Build a foundation in data analytics using Excel, SQL, and Tableau. Classes teach how to interpret data and make informed, strategic 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.
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 solve complex problems. It involves various techniques and tools to collect, clean, process, and analyze data from diverse sources such as databases, spreadsheets, sensors, and social media platforms. Organizations can derive valuable insights to optimize operations, improve customer experiences, and drive innovation by leveraging statistical analysis, machine learning algorithms, and data visualization methods.
The history of data analytics can be traced back to the mid-20th century when businesses began adopting computers for data processing. However, it was only with the advent of powerful computers and advanced statistical techniques in the late 20th and early 21st centuries that data analytics started to gain momentum. With the proliferation of digital technologies and the exponential growth of online data, data analytics has evolved rapidly, becoming a cornerstone of modern business strategy and decision-making processes. Today, data analytics is indispensable across various industries, including finance, healthcare, marketing, and manufacturing, driving efficiency, competitiveness, and innovation.
With data analytics training, you'll gain the skills and knowledge to tackle a wide range of projects and applications across various domains. One common application is business intelligence, where data analysts use analytics techniques to uncover insights from large datasets, enabling organizations to make data-driven decisions. For example, data analytics can be used to analyze sales trends, customer behavior, and market dynamics to identify opportunities for growth and optimization.
Data analytics is a game-changer in marketing and advertising, enabling a deep understanding of consumer preferences, precise audience segmentation, and accurate campaign effectiveness measurement. By analyzing data from social media, website traffic, and customer interactions, marketers can tailor messaging, optimize ad spend, and enhance ROI. In important sectors like healthcare and finance, data analytics is a lifeline, facilitating risk assessment, fraud detection, and patient outcomes analysis, fostering more streamlined processes and better-informed decisions.
Beyond professional settings, data analytics skills can also be applied to personal projects and hobbies. For instance, you can use data analytics to analyze your finances, track fitness progress, or even optimize household energy consumption. The versatility of data analytics opens up a myriad of possibilities for both professional and personal use, empowering you to derive insights and make informed decisions from data in various aspects of your life.
In a data analytics class, you'll learn essential data cleaning and preprocessing skills. This involves identifying and correcting errors or inconsistencies in datasets, handling missing values, removing outliers, and transforming data into a format suitable for analysis. Data cleaning is crucial because real-world data is often messy and incomplete. Without proper cleaning and preprocessing, analysis results may be inaccurate or misleading. Therefore, mastering this skill ensures that the data used for analysis is accurate, reliable, and ready for further processing.
Statistical analysis is another fundamental skill taught in data analytics classes. This skill is not just about numbers but about making informed decisions based on data. It involves applying statistical methods to analyze data, identify patterns, trends, and relationships, and make data-driven decisions. Statistical analysis enables analysts to infer insights from data, test hypotheses, and validate findings. In data analytics, understanding statistical concepts such as probability distributions, hypothesis testing, regression analysis, and correlation is essential for deriving meaningful insights and making informed decisions based on data.
Data visualization is the art of representing data graphically to facilitate understanding and interpretation. In a data analytics class, you'll learn techniques for creating compelling visualizations using tools like ggplot2, Matplotlib, or Tableau. Visualization isn’t just about making data look pretty; it also drives data-driven decision-making. It plays a crucial role in data analytics by enabling analysts to communicate insights, trends, and patterns in a clear and compelling manner. Visualizations help stakeholders grasp complex information quickly, identify outliers or anomalies, and gain actionable insights from data. Mastering data visualization allows analysts to present findings effectively and drive data-driven decision-making within organizations.
Machine learning is a branch of artificial intelligence that enables systems to learn from data and make predictions or decisions without being explicitly programmed. In a data analytics class, you'll learn various machine learning algorithms and techniques such as linear regression, logistic regression, decision trees, random forests, support vector machines, and clustering. Machine learning is invaluable in data analytics for tasks such as predictive modeling, classification, clustering, and anomaly detection. By understanding machine learning algorithms and how to apply them to analyze data, analysts can uncover hidden patterns, predict future trends, and automate decision-making processes.
