Best Data Analytics Classes Pittsburgh, PA: Find Courses & Onsite Training

Data Analytics Certificate Learn The Skills Guarantee™

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 more & register Explore Noble Desktop’s Data Analytics courses
- $4,995
- 156 hours
- Free retake
- Live Online or NYC
- 1:1 Mentoring Included
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Noble Desktop Data Science & AI Certificate

In this comprehensive data science program, individuals will learn a variety of critical skills to become a data scientist. Students will learn to code with Python, create machine learning models, automate tasks like web scraping, and interact with databases using SQL. Topics include data analysis with Python's NumPy and Pandas packages, data visualization, predictive analytics, machine learning, SQL querying, Python automation, and web scraping. Students will learn hands-on by working in Python and SQL and will complete the program with the skills they need to enter the field of data science. This program offers flexible scheduling and provides a free retake so students can refresh the material.

Available live online (virtual training) from anywhere
- $3,995
- 114 Hours
- Weekdays or Evenings
- Beginner
- Free Retake
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USF Bootcamps by Springboard Become a Cybersecurity Analyst
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- 6 Months, 18 hr/wk
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- Beginner
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Thinkful Data Analytics Flex

Data Analytics Flex is a 6-month part-time program designed to get you a job as a data analyst. You will begin by learning how to master Excel and tell stories with PowerPoint. Afterwards, you'll learn how to query data using SQL and visualize data using Tableau. Once you've mastered these building blocks, you'll receive business case studies to simulate real-world work projects, and you'll also be exposed to statistics. Finally, you'll learn how to clean data using Python and work on your capstone project. This program comes with a tuition-back guarantee if you don't land a job within 6 months of graduation.

Available live online (virtual training) from anywhere
- $4,500
- 6 Months, 25 hr/wk
- Weekdays
- Intermediate
- Money-back guarantee
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NYIM Training Data Analytics Technologies Bootcamp

In this program, participants will learn the key skills and tools needed to succeed as a data analyst, including Excel, SQL, and Tableau. Attendees will learn how to use Excel to create graphs, summarize data with PivotTables, and analyze data. In SQL, attendees will learn the essentials of relational databases, queries, functions, and more. Finally, participants will learn Tableau to create visualizations to present their data clearly. The program is open to beginners and has flexible scheduling options. Tuition includes hands-on training, a free retake, and training manuals for each course.

Available live online (virtual training) from anywhere
- $1,949
- 51 Hours
- Weekdays
- Beginner
- Free Retake
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NYC Career Centers Data Analytics Technologies Bootcamp

In this 8-day intensive Data Analytics bootcamp, students with little to no background in data analytics will learn how to use Microsoft Excel, SQL, and Tableau to summarize, display, and discover key insights hidden in data sets. By the end of this bootcamp, students will be able to use Excel to aggregate and summarize data, summarize and analyze data with PivotTables, create charts and PivotCharts in Excel, use SQL to filter and sort large data sets, perform advanced database queries, create visualizations with Tableau, and much more. Students will also be able to reinforce what they learn through the numerous projects they encounter throughout the course. This course also offers a free retake within six months to allow students to revisit any concepts needing to be refreshed and walk away with a much stronger grasp of the material.

Available live online (virtual training) from anywhere
- $1,949
- 51 Hours
- Weekdays
- Beginner
- Free Retake
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Fullstack Academy Online Data Analytics Bootcamp (Full or Part Time)

Students build in-demand industry skills and prepare to take certification exams in Tableau and Amazon Web Services. The program consists of Fullstack’s live online classes and workshops so you will feel fully supported along your journey. The up-to-date curriculum ensures course activities are relevant with real-world applications and interactive technology to help you master the knowledge and skills that you’ll acquire throughout the program.

Available live online (virtual training) from anywhere
- $14,495
- 10 Weeks
- Weekdays
- Beginner
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ONLC Training Centers Tableau Desktop Level 1: Introduction for Novice Users

This introductory course offers valuable lessons in exploring, visualizing, and understanding data using Tableau desktop, allowing you to take your first steps toward mastering the application. With a strong emphasis on optimizing the software’s powerful capabilities, participants will learn about data connection, creating views, mapping, data calculations, and other critical functional areas. Perfect for beginners, this course aims to help you transform raw datasets into impactful visual narratives.

Live online with optional computer lab access @ 355 Fifth Avenue, Pittsburgh, PA
- $1,495
- 20 Hours
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- Beginner
- Free Retake
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ONLC Training Centers Tableau Desktop Level 3: Dashboard Deep Dive

This specialized course deeply delves into Tableau Desktop, focusing on advanced visualization techniques and dashboard design. Participants will gain expertise in managing big datasets, utilizing advanced calculations, and incorporating real-time data to produce dynamic dashboards. The program is intended for professionals with previous experience in Tableau Desktop, aiming to elevate their skills to a more advanced level.

