Develop data analytics skills using Excel, SQL, and Tableau, learning how to transform raw data into meaningful insights that inform better 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.
The field of data analytics dates back thousands of years, long before the term “data analytics” could be coined. Ancient tribes whittled notches into bones to keep a record of their supplies and trade activity in 18,000 BCE. In ancient Babylon, the abacus was introduced in roughly 2400 BCE to store data and perform basic calculations. In 1663, the first formal statistical data analysis was conducted by John Graunt on The Plague. Then, in 1880, with the introduction of the Hollerith Tabulating Machine, it became much quicker and easier to manage data such as that which was collected for the U.S. Census Bureau. Nearly a century ago, modern data storage began using magnetic tape. The U.S. government then created the first data center in the world in the 1960s. As the internet became increasingly popular from the 1990s onward, the amount of data being created exploded and, along with it, the need for more effective data mining strategies.
Today, data is a core component of almost everything in the modern digital realm. On a daily basis, approximately 2.5 quintillion bytes of data are created. In many industries, so much data is created in a given day that humans wouldn’t be able to process even a fraction of it without the help of a computer. As more organizations extend their online presence and continue to incorporate new technological advances, it’s expected that even more data will be created. This information isn’t valuable to the company until it can be analyzed, though. This is where a Data Analyst’s training is required. These professionals draw on their training in statistics, math, computer programming, and data visualization to explore what these numbers mean and transform them into useful insights that can be shared with organizational stakeholders and used to improve operations and increasing revenue.
Data analytics is a broad field that involves working with different techniques to gather, analyze, and visualize raw, unprocessed data to extract trends and insights that can be used to help organizations better understand their current operations and make more-informed decisions. The basic goal of the data analytics process is to use the conclusions gathered from studying the data to offer predictions that will inform the sorts of choices a company will make moving forward.
Learning data analytics is a valuable and versatile skill set that’s in-demand across industries in which data is gathered. The heart of the data analytics process involves solving real-world problems that organizations face. These challenges may pose hurdles for the employees, customers, or the entire way the organization operates. Those who have data analytics training learn how to approach these problems with an analytical mindset, which is a skill that has far-reaching benefits in many contexts and situations that also extends beyond the professional sector. As an increasing number of businesses turn to the data they’re collecting and value its possibilities for informing their decision-making process, the need for those who know how to make sense of this information is expected to continue to increase.
Data analytics is a useful tool with many real-world applications. Within the healthcare industry, it can help prevent diseases, optimize resources, and improve the care options available to patients. Healthcare analytics is used to identify disease outbreaks, evaluate the efficacy of drug treatment regimens, and provide high-risk patients with personalized treatment. In finance, data analytics training is handy for tasks like detecting fraud, evaluating risk, and informing investment strategies. Financial institutions like banks work with huge volumes of raw data to spot suspicious transactions, provide customers with personalized financial advice on investment portfolios, and decide how credit-worthy loan-seeking individuals are. Data analysis tools and techniques can even play a role in how internet search engines perform to ensure the most relevant search results appear quickly for users.
In addition to opening many professional doors to high-paying careers, data analytics is an excellent way to acquire many related, valuable skills that extend beyond organizational problem-solving. During data analytics training, you’re likely to also become familiar with how to break down and clearly communicate complicated information and translate it so that audience members who don’t come from a technical background can understand it. The ability to clearly communicate is essential to any career path.
Once you decide to enroll in a data analytics class in Philadelphia, you may wonder what to expect from the coursework. While it’s true that the content you’ll encounter depends on the educational provider, the scope and difficulty level of the program, and its duration, there are a handful of core skills you’ll likely learn regardless of the class you opt for. In the following sections, several essential data analytics tools and skills will be briefly summarized, including the four types of data analysis, data cleaning, programming languages that are helpful for analysis, Microsoft Excel, and data visualization, so you’ll have a clear idea of what to expect from your studies.
Beginner-friendly data analytics classes often begin by providing learners with a brief overview of what data analysis is, as well as the different types of analysis. If you enroll in one such program, you’ll likely encounter instruction that teaches the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics involves working with historical data to gain insights into past trends or events so that data analysts can get a clear idea of what happened at an earlier time. Diagnostic analysis is a type of analytics in which the reasons behind these past trends or events are explored to provide an understanding of why something occurred for the business. In predictive analytics, students apply machine learning algorithms or statistical models to historical data to predict outcomes or future events. The fourth form of data analysis is prescriptive analysis. It involves applying predictive analytics and optimization techniques to historical data to shed light on the best course of action for the organization based on the predicted outcomes.
