Develop your data analytics confidence through practical projects in Excel, SQL, and Tableau that bring real-world meaning to complex information.
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.
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.
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 the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Automate and enhance the data analysis process with artificial intelligence (AI). In this course, you'll learn how to utilize AI tools to collect, preprocess, analyze, visualize, and interpret data without the need for extensive coding knowledge.
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
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.
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 Tableau for data visualization and become a certified Tableau Desktop Specialist in this hands-on certification course.
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.
Learn Power BI for data analysis & visualization and become a certified Power BI Data Analyst Associate in this hands-on certification course.
Boost your resume with a Microsoft Excel Certification. Our program offers three courses, private training, the Specialist Exam fee with free retake, lifetime access to Excel video courses, and free exam proctoring.
Data analytics is the process of examining large datasets to uncover hidden patterns, correlations, and insights that can inform decision-making and drive business strategies. It involves collecting, cleaning, and analyzing data using various statistical and computational techniques such as data mining, machine learning, and predictive modeling. The history of data analytics dates back to the 1960s with the emergence of early computer systems capable of processing large volumes of data. Over the years, advancements in technology, particularly in computing power and data storage, have fueled the growth of data analytics, making it a cornerstone of modern business intelligence and decision support systems.
With data analytics training, you can unlock a plethora of opportunities across various domains. You can analyze customer behavior to enhance marketing strategies, forecast sales trends to optimize inventory management, or detect fraudulent activities to mitigate risks. In healthcare, you can analyze patient data to improve treatment outcomes, while in finance, you can develop models for investment analysis. Data analytics skills are also invaluable in fields like sports analytics, social media analysis, and urban planning. Moreover, you can use these skills for personal projects such as analyzing personal finances or tracking fitness progress, adding value beyond professional applications.
You’ll learn how to clean, transform, and organize raw data into a usable format for analysis, using tools like Python or R.
You’ll acquire skills in applying statistical techniques to interpret data, including hypothesis testing, regression analysis, and descriptive statistics.
You’ll learn how to create clear and insightful visual representations of data using tools like Tableau or ggplot in R, enhancing communication and understanding of complex datasets.
You’ll gain an understanding of machine learning algorithms and techniques, enabling you to build predictive models and uncover patterns within data.
A crucial soft skill developed in data analytics classes is the ability to critically evaluate data, methodologies, and results, fostering a deeper understanding of the strengths and limitations of various analytical approaches.
Learning data analytics can vary in difficulty depending on your background, prior experience, and the depth of knowledge you aim to acquire. Becoming familiar with basic concepts and tools can be relatively straightforward with dedication and structured learning resources. However, mastering data analytics, including advanced statistical techniques, programming languages like Python or R, and the intricacies of data visualization and interpretation, requires significant time and practice. It involves continuous learning and staying updated with evolving technologies and methodologies. While the learning curve may be steep, persistence and hands-on experience can lead to proficiency and expertise in data analytics over time.
The most challenging aspects of learning data analytics often revolve around mastering complex technical skills and concepts. This includes understanding statistical methods and algorithms, learning programming languages like Python or R, and becoming proficient in data manipulation and analysis techniques. Additionally, interpreting and deriving meaningful insights from large and diverse datasets can be challenging. Moreover, staying abreast of rapidly evolving technologies and techniques in the field requires continuous learning and adaptation. Finally, integrating data analytics into real-world applications and solving complex business problems demands critical thinking and problem-solving abilities, adding another layer of difficulty to the learning process.
The time it takes to learn data analytics varies depending on factors like prior knowledge, learning pace, and the depth of expertise desired. Becoming comfortable experimenting with data analytics, including basic concepts and tools, may take a few months of consistent study and practice. However, reaching a professional level, where one can confidently apply advanced statistical techniques, programming skills, and analytical thinking to solve complex problems, typically requires several years of dedicated learning and hands-on experience. Continuous learning and staying updated with industry trends are essential for mastering data analytics at a professional level.
The choice between in-person and online learning for data analytics depends on individual preferences, learning style, and availability. In-person learning offers direct interaction with instructors and peers, facilitating immediate feedback and collaborative learning. However, it may be limited by geographical constraints and fixed schedules. Live online learning provides flexibility while maintaining real-time interaction, offering convenience without sacrificing engagement. Asynchronous online learning allows for self-paced study, accommodating diverse schedules, but lacks immediate feedback and live interaction. Ultimately, the best option depends on personal preferences, time commitments, and the desired level of interaction and flexibility.