While technical skills are crucial in data analytics, communication and presentation skills are equally important. In a data analytics class, you'll learn how to effectively communicate findings, insights, and recommendations to stakeholders through written reports, presentations, and visualizations. Clear communication ensures that decision-makers within organizations understand and act upon data insights. Additionally, presenting complex technical concepts simply and understandably is essential for bridging the gap between data analysts and non-technical stakeholders. Mastering communication and presentation skills empowers data analysts to effectively convey the value of data-driven insights and drive organizational change.
Learning data analytics can vary in difficulty depending on factors such as prior experience, background knowledge, and the depth of understanding one aims to achieve. Becoming familiar with data analytics, including basic concepts like data cleaning, visualization, and simple statistical analysis, can be relatively straightforward with the right resources and dedication. However, mastering data analytics, which involves proficiency in advanced statistical methods, machine learning algorithms, and complex data manipulation techniques, requires significant time, effort, and continuous learning. It often involves gaining hands-on experience with real-world datasets, practicing problem-solving, and staying updated with the latest tools and techniques in the field. While challenging, the rewards of becoming an expert in data analytics such as the ability to derive actionable insights from data and make data-driven decisions, make the journey worthwhile for many aspiring analysts.
The most challenging parts of learning data analytics involve mastering the technical skills and concepts required for practical data analysis. This includes understanding complex statistical methods and algorithms, learning how to manipulate and clean large and messy datasets, and gaining proficiency in programming languages like Python or R. Additionally, keeping up with the rapidly evolving landscape of data analytics tools and techniques can be daunting. Moreover, applying theoretical knowledge to real-world scenarios and effectively communicating insights to stakeholders present further challenges. However, with dedication, practice, and continuous learning, these challenges can be overcome, leading to a rewarding and fulfilling journey in data analytics.
The time it takes to learn data analytics varies depending on factors such as your prior experience with data analysis and the depth of knowledge you aim to acquire. Generally, it may take a few months to become comfortable experimenting with basic data analytics techniques, especially if you have some familiarity with data manipulation and statistical concepts. However, mastering data analytics professionally, which involves understanding advanced statistical methods, programming languages like Python or R, and specialized tools and platforms, typically requires several years of dedicated study and practice. Continuous learning and real-world application of skills are essential for reaching a proficient level in data analytics.
When deciding whether to learn data analytics in-person or online, consider the differences between various learning formats: in-person, live online, and asynchronous learning. In-person learning traditionally involves physically attending classes or workshops and interacting with instructors and peers face-to-face. This format gives you immediate feedback, personalized attention, and hands-on learning experiences. However, it may require a significant time commitment and geographical proximity to educational institutions. Additionally, in-person learning can be costly due to expenses like tuition fees, travel, and accommodation.
Live online learning, on the other hand, offers a flexible and accessible alternative. It allows you to engage in virtual classrooms through video conferencing platforms, participating in live lectures, discussions, and interactive activities. This format eliminates geographical barriers, enabling you to access quality education from anywhere with an internet connection. It also provides the convenience and flexibility to balance studies with work or other commitments. However, it's worth noting that live online learning may require a stable internet connection and suitable technology, and scheduling conflicts could arise due to time zone differences.
Asynchronous learning provides the ultimate flexibility, allowing you to access course materials and lectures at your own pace and convenience. You can study whenever and wherever you choose, making it ideal for those with busy schedules or commitments. Asynchronous learning accommodates diverse learning styles and preferences, enabling you to review content multiple times and progress at your own speed. However, asynchronous learning lacks real-time interaction and immediate feedback, which could impede the learning process for some individuals. Additionally, staying motivated and disciplined without the structure of live classes may pose challenges for certain learners.
Ultimately, the choice between in-person, live online, or asynchronous learning depends on your individual preferences, learning style, schedule flexibility, and resource access.