Data cleaning is an essential part of the data analytics process and is one you’re likely to encounter during your studies. Also known as data cleansing, it entails getting data ready for analysis. Data cleaning varies depending on the type of data being used and the extent of preparation required to make it uniform and workable. It may mean deleting any data that’s repeated, incorrect, incomplete, or irrelevant. It also may entail formatting data. The goal of data cleaning is to ensure that the data being used for analysis will yield results that are as accurate as possible. While it may seem straightforward to clean data, this process can become complicated in some instances because it may require more than simply deleting existing numbers and substituting new ones in their place. Sometimes, it’s necessary for data analysts to experiment with new ways to fully maximize the existing data’s accuracy so this information is viable. This sometimes requires correcting empty fields or missing codes, standardizing datasets, or fixing errors in spelling or syntax. Data analysts work with various tools for cleaning data such as RingLead and Oracle Enterprise Data Quality.
The data analytics process typically requires knowledge of one or more programming languages. Although there are many programming languages, some are more suited for working with big data than others. This is why most data analytics coursework teaches students coding languages like SQL or Python. SQL, which is short for structured query language, remains the industry-standard for working with databases. Often, this language is the first programming language aspiring data analysts study. SQL is a valuable tool because it’s used to query databases and help users access data and statistics. One of the benefits of using this language includes its relative user-friendliness and ease of learning. It assists with fast querying, and since it’s non-procedural, no knowledge of traditional programming logic is required to use it.
Python is another popular coding language you’ll likely encounter if you study data analytics. It’s been used for more than thirty years and remains one of the most popular coding languages in the world to this day. Python is a go-to for data professionals because it can assist with tasks like gathering data, analyzing and modeling it, and creating visualizations that graphically represent the results of analysis. Python is generally considered easy for individuals to learn. It has applications in machine learning, deep learning, and artificial intelligence (AI). Data analysts rely on this language’s data science libraries such as NumPy, Matplotlib, and Pandas, to perform a range of technical tasks. This free, open-source language also provides users with various open-source tools that are helpful for machine-learning tasks and data visualization.
Most data analytics programs in Philadelphia include instruction on how to work with Microsoft Excel for organizing, analyzing, and visualizing data. Excel is a widely popular spreadsheet application that is composed of rows and columns that intersect to create cells. Each cell contains a single piece of information or one point of data. Learning Excel is a useful skill that helps data professionals quickly perform tasks with cells such as formatting information, spotting trends, or retrieving data. Excel skills can greatly simplify the data analytics process and save users hours of tedious, manual entering. Excel is a go-to for data analysts because its clear organization can make data retrieval easier and faster. The actionable insights gleaned from spreadsheets can indicate revenue patterns, marketing trends, or profit margins. The complex data stored in Excel can also be depicted in easy-to-understand visual representations like graphs or charts.
Data visualization is one of the most commonly covered topics in data analytics study. The process of visualizing data entails transforming the insights gathered from raw data during the data analytics process into visual representations such as charts or graphs. These visual depictions are created so that audience members, even those who don’t come from a technical or analytical background, can understand the information and quickly process it. Data visualizations don’t just represent essential data findings; they also tell a larger story about this information to place it into a greater context of why it’s important for the audience. Effective data visualizations also ensure that patterns, outliers, or trends from within the data will emerge clearly. Software like Tableau and Excel are go-to tools for data analysts interested in visualizing data. These programs provide ways to make both basic and complex visuals, including heat maps, pie charts, infographics, scatterplots, waterfall charts, histograms, and box charts. Data visualization has many applications, including helping viewers identify areas for improvement within their organization, exploring variables in customer behavior patterns, and assessing risks.
Data analytics coursework isn’t just a great opportunity to acquire hands-on training in programming languages and data visualization software; it also can provide learners with new ways to communicate clearly. Strong communication skills are in-demand across industries and professions. They’re an essential tool that helps people quickly and accurately convey or understand information. Those who enroll in a data analytics program are often tasked with handling very complex datasets that are difficult to articulate. They must strive to find avenues into clarity to break down complicated data findings in a manner that others can understand and use. Data analysis training teaches students effective techniques for storytelling and presentation, which ensures that the information at hand will be accessible to audiences who may not otherwise be privy to data findings. Additionally, studying data analysis can also improve active listening capabilities, which is another core component of clear communication. Those who learn how to listen actively and understand what’s being said to them can use this training to not only be more engaged listeners and communicators but to truly grasp the information they’re being given.