Live online with optional computer lab access @ 355 Fifth Avenue, Pittsburgh, PA
- $1,095
- 13 Hours
- See provider
- Advanced
- Free Retake
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ONLC Training Centers Excel Charts, Formatting and Reporting

This comprehensive course offers an in-depth overview of Excel's charting and report generation features in MS Office 365,2019, and 2016. Participants will learn to create and manage advanced charts, apply conditional formatting, and generate dynamic reports. This engaging professional development opportunity is perfect for those seeking to enhance their data presentation and analysis skills.

Live online with optional computer lab access @ 355 Fifth Avenue, Pittsburgh, PA
- $295
- 13 Hours
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- Intermediate
- Free Retake
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Showing 10 of 18 courses

Key Insights

Professionals trained in data analytics can pursue specialized careers in industries such as finance, marketing, healthcare, and scientific research, or work independently as freelance analysts serving varied clients.

Core skills taught in data analytics classes include statistical techniques such as regression, cohort, cluster, and time-series analyses, enabling analysts to interpret data relationships and inform strategic decisions.

Data analytics education typically covers practical instruction in software and languages like Excel, Tableau, Python, and SQL, equipping analysts with tools necessary for data management, visualization, and customized analysis.

Learn More About Pittsburgh Data Analytics Courses

Data analytics is a high-demand skill, globally and across nearly every industry. The amount of information that organizations must gather and understand is constantly increasing. The analytics industry strives to keep pace, finding new and better ways to produce insights and guidance. Work is consistently available for Data Analysts, whether as independent contractors, members of an analytics firm, or staff analysts within organizations. Analysts may apply their skills flexibly to varied clients or may specialize in fields like marketing, healthcare, finance, government, or sports statistics. Most in-person data analytics training in Pittsburgh is offered through degree programs from the region’s many colleges and universities. However, local students can find shorter introductory classes, boot camps, and professional training programs through live online providers.

What is Data Analytics?

Information does not explain itself. Reading large databases is impossible, even after applying descriptive statistics. People need sophisticated analyses and visualizations to detect significant patterns, answer questions, and make informed predictions. Data analytics applies data science, statistics, and communication to derive meaning from raw information and apply this knowledge to decisions and actions. Given the increasingly large datasets that businesses, researchers, and governments must collect and manage, data analytics is crucial to interpret and act upon this information.

Data analytics is as old as the first methods used to describe and interpret number sets. Records were once collected and processed by hand, requiring physical storage, precise organizational methods, sophisticated formulas, and careful graphing techniques. Researchers developed early computers largely to improve such data-processing tasks. Now, Data Analysts use spreadsheet and database programs like Microsoft Excel and languages like Python, SQL, R, or SAS to gather, store, process, and display data. These technical tools do not replace older mathematical and scientific knowledge; rather, they automate and expand those processes to make large-scale analyses practical. Data Analysts also use software like Tableau and Microsoft Power BI to produce graphic depictions and written reports that more effectively explain their findings.

What Can You Do with Data Analytics Training?

Any field that handles significant amounts of information can benefit from data analytics. Data Analysts can be freelancers or employees of a data analytics firm, providing analyses and guidance for varied clients, or staff Analysts within businesses or other organizations. Often, analysts specialize in a particular industry, focusing on the data types, analyses, and visualizations used in that area. The industries that employ the most Data Analysts are finance, marketing, scientific research, and healthcare, but there are also Sports Analysts, Educational Analysts, Political Analysts, and Transportation Analysts, for just a few examples.

Scientific research is particularly data-driven, and every researcher—including students, academics, and non-profit investigators—needs data analysis training. Healthcare providers also use data analytics to confirm the effects of various interventions and determine the effectiveness of their care. Medical technologies, particularly imaging and diagnostic systems, are often direct applications of data analytics. Other technologies, including sensor systems, robotics, and artificial intelligence programs, are similarly built upon analytic algorithms.

Being able to analyze data effectively is useful for many other people, either professionally or for personal projects. Understanding data and analysis helps people translate and explain information and make better decisions. For example, all businesses, even small ones, gather data on sales and customers. Data analytics can find patterns in this data that help to identify and retain frequent buyers, spot problems, or suggest untapped markets. Data analytics training can also help a Business Manager understand the data and analyses provided by a consultant, vendor, or client. Similarly, a person trained in data analytics can better read and evaluate data products like poll results, investment information, or product performance reports. In particular, journalists and other writers benefit from data analysis to conduct investigations of available information and evaluate others’ reports.