Alongside data analytics, acquiring skills in related fields can enhance your capabilities and career prospects. For instance, proficiency in programming languages like Python or R bolsters your data manipulation and analysis abilities. Knowledge of database management systems such as SQL can facilitate efficient data retrieval and organization. Additionally, familiarity with data visualization tools like Tableau or Power BI can complement your analytics skills by enabling clear and impactful presentation of insights. Moreover, understanding statistical concepts and machine learning algorithms can deepen your analytical capabilities and broaden your problem-solving toolkit in data-driven decision-making contexts.
Financial institutions like Western Alliance Bancorporation (Phoenix) extensively use data analytics to detect fraudulent activities, manage risk, optimize lending/portfolio decisions, and analyze customer transaction patterns for personalized banking services.
In healthcare, organizations such as Banner Health, headquartered in Phoenix, apply data analytics to improve patient outcomes, support predictive diagnosis, manage patient loads, allocate resources efficiently, and streamline clinical and operational processes across their hospitals and care centers.
Retailers like Sprouts Farmers Market (based in Phoenix) use data analytics for demand forecasting, inventory optimization, customer segmentation and targeting, and personalized marketing based on customers’ purchase histories and shopping behavior. Also, PetSmart, founded in Phoenix, leverages analytics to manage its retail supply chain, optimize store operations, and tailor product offerings and promotions to local and national customer data.
Tech companies in the Phoenix area — for example, GoDaddy (originally Phoenix‑based, now headquartered in Tempe, part of metro Phoenix) — rely heavily on data analytics for product development, customer usage insights, performance optimization, marketing, cybersecurity, and scalability of their web‑hosting and domain services. Another example is ON Semiconductor (now “onsemi”), which has a major presence in Scottsdale/Phoenix — in semiconductor and hardware, analytics plays a role in optimizing production, supply chain, quality assurance, and performance monitoring.
A Data Analyst is responsible for collecting, analyzing, and interpreting large datasets to extract actionable insights and inform decision-making processes within an organization. They utilize statistical techniques, data mining, and visualization tools to identify trends, patterns, and correlations in data. Data Analysts play a crucial role in various industries, including finance, healthcare, retail, and technology, where their findings help optimize operations, improve efficiency, and drive business strategies. Their work closely relates to data analytics as they apply analytical skills and methodologies to transform raw data into valuable information that supports informed decision-making and drives organizational success. Data Analysts in Phoenix make $54,000 to $64,000.
A Data Scientist dives into complex datasets to extract insights and make informed decisions. They employ statistical analysis, machine learning algorithms, and programming skills to interpret data trends, predict outcomes, and uncover patterns. Data Scientists play a crucial role in solving business problems, optimizing processes, and identifying opportunities for growth. They develop models to forecast market trends, analyze customer behavior, and streamline operations. Data analytics focuses on examining data to draw conclusions and support decision-making. While Data Scientists specialize in advanced techniques and model development, data analytics encompasses a broader range of methods, including descriptive and diagnostic analytics. Data Scientists in Phoenix make $113,000 to $133,000.
A Business Analyst acts as a bridge between stakeholders and technical teams, translating business needs into actionable insights. They analyze processes, identify inefficiencies, and propose solutions to enhance operational efficiency and profitability. In the realm of data analytics, Business Analysts utilize data to generate reports, create visualizations, and derive meaningful insights to support decision-making. They work with datasets to uncover trends, identify opportunities, and assess risks. While Business Analysts may not specialize in advanced statistical methods like Data Scientists, they leverage data analytics to drive strategic initiatives, improve processes, and optimize business performance. Business Analysts in Phoenix make $71,000 to $91,000.
A Data Engineer designs, constructs, and maintains the infrastructure required for data generation, storage, and processing. They build pipelines to ingest, transform, and load data from various sources into databases or data warehouses. Data Engineers ensure data integrity, scalability, and accessibility for analysis. In the context of data analytics, their role is foundational. They create the architecture that enables Data Scientists and Analysts to perform their tasks effectively. By optimizing data pipelines and implementing efficient storage solutions, Data Engineers facilitate the seamless flow of data, enabling timely and accurate insights to drive business decisions and strategic planning. Data Engineers in Phoenix make $117,000 to $137,000.