Learning any new skill is a subjective undertaking, one whose difficulty depends on a host of factors such as your prior educational background, learning method and style, and ultimate goals for applying this knowledge. Studying data analytics is similar. Learning this skill set for your current career or for professional advancement into other roles may require much more time and training than acquiring basic data analysis skills for personal reasons. The difficulty of learning data analytics is affected by factors like whether you have prior experience working with programming languages like SQL or Python, if you have a background using spreadsheet software like Microsoft Excel, and if you already know how to communicate with databases or visualize data. Those who possess one or more of these skills will likely find it much easier and faster to learn data analytics than those who don’t.
If you’re interested in learning data analytics but don’t have knowledge of how to use data visualization software like Tableau, or if you don’t have a background in computer programming, there are several steps you can take that will make your learning process easier. A good starting place is to review the basic steps of the data analytics process. Data analysis typically begins with a driving question you’ll formulate and try to answer as you interrogate the data. The more clearly you can articulate this question, the more useful it will be as a frame for the analytics process. Once this question is formulated, the next step is to locate the data you’ll use that will be most useful for your organization. It may come from a data warehouse, which is a central location that stores data from multiple sources. Next, you’ll have to understand the data you’ve acquired. Some data analysts work with automated analytic features to streamline this process and ensure accuracy.
Once the data has been gathered, it’s necessary to clean it to eliminate any outliers or formatting inconsistencies. At this preprocessing stage of the analytics process, AI and machine learning techniques, including augmented analysis, can be useful. Next, data analysts approach this information with different tools and techniques, each of which is aimed at addressing the initial question. These tools include predictive analytics, Excel, Python, or statistical analysis. The results of this stage will then be shared with other internal or external stakeholders using a data visualization program like Tableau.
Becoming proficient in data analysis is a process that takes time and perseverance to master. One of the most common challenges associated with learning to analyze data is deciding which data to use. The data you select will need to be valuable and relevant to your organization’s decision-making process. It may also originate from multiple sources, which can pose additional challenges for data analysts when selecting the most relevant data. Another challenge in learning data analytics is understanding the information that’s been accessed. This may entail keeping an accurate record of data assets, including definitions of the columns in a data warehouse table. Some aspiring data analysts also consider data cleansing a challenging, tedious process. It involves encoding variables, formatting data, and deleting any redundant variables. While this is a time-consuming process, it’s vital to ensure the data that will be analyzed is clean and high-quality.
Learning data analytics involves acquiring knowledge of a range of related tools and skills. This involved process requires time, practice, and patience as you learn to work with spreadsheets, computer programming languages, statistics, and data visualization. The length of time it takes to learn data analytics also depends on the level of expertise you’re hoping to acquire in this field. If you’re interested in learning the basic concepts and tools, it’s possible to do so in approximately three months. However, this length of time can vary between several months to several years, depending on certain key factors. One key determinant is the type of training format you select. Enrolling in a bootcamp or certificate program in Philadelphia is an excellent way to receive hands-on training in various data analytics tools and programs and can greatly expedite your learning speed. Those self-taught in data analysis may require much more time and practice to learn basics like SQL, Tableau, and Excel. While the time necessary to learn data analytics depends on your prior background in math, your experience querying databases, and your previous training with spreadsheets and data visualization, some experts estimate that it can take between three and four years to fully master this skill set and use it professionally.
The decision of whether to learn data analytics in-person in Philadelphia or in the online environment is a choice all learners will make before enrolling in a training program. Both learning formats have their unique strengths that should be considered when making this decision. If you’re the kind of learner who prefers the traditional classroom environment, then enrolling in an in-person class, certificate, or bootcamp in Philadelphia is a great way to learn data analytics. These programs provide hands-on training in a small class environment. Students can focus on learning one data analytics tool such as Excel or SQL, or enroll in a more comprehensive program that teaches data analytics as a broad topic. In addition to learning directly from an expert instructor with real-world data analytics training, you’ll also benefit from the support of a cohort of learners alongside you for the learning journey. Training facilities provide access to computers that have the most up-to-date software and programs already installed, including Tableau and Excel. If you want to learn data analytics through in-person classes, you will need to have a form of reliable transportation to commute to and from class. This may be prohibitive for those who don’t have access to a car or who live far from a major training facility in the city.