What Will I Learn in a Data Analytics Class?

Data Science

Data science is the broad study of information: how data is collected, structured, and stored, plus the analysis and visualization methods of data analytics. This field explores the nature of data, starting with raw inputs like measurements, recorded actions, and surveys. It also discusses how data can be most effectively organized and retrieved, cleaned to remove irregular data points, and transformed for various uses. In data analytics classes, discussions of data science particularly address the usefulness and appropriateness of various analyses when applied to different kinds of data. An analysis used with the wrong type of data can give nonsensical or misleading conclusions. Finally, data science explains how choosing the right visualization methods can communicate analytic results more clearly and accurately.

Statistical Methods

The core of analytics is statistics, the mathematical processes that describe datasets and use them as samples to produce wider conclusions. Statistical processes include simple summaries like averages and more complex calculations like regression, factor, cohort, cluster, and time-series analyses. These analyses can reveal underlying relationships between data points and suggest causation between events. Data analytics students must be able to describe these analyses and perform them manually on small datasets so they can choose appropriate methods and implement them correctly within larger, automated systems. Many data analysis classes use spreadsheet programs to demonstrate different statistical methods so that students can visualize processes and understand how they expand and combine into more sophisticated analyses.

Data Visualization

The third major part of data analytics is communication: displaying and explaining the conclusions drawn from data and its analyses. Visualization methods convert data into images that quickly convey patterns and meanings. Static visualizations include simpler graphics like line, bar, and pie charts and more elaborate displays like scatterplots and heatmaps. Analyses may also be displayed in a dashboard interface, including updates and animations for changes over time. Data analytics students should learn various visualization forms, not only knowing how to create them but also understanding how to appropriately select images that accurately and effectively communicate their findings.

Technical Tools: Software and Programming Languages

Most of an analyst’s work is performed with prewritten software packages and/or custom-coded programs. Nearly all data analytics classes feature at least one popular data management program such as Excel or Tableau, and some courses include multiple programs to give students versatility. Other courses teach the basics of data analysis in a programming language commonly used for this purpose such as Python or SQL. Learning to code enables analysts to build custom tools for data collection, storage, analysis, and visualization. Professional training programs often include multiple programs and one or more languages, highlighting the features that make certain tools preferable for certain tasks. A given industry or employer might also prefer that their analysts be proficient with a specific tool.

Spreadsheet Programs

These programs are easy to learn and use thanks to their graphical interface. They are also handy for many analysis and visualization tasks but struggle with larger datasets and more complex analyses.

Microsoft Excel
ClickUp
Google Sheets
Quattro Pro

Database Management Systems

These systems efficiently store and sort large quantities of data, often as part of a larger system providing data collection, analysis, and reporting functions. They take more study to master but offer enormous flexibility and power.

MySQL
MongoDB
Amazon RDS
Airtable—a hybrid spreadsheet/data management program

Statistical Systems

These programs and languages are primarily created for statistical processing. They structure, calculate, and report complex analyses with relative ease and high efficiency, even for huge datasets.

Matlab
Statistical Analysis System (SAS)
R

Analytics Services

These comprehensive data systems offer multiple integrated services to support organizations’ analytic needs. They particularly emphasize data reporting, offering powerful visualization tools, easier sharing through varied methods like online dashboards and collaborative platforms, and automation and AI features.

Tableau
Microsoft Power BI
Amazon Web Services (AWS)

Data Collection Tools

These programs and languages are specialized to assist in data collection, gathering information from sources like surveys, forms, sensors, websites (aka ‘scraping’), or searches through larger databases (aka ‘data mining’).

RapidMiner
Looker
Structured Query Language (SQL)

General Programming Languages

Many general-purpose programming languages have found extensive use in data analysis, due to their flexibility, processing power, and prewritten code libraries that simplify analysis tasks. It is often more convenient to build an entire data system as a set of interlinked programs written in a single language, especially for unique datasets or analysis types. Programming analyses from scratch also helps students more thoroughly understand their underlying structures.

Python—particularly libraries like NumPy, Pandas, Mathplotlib, and Seaborn
Java
JavaScript
Swift—for Apple and iOS
C++

Machine Learning

Some data analytics courses will include lessons on artificial intelligence applications, particularly machine learning algorithms. Machine learning systems use data analysis methods to collect and process information and then use their conclusions to guide subsequent actions, including further investigation. Machine learning and automated data-gathering programs can identify patterns and pursue analyses that might not occur to human analysts working alone.

How Hard is It to Learn Data Analytics?