Yes, you can learn data analytics online for free through a variety of resources. Noble Desktop's YouTube channel offers free tutorials and courses on topics like Excel, SQL, and data visualization, which are foundational in data analytics. Online education platforms also provide free courses that cover essential skills. However, it's important to remember that mastering data analytics requires more than just watching videos or reading articles; it involves hands-on practice, advanced problem-solving, and real-world experience, which typically require more structured training, mentorship, or professional projects. So, while you can start learning for free, achieving professional-level skills will likely require additional investment in comprehensive courses, certifications, or industry experience.
Here are several skills that commonly complement data analytics, with brief explanations of how they relate to data analysis work:
These complementary skills, when combined with data analytics expertise, can significantly enhance your ability to analyze data, derive insights, and effectively communicate results.
Atlanta, the bustling capital of Georgia, has a diverse economy driven by various industries, each harnessing the power of data analytics to drive growth and efficiency. The city is a vibrant hub for companies that rely on data to inform decision-making and strategy, from healthcare to logistics. Notably, Atlanta also houses a strong nonprofit sector and civic-oriented organizations that utilize data analytics to improve community services and public welfare. Here are some of the key industries in Atlanta and how data analytics plays a vital role in their operations:
Atlanta is a major healthcare center, with renowned institutions like Emory Healthcare, Grady Health System, and the Centers for Disease Control and Prevention (CDC) headquartered in the area. Data analytics in healthcare is used to improve patient care, streamline hospital operations, and support public health initiatives. In particular, hospitals use data analytics to track patient outcomes, optimize resource allocation, and predict healthcare trends. Conversely, the CDC leverages data analytics to monitor disease outbreaks, conduct epidemiological research, and develop public health policies. In a city as large and diverse as Atlanta, data-driven insights are crucial for providing effective and equitable healthcare services.
With its strategic location as a transportation hub, Atlanta is home to major logistics and transportation companies, including Delta Air Lines and UPS. Data analytics is fundamental in this industry, allowing companies to optimize routes, manage supply chains, and improve customer service. For example, UPS uses advanced analytics to design efficient delivery routes, reducing fuel consumption and delivery times. Delta employs data analytics to optimize flight schedules, improve aircraft maintenance, and enhance the passenger experience. Given Atlanta's role as a logistics powerhouse, data-driven decision-making is essential for maintaining smooth operations and staying competitive in this fast-paced industry.
Atlanta's tech scene is rapidly growing, with an increasing number of startups and established companies focusing on software development, FinTech, and cybersecurity. Data analytics is at the core of this industry, providing insights for product development, user behavior analysis, and security threat detection. Companies like NCR and Equifax use data analytics to enhance their products and services. Additionally, the FinTech sector in Atlanta, known as "Transaction Alley, " uses data analytics to process financial transactions securely and efficiently. As the tech industry in Atlanta continues to expand, data analytics will play an even more significant role in driving innovation and ensuring data security.
Atlanta is also known for its vibrant nonprofit sector and civic engagement initiatives. Organizations like the United Way of Greater Atlanta and the Atlanta Community Food Bank use data analytics to allocate resources, track program effectiveness, and engage with the community. In the nonprofit sector, data analytics helps organizations identify areas of need, measure the impact of their programs, and make data-driven decisions for future projects. Civic engagement groups in Atlanta leverage data to analyze voting patterns, assess public opinion, and promote community involvement. By harnessing data analytics, these organizations can better serve the public and drive positive change in the Atlanta area.
Data analytics is a broad field with various roles and specializations. These job titles span industries from technology to healthcare and beyond. Below are some common job titles that involve data analytics, along with a description of their roles and responsibilities.
A Data Analyst is responsible for collecting, processing, and analyzing data to identify trends, patterns, and insights. They create reports, dashboards, and visualizations to communicate findings to stakeholders. Data analysts work in various industries, including finance, healthcare, and marketing, helping companies make data-driven decisions. The average annual salary of data analysts is $77,000 in the U.S.
Data scientists use advanced statistical methods, machine learning, and predictive modeling to analyze large datasets and extract meaningful insights. They often develop algorithms, build models, and work on complex problems that require a deeper understanding of data science concepts. Data scientists frequently collaborate with business leaders to solve specific business challenges using data. The average annual salary of data scientists is $125,000 in the U.S.