If you’d like to receive the same learning benefits as in-person coursework but want to avoid the hassle of battling traffic to attend class, live online data analytics programs are an excellent alternative. Classes are available for those who are just getting started with data analytics, as well as intermediate or advanced learners interested in mastering this field for professional reasons. During live online coursework, students connect using a teleconferencing platform like Zoom. They receive live instruction from an instructor who’s available to answer questions and provide additional support and guidance. Participants can even share their screen with the teacher (with permission) for additional support. Since this form of study takes place online, all coursework can be completed from any space that has a reliable internet connection. One important consideration of live online data analytics training is that it does require that participants commit to attending regularly scheduled classes. This may mean having to rearrange your work schedule to study data analytics.
The most flexible type of data analytics training is asynchronous coursework. Unlike in-person or live online classes that meet in real-time, asynchronous coursework is pre-recorded and placed online. Students can complete their studies whenever they wish and do so from any location. One of the main benefits of this type of training is that it’s entirely self-paced. In live classes, the instructor dictates the speed lessons are covered and the time devoted to each topic. In asynchronous data analytics classes, you can pause lessons as often as needed, rewind them, and even rewatch them completely to ensure you’re learning the content. Cost-wise, on-demand content is typically a more affordable learning option since it was recorded earlier. However, it’s important to consider that no live instructor is available in these sessions, which means that learners will have to find answers to questions on their own. This may make it challenging for some students to grasp complex analytics concepts. This is why some individuals may prefer to begin their data analytics learning journey with on-demand content but ultimately will need to gravitate into more structured, live training to fully master advanced data skills.
If you want to learn more about data analytics but don’t want to invest hundreds or even thousands of dollars in your studies, free online content is available:
Noble Desktop offers several excellent free online resources for those looking to learn data analytics. It has a free SQL seminar, video content on Python fundamentals, and video content on data science. Noble’s Learn Hub also provides a range of up-to-date articles on relevant data analytics skills and topics, including information on how to install and work with Tableau Public, use SQL for business analytics, and perform statistical data analysis in Microsoft Excel.
CareerFoundry offers a Data Analytics Short Course for free for those interested in learning about the various analytics roles, the skills and tools necessary to pursue a data career, and the core components of the data analytics process.
FreeCodeCamp’s Data Analysis with Python is intended for those who don’t have a background in computer programming and want to learn how Python is used in the data analytics process. This 28-section program covers essential skills like working with Pandas and NumPy for data processing, as well as visualizing data with Matplotlib.
Before selecting free online training material, it’s essential to research the provider who posted the content to ensure that they’re reputable and that the material they post is up-to-date and reflects the current best practices in the field of data analytics. While you can study data analytics for free online, it’s important to remember that this content is all pre-recorded, which means no instructor is there to provide guidance or answer questions. If you’re interested in learning data analytics for professional reasons such as to complete a work project or to pursue an entirely new career path in data, you may benefit more from a more structured, live approach to learning this skill set.
While you study data analytics, you may consider learning other related skills that can make you a more well-rounded employee at your current job or improve your job prospects for new roles.
Machine learning is one such skill. Machine learning is a form of artificial intelligence that focuses on creating statistical algorithms that can learn from data and eventually perform tasks without specific instructions to do so. Machine learning can be a valuable tool that helps data professionals more efficiently perform data analytics tasks such as preparing data, evaluating datasets, and proposing visual recommendations based on data findings. Those who work with data also use machine learning for tasks like statistical inference or model-building automation.
Another useful skill you may consider learning alongside data analytics is business analytics. Business analytics is a field in which various technologies, tools, and strategies such as data mining, statistical analysis, and predictive analysis, are applied to help companies obtain essential information about how their business has been operating, which can be used to fuel its current decision-making process. The goal of business analytics is to learn more about business drivers and use these insights to improve the company’s efficiency and profit margins. Business analytics is closely related to the field of data analytics, but these two differ in important ways. Data analysts usually work closely with the actual data, whereas business analysts are more focused on exploring their organization’s needs and providing data-driven solutions.
Data analytics is a staple across Philadelphia industries in which customer or client data is collected. Those with data analytics training can pursue a range of careers in sectors like cybersecurity, finance, advanced manufacturing, and healthcare. Data analytics is also a widely used tool for Philadelphia-based nonprofit organizations. In the following sections, each of these industries will be briefly explored, as well as the role data analytics plays in them.