Even with modern technical tools, data analytics is built upon fundamental mathematical procedures. Students intimidated by numerical principles and methods will have more difficulty understanding data analysis and learning its skills. Similarly, new students must learn enough data science to understand the collection and structure of data and how its properties guide effective analyses. In an introductory course, these abstract lessons are often taught using data management software and languages that implement their principles. Students less familiar with computation and coding will then need additional time to practice with these technologies.

An introductory data analytics class only familiarizes students with basic concepts and techniques and helps them interpret more complex analyses. To effectively use analytic techniques on real data, students need more comprehensive training, ideally beginning with a bootcamp or similarly intensive course. Such courses cover essential skills like cleaning and organizing large datasets, selecting and performing analyses based on inquiries, using multiple analytic tools, and creating visualizations and reports from their results. Specialized programs, like financial analytics, also address specific data types and methods within their fields. While this degree of study can give workers many useful analysis skills, it is still typically insufficient for most data analytics careers.

Professional data analytics training such as a certificate course or degree program spans even more analysis types, visualization methods, and technical tools. Students must master these skills to handle real-world analysis problems. Working data analysts must translate employers’ problems into full analysis projects including data collection and cleanup, well-chosen and accurate analyses, and effective reporting that provides clear answers. Advanced classes also cover challenging topics like predictive modeling, data mining, and machine learning, skills that further distinguish expert analysts from ordinary statisticians.

What Are the Most Challenging Parts of Learning Data Analytics?

Outside observers and new students often think that the most challenging aspect of data analytics is its mathematics. Certainly, learning the principles and equations that underlie statistical analysis takes some study. Identifying the analyses that best apply to a given dataset and question also requires knowledge and experience. Complex analyses like multi-factor models can be difficult to structure and interpret correctly. However, persistent students soon learn that statistical methods are consistent and closely interrelated. Once they grasp common principles, the routine work of calculation can be left to software.

What remains difficult, even for experienced analysts, is dealing with the variety and noise of real, raw data. Clients’ information can come in wildly differing formats and can contain inconsistencies and irregularities that require extensive cleanup. At best, data cleanup takes time and care, but at worst, messy data can make analyses uncertain or useless.

Another hurdle for new analysts is the challenge of learning unfamiliar, multi-functional software tools like Tableau and Power BI and programming languages like SQL and Python. If students are not already technologically fluent, mastering these skills can take considerable time and thought. Even experienced analysts must continuously learn newer programs, updates, code libraries, and coding techniques to stay current and competitive.

Finally, visualizing and reporting analyses can be difficult to master. Not only must results be arranged in ways non-analysts can read and understand, but these reports must also explain exactly what employers need to know. Even if one’s underlying analyses are valid, a mishandled graph or description might yield useless or even misleading conclusions, if not guided by communication skills and experience.

How Long Does It Take to Learn Data Analytics?

Data analytics is not a single skill but an array of related abilities, including knowledges, techniques, and tools. Students first learn core concepts and methods for data collection, organization, analysis, and reporting, either in an introductory course or the first part of a longer study program. A one- or two-day class might show a student how to perform simple analyses in a single environment such as Microsoft Excel, Tableau, or Python. A longer tutorial, from three days to a week long, can give a firmer foundation and might introduce multiple tools.

Expanding on these basics takes more than a few class sessions. Students need to practice multiple analysis methods and work with larger, realistic datasets before they can consistently apply these techniques. Similarly, there are many visualization methods beyond simple charts and tables such as 3D models, maps, and infographics. A dedicated data analytics student should also build proficiency with multiple technical tools. To achieve consistent proficiency in data analytics, a student will need a minimum of two to three months of study and practice, possibly through a bootcamp, certificate course, or set of short courses. This is also a realistic minimum for an entry-level analytics position, longer for more specialized work like financial analysis.

Even this degree of ability is limited to a subset of techniques and tools. Full professional status as an independent analyst takes much longer to achieve. Certification programs, which train analysts to a verified standard, can last from four to six months. Degree programs that instill broader expertise will run even longer, from one to four years. Students will spend this additional time learning more sophisticated data collection, cleanup, modeling, and visualization techniques; practicing with larger, less orderly datasets; and learning additional technical tools, automation methods, and communication skills. Fully mastering these abilities takes even longer. Becoming a Senior Data Analyst—able to manage any analytic task, devise and conduct complete analysis projects, consult with clients, and direct and train junior analysts—takes around three to four years of continuous study and experience.

Should I Learn Data Analytics in Person or Online?

