the Phoenix area. Wells Fargo, Northern Trust, and FinTech, along with other financial services giants, offer services to the residents of Phoenix. The financial services industry depends on the field of data science to predict risk, protect their clients from fraud, and report on internal expenses.
One of Phoenix’s most prominent industries is leisure and hospitality. Museums, shops, recreation, and a desert landscape draw in over $5 billion annually in tourism revenue, along with supplying over 200,000 jobs around the state. This industry uses data science for predictive analytics, which can provide insight into market trends, customer behavior, and operational efficiency.
Interested in starting a career in data science? Depending on your skill set and interests, a wide variety of career opportunities await you.
If you want to work closely with data, using data science principles in your everyday tasks, consider applying for a position as a Data Scientist. Data Scientists may specialize in AI or data analytics. As part of their job, they often create algorithms and models which they then use to find data-driven solutions for their company or client. The average annual salary for a Data Scientist is $124,000.
Want to dive deeper into data science? Machine Learning Engineers are a type of Data Scientist who works on training computers through datasets and algorithms. Their focus is helping automate machines across industries, from healthcare to transportation. In the US, a Machine Learning Engineer receives an average annual salary of $165,000.
Whether you’re interested in taking a data science class in-person or online, there are a great number of classes, bootcamps, and certificate courses that will help you become proficient in data science and its many related topics.
Noble Desktop’s ||CPN411|| class covers Python, SQL, and machine learning. The 114-hour live online course will help you become prepared for an entry-level data science or Python engineering position. Noble has divided the course into five units, which build upon each other to help you understand the fundamentals of programming and machine learning before diving into hands-on experience that will prepare you for a career in the field.
Students with some background in Python and programming fundamentals looking to sign up for a thorough data science learning experience can enroll in General Assembly’s Data Science Bootcamp held at their Scottsdale location. The intermediate-level course walks students through the fundamentals of data science such as statistical modeling, machine learning models, and natural language processing, to help build the skills needed for a career as a Data Analyst, Machine Learning Scientist, Database Specialist, or other data science professional.
Students with an interest in data analytics can sign up for Maricopa Community College’s Certificate of Completion in Data Analytics, offered at several in-person locations. The certificate program typically takes students three semesters to complete the seven required courses. During the duration of the program, students can expect to learn how to model, synthesize, analyze, and present data for business decision-making.
Likewise, Arizona State University offers a Data Analytics Bootcamp where students will cover a variety of concepts, languages, and programs such as Excel, machine learning, Python, and web visualization. The part-time online program allows students to choose a personalized project track to help tailor a portion of their learning experience.
Companies and organizations interested in team data science training can contact Noble Desktop about their corporate training options. Training is available live online with real-time instructors and onsite at your offices. Noble allows companies to customize their curriculum or choose from existing courses.
Group class vouchers are available for teams signing up for corporate training with Noble. Employers may select which courses to offer to employees, who can then attend training that fits their schedule. For more information about group vouchers or questions about Noble Desktop’s corporate training options, email corporate@nobledesktop.com.