Noble Desktop offers a Data Analytics Certificate. Explore the essential skills needed in today’s workforce, from extracting insights to visualizing data. Discover how data drives decision-making across industries like healthcare, finance, and retail. With hands-on projects, you’ll learn to analyze and visualize data, using tools vital for predictive and prescriptive analytics. Dive into real-world case studies to master regression models, forecasting techniques, and optimization tools. Elevate your career in analytics and become a valuable asset in any organization. Start your journey towards becoming a skilled Data Analyst.
Noble Desktop also has a Business Analyst Certificate. Master the essentials of business analytics in this course. Learn to harness the power of Excel for data organization, applying functions, formulas, and calculations. Dive into Microsoft SQL Server to query data from relational databases, unlocking valuable insights. Craft professional presentations with PowerPoint, effectively communicating your findings. Elevate your data analysis skills with Tableau, creating dynamic dashboards and visualizations for informed decision-making. Whether you’re a beginner or looking to enhance your expertise, this course offers hands-on training and real-world applications.
Practical Programming offers a class called Python Developer Intensive. In this course, students will master Python programming essentials and dive into Django development, equipping themselves with the skills essential for a Python Developer role. From foundational Python concepts to advanced Django frameworks, Git, and SQL, this immersive program covers it all. Upon completion, students will qualify for roles like Python Developer, Back-end Developer, and Software Engineer. They’ll also be able to use Python for Data Analytics. Students will dive deep into Python’s lexical syntax, OOP principles, API integration, and web database management.
Empower your team with a data analytics corporate training program, available in both in-person and online formats. Tailored to meet the specific needs of your organization, Noble Desktop’s courses cover essential data analysis techniques and tools. Noble also offers discounted bulk vouchers for access to its regular classes, providing flexibility and cost-effectiveness. Elevate your team’s analytical skills and drive business success with Noble’s comprehensive training solutions. Contact Noble at corporate@nobledesktop.com to discuss your training needs and schedule a consultation today.
Phoenix professionals have a lot of opportunities when it comes to expanding their knowledge in fields like tech, design, business, and data. Anchored by major businesses in downtown Phoenix and the surrounding areas, such as Tempe, Mesa, and Scottsdale, the city benefits greatly from a diverse economy that spans industries ranging from local government and education to tech and finance, all while maintaining strong connections to the global market. These days, companies all across Phoenix and the world are becoming more data-dependent. As such, it's worth exploring data analytics training to learn how to make sense of all the complex information. Through Noble Desktop’s network of professional instructors, students in Phoenix can access high-quality training that helps them meet the industry standard. With decades of combined experience, these instructors go beyond the basics and blend practical guidance with real-world insights. This helps students not only acquire technical skills but also the confidence to make it in their professional careers.
Dr. Deanne Larson is a data science and analytics expert with 20 years of experience and counting. She has worked as an advisor for Fortune 500 companies and FinTech organizations through her firm, Larson & Associates LLC. Throughout her career, she has been drawn to work with AI-driven strategies and initiatives, machine learning implementation, and customer data platforms. Additionally, Dr. Deanne has worked on fraud detection models and managed global personalization platforms that have served hundreds of millions of customers. She is also a knowledgeable instructor, having worked for City University of Seattle, where she mentored doctoral students and developed curricula in data and business analytics. Dr. Deanne currently teaches courses in subjects ranging from data and project management to AI and Python.
After 34 years of federal service, Geno Sexton retired in 2012 and now works as a Phoenix-based project management instructor. Throughout these decades, Geno worked for eight years in the Senior Executive Service, culminating as Deputy Chief Human Capital Officer and HR Director at the U.S. Department of Labor, where he led 120 HR professionals serving 18,000 employees. He was named Deputy CHCO of the Year in 2011 and served as the first HR Officer at the new DHS Headquarters. In his distinguished career, Geno has made a real impact and currently teaches project management courses. He is a Vietnam-era Army veteran, holds a Bachelor’s degree in Journalism and Mass Communications and a Master’s degree in Government from New Mexico State University, and is a proud bilingual and ESL-certified instructor.