Since most data analysis work is done on a computer, online classes are a natural fit. Live online study conducted by video conferencing can offer nearly the same interaction as in-person attendance but allows students to work comfortably from the site of their choice, use their familiar computer, and avoid the costs and limitations of travel. By removing the need for a local provider, online study expands a student’s course options. Online classes also tend to be less expensive than in-person classes. Although students must supply their own hardware, online courses often offer free or discounted downloads for necessary software.

Still, some students find online study difficult. Sensory, attentional, or technical limitations can interfere with remote learning and may be avoided with in-person studies. In-person instruction also allows the fastest and clearest interactions with an instructor. As another advantage, most physical schools provide all the hardware, software, and training materials that students need. These benefits make in-person study necessary or preferable for some students.

Both online and in-person classes require adherence to a fixed schedule, making it challenging for students to balance work and family commitments. Live classes are also restricted by set topics and pace. An alternative, self-paced learning, eliminates these issues through pre-recorded video lessons and interactive materials, allowing students to study at their convenience and their own pace. This format is cheaper due to the absence of a live instructor, but students may face delays in receiving feedback or support, as most self-paced courses provide only limited interaction through message boards and occasional chat, phone, or email support. These delays can slow progress and potentially raise costs for timed courses, hindering completion. Additionally, some students may struggle with self-guided study, requiring motivation from live classes, and pre-recorded courses may lack variety and up-to-date content.

Can I Learn Data Analytics Free Online?

Data analytics is a valuable, high-demand skill set. As a result, many free online resources offer to help people learn about data analytics and improve its various skills. These lessons come in a variety of formats, including videos, text, interactive websites, and downloadable programs and exercises. Video lectures are the easiest to access, available even on social media sites like YouTube. For example, Noble Desktop’s YouTube channel hosts a video series on Python, Data Science, and SQL.

A free subscription to several skill training websites—including Udemy, Coursera, CareerFoundry, and OpenLearn—grants access to many data analytics classes. Technical training sites, like Datacamp, Codecademy, and Springboard, also regularly feature free data analytics courses. Further, institutions like MIT, Harvard, and Yale and businesses like Google and Amazon host free courses to promote data analysis skills.

What Should I Learn Alongside Data Analytics?

Learning more about data science can give data analytics students stronger grounding, a better understanding of how analyses work, and a broader skill set. Studying data science also increases a worker’s employability and potential income. Similarly, a deeper study of statistics will enable students to construct more detailed analyses and visualizations, read results better, and avoid incorrect conclusions. A third fundamental skill, and the one most often neglected by novice data analysis students, is communication. The most sophisticated and accurate technical work is useless if it does not answer a client’s questions and help resolve their problems. Data Analysts must not only provide useful visualizations and reports but also discuss a client’s needs and translate these concerns into appropriate analyses. Classes on business communication can help analysts conduct interviews more effectively, discuss their progress more clearly, and write better reports.

Additionally, data analytics uses a wide variety of technical tools, including software packages, general and specialized programming languages, and some web-based and mobile services. A given data analysis course might only focus on one such tool such as Excel or Power BI, or cover several tools, but a single study program can never address the full range of available tools. Students will benefit from studying other tools on their own, including statistical software like SAS and Matlab, database management systems like MySQL and MongoDB, data-specific languages like SQL and R, general programming languages like Python and Java, or general research systems like Jupyter Notebook. Certain analytic tools are designed for specialized work such as RapidMiner for data mining, Apache Spark for exceptionally large datasets, or Google Analytics for website monitoring. In some cases, an analyst may need to learn a specific tool for a particular task or employer. For example, some businesses prefer to use Amazon Web Services (AWS) Analytics Services for their data collection, storage, and analysis.

Industries That Use Data Analytics

Data analytics is an industry in itself. Professional Data Analysts act as data-driven consultants for nearly every kind of business. Big Data firms handle volumes of information organizations cannot manage for themselves. Business intelligence, logistics, and expert data system providers provide sophisticated analyses that offer novel insights, strong predictions, and clear answers to complicated problems. Certain industries, though, need these services so frequently that they employ internal analysts, many specialized to better understand their chosen field. These industries include finance, marketing, sales, manufacturing, transportation, healthcare, scientific research, and government.

Data Analytics

The data analytics industry includes businesses as diverse as the clients they serve. Self-employed Data Analysts act as independent consultants to address data-driven questions, often for small businesses that cannot hire a full-time analyst or consulting agency. Data analytics firms range from small local agencies to enormous, international corporations. Analytics firms often serve multiple clients, assist with large research projects, or provide long-term support, and can offer cutting-edge and computationally demanding approaches. Local business intelligence (BI) and ‘Big Data’ companies in Pittsburgh include Data Ideology, Uptima, and Innovative Systems, Inc.