BI analysts focus on transforming raw data into actionable insights for business decision-makers. They design and create business intelligence reports, dashboards, and scorecards to track key performance indicators (KPIs). BI analysts work with various tools and platforms, like Tableau and Power BI, to visualize data and support business strategy. The average salary for business intelligence analysts is $100,000 per year in the U.S.
Data engineers design, build, and maintain data pipelines and architectures. They ensure that data is stored and processed efficiently, enabling data analysts and data scientists to access reliable data. Data engineers work on integrating data from various sources, cleaning data, and optimizing data storage and retrieval processes. The average annual salary of data engineers in the U.S. is $127,000.
Quantitative analysts, or quants, are data analysts who specialize in financial markets and quantitative modeling. They apply mathematical and statistical techniques to analyze financial data, develop trading strategies, and assess investment risks. Quants often work in investment banking, hedge funds, and financial services firms. The average annual salary for quantitative analysts in the U.S. is $100,000.
Machine Learning Engineers design and implement machine learning models to solve complex problems. They work on projects involving artificial intelligence (AI), natural language processing (NLP), and neural networks. Machine Learning Engineers collaborate with data scientists and data engineers to build and deploy machine learning applications. The average annual salary for Machine Learning Engineers in the U.S. is $120,000.
Data architects design data structures and systems to support data storage, processing, and retrieval. They ensure data quality, integrity, and security while creating scalable solutions for data management. Data architects work closely with data engineers to establish the framework for an organization's data infrastructure. The average annual salary of data architects in the U.S. is $117,000 per year.
The Chief Data Officer is an executive responsible for an organization's data strategy, governance, and management. They oversee data-related functions, including data analytics, data science, and data engineering, ensuring that the organization's data assets are leveraged effectively. CDOs play a key role in shaping business strategy and promoting data-driven culture within the organization. The average annual salary for CDOs in the U.S. is around $104,000.
These job titles represent a range of roles within the data analytics field. Each role requires a unique skill set and expertise, with many offering opportunities for career advancement and specialization.
Introduction to Data Analysis—Emory University Continuing Education
This data analytics course is designed for professionals and leaders seeking to make data-driven decisions in their organizations. This intermediate-level course covers the importance of quantitative analytics and how data quality impacts decision-making. Participants learn about various decision-making models, fundamental concepts of measurement, and techniques for ensuring data quality and accuracy. The course also explores the distinction between correlation and causation, methods for data transformation, and the potential biases in data collection.
The course is self-paced and delivered online, providing flexibility to learners. Participants can access a comprehensive overview of data analysis techniques and how they’re used across different sectors. The course outcomes include the ability to apply appropriate decision-making techniques to specific cases and discuss the value of big data and analytics.
Data Analytics Foundations—Noble Desktop
The Data Analytics Foundations course offered by Noble Desktop is an entry-level training program that introduces you to the fundamentals of data analytics, focusing on descriptive and inferential statistics. It explores how different industries and organizations use data for forecasting and decision-making, with insights into statistical analysis, algorithms, theorems, and models. This course provides you with hands-on training from experienced instructors, with small class sizes and free retakes within one year.
In this course, you’ll gain practical knowledge in data analytics, including data distributions, predictive and prescriptive analytics, and Big Data's role in business decision-making. A basic understanding of Excel is recommended as a prerequisite, but the course is designed to be beginner-friendly. The course consists of 12 hours of instruction, with options to attend in-person in NYC or live online via Zoom.
Data Analytics Short Course—General Assembly
This short course in data analytics is designed to equip students with in-demand data skills, providing a quick path to boosting a career in data analytics. The course offers flexibility with part-time or accelerated full-time formats, enabling learners to choose between 10-week evening sessions or a one-week intensive course. Students will gain a comprehensive understanding of data collection, cleaning, analysis, and visualization, using popular tools like Excel, SQL, and Tableau. With no prior data experience required, this course is suitable for individuals across various industries seeking career advancement.