Many individual analysts and most agencies specialize in particular industries, offering approaches and expertise specific to that field. Tailored analysis work is often offered alongside related services like consultation, software development, and risk management. For example, in Pittsburgh, Allovision IO collects and processes vehicle-based data to create maps of infrastructure to assist planning and decision-making for transportation and government.

Marketing

Marketing agencies and departments connect businesses with customers through services like research, product design and testing, branding, advertising, social media management, and customer retention. Many of these functions need extensive data collection and analysis, so these businesses frequently employ Data Analysts and Market Analysts. Market analysis is also a common specialty for data analytics firms, particularly when helping entrepreneurs or businesses evaluate a potential product or sales campaign.

Business Intelligence

Market analysis overlaps with business intelligence (BI), which draws data from market research, sales, and investment to guide decisions on manufacturing, logistics, sales, employment, and facilities. Data analysts also assist manufacturers by studying efficiency and processing data for engineers and research and development teams.

Finance

Financial companies are another major employer of Data Analysts, particularly as Financial Analysts. The financial industry includes banks, credit unions, lenders, and similar customer-focused services; financial advisors, investment firms, and brokers; ensurers; and financial technology (FinTech) providers. Financial Analysts may assist customers with investment decisions, help build FinTech products and services, or provide internal guidance to financial organizations. Self-employed Financial Analysts and Consultants particularly need data analysis skills to yield valid recommendations from client records and market information. Traders and investors similarly benefit from data analytics training, both to perform their own investigations and to better understand third-party analyses.

Scientific Research

Nearly all research is built on data analytics. Scientists collect data through natural observation or experimental manipulation and produce data that can be queried to obtain new knowledge and predictions. Data analytics also explains how to clearly and validly explain these findings. Thus, most scientists study and practice some form of data analytics, though only some research programs handle large datasets that require sophisticated analysis tools. Still, an increasing number of scientific fields are finding uses for methods like data mining, machine learning algorithms, and meta-analyses (analyses that combine data from multiple studies). For example, DNA sequencing is a massive data procedure requiring numerous stages of collection, analysis, and comparison. Some research organizations even hire full-time Data Analysts to handle their data storage, processing, and reporting needs. These analysts may suggest new research designs and models that take fuller advantage of their skills. Data science, too, is a scientific field that includes data analytics and also explores the nature of data, especially the properties of real-world datasets, human awareness of information, and our behaviors toward it.

Government

Government organizations, from federal agencies to local councils, need to collect and understand various kinds of information. The most obvious data sources are population statistics, but different agencies might need to explore public health, education, utilities, transportation, construction, crime, finance, economic, or housing data. If governments do not outsource these tasks, they must employ internal analysts. Data analysts also find regular employment with political parties, campaigns, and policy consultants, often examining polls and other public opinion data and producing reports on subjects under debate.

Healthcare

The healthcare industry includes hospitals, medical offices, home care providers, ensurers, pharmaceutical companies, and medical device manufacturers. Patient care produces enormous amounts of data, which facilities, governments, and related businesses track to identify expenses, inefficiencies, effectiveness of treatments, and needs like staffing, training, supplies, and infrastructure. Data analysts also assist with medical research, including studies of the healthcare industry as a whole, investigations of diseases (e.g., epidemiology), and tests of specific treatments. Many data analytics firms specialize to serve the healthcare industry’s many needs. Analysts are also employed by companies developing healthcare technologies with embedded analytic functions such as medical imaging devices and expert diagnosis systems.

Data Analytics Job Titles and Salaries

Data Analyst

Data Analyst jobs are often flexible, generalist positions. These Data Analysts apply analytic techniques to varied datasets to address a wide range of needs. They may be self-employed or employees of a data analysis firm or data-driven business. Some Data Analysts are members of data management teams including Data Scientists, programmers, and consultants.

Data Analysts typically start at entry-level positions like Junior Data Analyst, managing data collection and storage, cleaning data for more senior Analysts, and building visualizations and reports from completed analyses. A Junior Data Analyst in Pittsburgh starts at an average annual salary of $69,000. With a year or more of active experience, their annual salary can increase to around $79,000.

A full Data Analyst is usually defined as having three to five years of experience, a wider mastery of data analysis tools, and one or more certifications. Data Analysts often build and manage full analysis tasks as needed and may oversee the work of Junior Data Analysts. The average annual income for a Data Analyst in Pittsburgh is $70,000, varying depending on experience and duties. Some companies have finer grades of experience like Data Analyst I, II, and III.

Senior Data Analysts typically have 10 or more years of experience and may supervise subordinate Analysts. A Senior Data Analyst in Pittsburgh earns around $104,000 per year, with even higher salaries possible in certain industries or for positions with significant supervisory duties. These top positions may also be titled Data Analyst IV (or higher), Lead Data Analyst, or Principal Data Analyst.

Data Scientist

Data Scientists often perform analysis work but have studied data more extensively and can address the subject more broadly. These positions require additional study, and their duties are often more complex and wide-ranging. Data Scientists have research and technical skills that enable them to investigate data closely and address problems that are not yet well understood. Data Scientists are often hired by organizations that need to understand new domains of information and create original solutions. They also assist in the development of new technologies for data management, analysis, and visualization, either in academia or as employees of tech development companies. Some Data Scientists work for organizations that manage massive amounts of data; in these roles, they may collaborate with or supervise one or more Data Analysts. Data Scientists in Pittsburgh earn an average annual salary of $88,000.

Data Engineer

Data Engineers are programmers and technical experts who work alongside Data Scientists and Data Analysts to create technological solutions for data work. Data Engineers are often employed by software and web developers building data-driven applications but may work in any industry that handles large quantities of data. Data Engineers create and maintain:

Data collection systems or ‘pipelines’ that move, combine, and transform information from various sources
Data infrastructure, including databases, ‘warehouses’, platforms, and displays
Data processing programs, including statistical and imaging software
Machine learning and other artificial intelligence systems

The average annual salary for a Data Engineer in Pittsburgh is $109,000.

Machine Learning Engineer

Another application of data analytics is in machine learning and artificial intelligence systems. These technologies gather, organize, and interpret extremely large datasets, using their analyses to choose actions, including the creation of novel analyses in response to new data and problems. Machine learning is in increasing demand, used in areas like internet search, content generation, and market analysis. Machine Learning Engineers combine data analysis techniques with expertise in software development and adaptive algorithms. They often work with Data Scientists, Analysts, and Engineers to build expert systems that incorporate specific data types and models. Salaries for Machine Learning Engineers in Pittsburgh average about $145,000 per year.

Data Analytics Classes Near Me

ONLC Training Centers offers in-person technical training at over 300 local classrooms, including a site in Pittsburgh. Rather than presenting a comprehensive data analytics course, they teach most technical tools used in analysis work, including Microsoft Excel and Power BI, Tableau, R, SAS, SQL, and Amazon Web Services. One of the most directly applicable courses, PL-300: Microsoft Power BI Data Analyst Course, is recommended for students with prior experience in data analysis and cloud computing. This three-day course connects these methods with Power BI, teaching how to collect, clean, transform, and model data in Power BI and then generate visualizations in multiple formats including online dashboards. The course also covers some advanced analytic techniques and the creation of complete workflows through Power BI, including data security and report publishing with Workspaces.

Technical training school New Horizons has classroom locations in cities worldwide, including Pittsburgh, although its data analytics courses are primarily offered live online. These classes teach data analysis skills grounded in specific technical tools like Microsoft Excel, Microsoft Power BI, Tableau, Python, and Amazon Web Services (AWS). For students already familiar with Excel, the short class Excel—Data Analysis with PivotTables is a quick way to add valuable analysis skills. This half-day class teaches students to use Excel’s Pivot Table function to quickly summarize and arrange large raw datasets for analysis. Microsoft Power BI—Data Analysis Practitioner is a two-day class demonstrating Power BI’s analysis capabilities, particularly emphasizing its visualization and modeling functions. For a more complete exploration of data science and a more powerful and flexible toolkit, Python for Data Science combines three full days (or five evening sessions) of live training with up to six months of access to additional on-demand course content. This course teaches Python users how to employ the NumPy, Pandas, Matplotlib, and Seaborn libraries and the Jupyter Notebook development format to create custom analysis and visualization tools, including integration with Excel and SQL databases. Students seeking professional, certified status in data analytics can also pursue training for either AWS Certified Data Analytics or the Certification in Business Data Analytics (IIBA®- CBDA).

General Assembly’s live online bootcamps and short courses are thorough professional training programs that take students from minimal experience to career readiness. Their Data Analytics Bootcamp runs for three months on a full-time schedule, first covering the fundamental concepts and statistical techniques of data analysis and then teaching multiple technical tools. These tools include the most commonly used analysis programs and languages: Microsoft Excel, Power BI, Tableau, SQL, and Python. The bootcamp also includes a unit on AI tools for analysis. Part-time, evening students can complete the bootcamp in about eight months, though they will need more self-guided, independent study. For students who need a faster introduction to data analytics, GA’s Data Analytics Short Course covers the same conceptual and statistical basics and data analysis techniques but only includes training in Excel, SQL, and Tableau. The Short Course can be completed in one week in its full-time format or about ten weeks for part-time students. Note that graduates of this course—or experienced data analysts who want to add AI automation to their work—can also take a short workshop, AI for Data Analysis and Visualizations. This class, conducted over two evening sessions, uses ChatGPT to demonstrate how to generate custom analyses and visualizations from datasets and prompts.