Tools of Data Analysis—Emory University Continuing Education
Emory Continuing Education's Tools of Data Analysis is an intermediate-level course designed to explore various statistical techniques used by organizations for decision-making. The course covers key tools such as crossover analysis, break-even analysis, cluster analysis, decision tree analysis, and provides an introduction to regression. It focuses on evaluating and analyzing these statistical techniques, discussing their real-world limitations and benefits. The self-paced, online course can be accessed as soon as one business day after enrollment.
Upon completing the course, learners will understand and be able to describe the applications of different statistical techniques, like linear programming, and utilize these tools for real-world decision-making scenarios. The course also covers the standard deviation rule, the interpretation of ANOVA tests, and forecasting techniques, providing a comprehensive understanding of when and how to apply different statistical methods. This course is suitable for professionals looking to enhance their data analysis skills and make more informed decisions in a business context.
Data Analytics Certificate—Noble Desktop
The data analytics certificate program offered by Noble Desktop provides comprehensive training to become a Data Analyst, emphasizing hands-on experience with real-world projects. This course covers critical skills such as data analysis, visualization, and statistical analysis, with modules on Python, SQL, Tableau, Excel, and more. You’ll learn predictive and prescriptive analytics through case studies and be guided by experienced instructors.
The program offers flexible scheduling, allowing for part-time or full-time attendance, and includes free retakes, 1-on-1 mentoring, and installment payment plans. Graduates earn a licensed certificate, with alumni praising the program for its supportive instructors and practical approach. With a live online option, students in Atlanta can learn in a virtual small-class environment and receive personalized attention. Additional benefits include job application support, portfolio reviews, and resume assistance, helping you prepare for a successful career in data analytics.
Analytics Certificate—Georgia Tech Professional Education
The analytics certificate from Georgia Tech is a comprehensive program designed to equip learners with foundational skills in analytics, providing a pathway to advanced careers in data science, data analysis, business analysis, operations research, and more. This certificate comprises three foundational courses from Georgia Tech's Online Master of Science in Analytics (OMS Analytics) degree and is offered through the edX platform. It covers a range of topics including analysis methods, analytical tools like R, Python, and SQL, and business applications of analytics.
By completing this certificate, learners gain experience with common analytics software, statistical and machine learning methods, and data visualization techniques. The certificate provides an immediate Return on Education (ROE), allowing learners to apply real-world skills to their careers upon completion. It also demonstrates a commitment to professional development, making it a valuable addition to any resume.
Data Analytics Bootcamp—General Assembly
General Assembly's Data Analytics Bootcamp is a full-time, 12-week program designed to help students launch their careers in one of the fastest-growing professions in tech. Through live online learning and hands-on projects, learners will gain the skills to become job-ready in data analytics. The program covers a comprehensive curriculum, including SQL, Tableau, Python, Power BI, and statistics, providing a 360-degree view of data analysis. Students will work on real-world projects, learn from industry experts, and receive personalized career coaching to prepare for roles like cyber Security Analyst, business development analyst, risk and compliance analyst, and more.
Data Analytics Technologies Bootcamp—NYIM Training
The Data Analytics Technologies Bootcamp offers a comprehensive, hands-on training program that covers advanced Excel, SQL, and Tableau skills. Ideal for those seeking to master data analysis and visualization tools, this bootcamp provides a mix of live projects, expert-led instruction, and interactive learning. The program is designed for both in-person training in NYC and live online sessions, with small classes ensuring personalized attention. Students will work on real-world projects that require organizing, analyzing, summarizing, and visualizing data to present actionable insights.
The curriculum includes a progression from beginner to advanced Excel techniques, SQL database interactions, and Tableau data visualization. It comes with additional benefits like free retakes within one year, flexible payment plans, and setup assistance. The bootcamp includes eight one-day courses, totaling 51 hours, with a discount for enrolling in the complete program. No prior data analytics experience is required, but comfort with computers is recommended. Graduates earn a licensed certificate, which can be shared on professional networks like LinkedIn. To further accommodate students, the bootcamp offers flexible payment options, installment plans, and corporate training solutions for teams.