Certstaffix Training’s live online data analytics classes are arranged around specific tools including Microsoft Excel, Tableau, SPSS, SQL, and Python. For students already familiar with Excel, the most immediately usable data analysis skills will come from Excel—Data Analysis with PivotTables, a half-day class that demonstrates how to use Excel’s Pivot Table function to summarize and order large datasets to simplify analyses. Data analytics students planning to use SPSS, a statistical analysis program, can begin with Certstaffix’s prerequisite course, Introduction to SPSS, then progress through Introduction to Statistical Analysis using SPSS, Data Management & Manipulation using SPSS, and finally, Advanced Statistical Analysis using SPSS. Each class consists of two full-day online sessions which include hands-on exercises and teach SPSS as part of an integrated data analysis program. Similarly, Tableau Desktop, a data analysis program with prominent visualization features, is covered through Basic, Advanced, and Business Intelligence courses, each two sessions long. Students can instead book a combined Tableau Desktop Combo to cover all three courses in six days.

Fullstack Academy’s live online classes primarily feature coding, engineering, and cybersecurity, but they also offer an extensive Online Data Analytics Bootcamp that covers a wide range of analysis tools and skills, including Excel, SQL, Amazon Web Services, Tableau, Python, and generative AI. This beginner-to-professional course runs for about 10 weeks for full-time students or 26 weeks for part-time evening enrollment. This duration gives students time for multiple workshops and practice projects, culminating in a capstone project using all of the course’s tools: a professional-quality analysis for each graduate’s portfolio. The course’s curriculum also prepares students for the Tableau Desktop Specialist and Amazon Web Specialist certification exams.

Noble Desktop offers live online classes for coding, design, and business skills. Their data analytics courses include a professional certificate program and several bootcamps teaching data analysis using specific technical tools. Noble’s Data Analysis Certificate program is a six-week course for full-time students or 24 weeks on a part-time, evening schedule. This program begins with a unit on Excel’s data analysis features and then covers the foundational concepts, techniques, and applications of data analytics. Next, students learn to code custom analyses in units equivalent to Noble Desktop’s Python for Data Science Bootcamp and SQL Bootcamp. Subsequent units teach automation methods, data visualization techniques, dashboard creation, and machine learning algorithms in Python. Finally, a unit comparable to Noble Desktop’s Tableau Bootcamp teaches analysis and visualization in this popular program. Certificate students can also enroll in a bonus course, Python for AI: Create AI Apps with Flask & OpenAI, which adds AI tools to their data analysis repertoire. In addition to their graduation certificate, students receive eight 1-on-1 mentoring sessions they can use to discuss difficult topics, polish their professional portfolio, or set career goals.

Noble Desktop’s short class, Data Analytics Foundations, introduces data analytics and explains its general principles and techniques using examples in Excel. This two-day class helps students explore this field and plan their further studies. Students who need to learn multiple data analysis tools for personal or professional use should take Noble’s Data Analytics Technologies Bootcamp. This two-week course combines multiple shorter courses on Excel, SQL, and Tableau, focusing on their combined use in data analysis work.

Data Analytics Corporate Training

Organizations seek data analytics training to improve existing analysts’ skills or to add analysis techniques to their members’ repertoire. Noble Desktop can provide live data analytics training either privately, in custom classes held onsite or online, or through their online open enrollment. Organizations can customize their private classes, highlighting the skills they need most and even using their preferred datasets. Custom scheduling is also available for private training sessions. Alternatively, any of Noble Desktop’s regular online data analytics classes are available for open enrollment. Either way, clients can choose a basic introduction, an in-depth bootcamp, an advanced course, or a full certificate program in data analytics, or include data analytics within a longer course on data science or machine learning. If your members need differing schedules or training, you can purchase vouchers for Noble’s regular online classes, at a discount for bulk purchases. Then, you can distribute these vouchers and allow members to book the classes they need at their available times. All Noble Desktop classes include supplemental study materials and access to class recordings for later study and practice.

Contact Noble Desktop at corporate@nobledesktop.com for a free consultation to discuss your corporate training needs. A consultant can answer your questions, help you create the perfect custom training program, book class sessions, or sell bulk course vouchers.

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