Data Analytics Technologies Bootcamp—Career Centers
The intensive data analytics bootcamp offers hands-on training in essential data analytics tools, including Excel, SQL, and Tableau. The bootcamp spans eight days and provides in-depth instruction on manipulating, analyzing, and visualizing data to uncover insights that drive business decisions. The course is available both in-person in NYC and live online, allowing for flexible learning options.
Designed for beginners with no prerequisites, this bootcamp teaches you to summarize, analyze, and chart data to derive actionable insights. The course covers the basics of Excel, intermediate and advanced Excel functions, and how to use SQL to sort and filter data from databases. Additionally, you'll learn about Tableau to create visualizations, dashboards, and data-driven stories. Upon completion, you'll receive a certificate of completion, which can be shared on LinkedIn and other professional platforms.
Tableau 101—Ledet Training
The Tableau 101 course by Ledet Training offers a comprehensive introduction to Tableau, a leading data visualization tool, over a two-day period. The course is available both online and in-person at Ledet’s Atlanta headquarters. For the course textbook, participants can choose between a print book, a digital book, or both. The course aims to help learners understand the basics of Tableau, including data visualization techniques and tools, and is designed for anyone looking to enhance their skills in data visualization, whether they are beginners or have some prior experience.
Python for Data Science Bootcamp—Noble Desktop
Noble Desktop's Python for Data Science Bootcamp is a comprehensive training program designed to take you from the basics of Python programming to the beginnings of machine learning. Through hands-on projects using real-world datasets, you’ll learn the fundamentals of data analysis, explore and manipulate data, and create insightful visualizations using key data science libraries like Pandas, NumPy, and Matplotlib. The course also covers statistical analysis and how to apply Python for data-driven decision-making.
This bootcamp is ideal for beginners and those with limited programming experience. It starts with Python basics, covering topics like data types, variables, functions, and control flow structures. You’ll then learn to structure programs with conditional statements, loops, and object-oriented programming. The bootcamp progresses to data science-specific topics, including arrays and dataframes, with a focus on importing, cleaning, and analyzing data. As you advance, you'll create various data visualizations such as bar charts, histograms, and line graphs, leading up to simple machine learning techniques.
The course includes 30 hours of live, project-based training, and offers a free retake within one year, giving you the flexibility to reinforce your learning. Small class sizes ensure personalized attention, and you also get a 1:1 bonus training session to review concepts, receive assistance with your projects, or further develop your skills. The program is available both in-person in NYC and online via Zoom, with setup assistance provided for remote learning.
Business Intelligence for Data Science and Visualization—Emory University Continuing Education
The Business Intelligence for Data Science and Visualization Program from Emory Continuing Education is an 11-week certificate program designed for professionals aiming to improve their data analytics and visualization skills. This Saturday-only course offers hands-on training with key business intelligence tools such as Advanced Excel, SQL, Microsoft Power BI, and Tableau. The curriculum emphasizes collecting, analyzing, and visualizing business data to produce insights that can guide business decisions. To enhance learning, the program features experienced practitioners, practical projects, and real-world assignments, with additional help sessions during weeknights for further support.
Graduates earn an official digital badge from Emory, suitable for online platforms like LinkedIn. The program is ideal for those seeking higher-paying careers, recent graduates, professionals aiming for career advancement, or anyone transitioning into data science and business intelligence. The comprehensive curriculum covers data collection, storage, querying, data mining, descriptive analytics, and results presentation, providing a solid foundation for solving corporate and organizational challenges.
Corporate data analytics training is available at Noble Desktop. You can explore the benefits of training in data analytics for employees—including how it greatly contributes to your business decisions and overall organization growth. You’ll learn about various onsite and online training options, and how they can enhance your employees’ professional skills and your organization's data analytics performance.
Noble Desktop offers a data analytics curriculum that’s customizable to meet specific company needs. There’s an option to purchase bulk seats in regular enrollment classes at a discount. The comprehensive program, led by experienced industry professionals, enhances employee satisfaction, increases retention rates, and ensures that companies stay competitive in the rapidly evolving digital landscape.
For more information, reach out to help@nobledesktop.com.