Philadelphia has a robust tech scene in which many cybersecurity companies like NorthStar Technology Services and Dataprise are currently thriving. These organizations provide those in the area with services like network security compliance and threat detection, which play an essential role in helping local businesses operate safely and securely. The city’s close proximity to major cities like Baltimore and DC has contributed to the area’s increasing need for qualified cyber professionals to prevent ransomware attacks and safeguard data from external threats. Within the field of cybersecurity, data analytics plays an important role in that it can be used to detect and prevent attacks. Most modern security systems operate by analyzing user behavior, system logs, and network traffic in search of any possible security breaches. With the help of data analytics, Philadelphia area businesses can take the necessary measures to be proactive with security and spot threats in real-time, thus protecting sensitive data.
Another prominent Philadelphia industry is finance. The Great Philadelphia area’s financial services industry is home to huge financial companies like Vanguard, Lincoln Financial, Colonial Penn, and FS Investments. The city’s market is a prime spot for investment houses and banks like Citizens Bank, JP Morgan Chase, and PNC Bank to establish operations. Currently, approximately 223,000 people in Philadelphia work in the finance sector. Data analytics training is an essential tool for those involved with financial analytics. Those with the skills to analyze financial information can put their training to use with risk assessment, fraud detection, or investment coaching. Financial institutions like banks must handle huge volumes of data on a regular basis so they can evaluate how credit-worthy individuals are, identify suspicious transactions, and provide useful advice to clients on their investment portfolios.
The advanced manufacturing sector in Philadelphia is another staple of the city’s economy. The greater Philadelphia area was once hailed as the “Workshop of the World, ” a term that derives from the city’s focus on advanced manufacturing subsectors like plastics, aerospace, biopharmaceutical, and chemical. Major players like DuPont, Lockheed Martin, Merck, and Dow Chemical all have manufacturing centers in the city. Currently, 190,000 Philadelphia residents work in advanced manufacturing in this region. In the advanced manufacturing sector, data analytics continues to transform operations, streamline processes, and improve the quality of the products being created. It’s a powerful tool that helps manufacturers anticipate which equipment is likely to fail, eliminate downtime, improve machine performance, and study sensor data. Additionally, manufacturers can draw from data analytics techniques and tools to monitor production lines in real-time, which reduces costs and improves operational productivity.
Philadelphia is home to 37 hospitals such as Temple University Hospital and Children’s Hospital of Philadelphia, that offer patients both long-term and acute care. Education and health services are the leading sectors in the city and employ approximately 35% of the area’s nonfarm workers. This sector has experienced a 5.3% rise in employment in 2023, which is above the national average of 4.1%. Those who work in the city’s healthcare system handle vast amounts of patient information on a daily basis. Within the healthcare sector, healthcare analytics is an essential tool that ensures that diseases are prevented, resources are fully utilized, and patients receive the best possible care. Healthcare analytics is used to spot patients who are at high risk for chronic illness, reduce treatment cost options, and study behavioral patterns that can predict the likelihood of a patient becoming sick with a specific disease. Data analytics in healthcare is also used to help patients evaluate doctors and provide better methods for e-recordkeeping.
Currently, more than 2,200 nonprofits are located in Philadelphia. Some of the most prominent nonprofits in the city are the Children’s Hospital of Philadelphia, the National Philanthropic Trust, and the American Association of Cancer Research. Data analysis is a crucial tool for those who work in the city’s nonprofit sector because it helps both small and large nonprofits perform a range of essential tasks. Those with a background in data analytics can use these skills in the nonprofit sector to spot donor patterns and identify opportunities for growth. It’s also a powerful tool that can be used to increase supporter engagement and improve outreach efforts, as well as ensure that the strategies being used for fundraising are as effective as possible. Additionally, data analytics provides transparency for nonprofits that helps them provide evidence of the impact they’re having on the local communities they serve. Predictive analytics techniques, too, are essential for helping nonprofits stay on top of upcoming trends and be prepared to take advantage of upcoming opportunities.
If you have a background in data analytics, many high-paying career options are currently available in Philadelphia. The following sections will briefly explore career paths like Financial Analyst, Database Administrator, Marketing Analyst, and Data Engineer, as well as the corresponding salary ranges and job requirements for each role.
Financial Analysts are hired to assist a financial institution with tasks like researching market trends to find possible opportunities for investment. These professionals also handle vast amounts of data on international currency exchanges or the stock market to discover patterns that can inform future investment strategies. In the US, Financial Analysts earn an average of $74,000-$77,000 a year. Those who work in Philadelphia earn around $72,000 annually.
Database Administrators are involved with overseeing their organization’s databases to make sure they continue to operate optimally. They also work to protect their company’s data so that only authorized users can access this information. Database Administrators are employed in a range of industries, including finance, IT, computer systems design, and healthcare. On a daily basis, these professionals perform tasks like establishing performance baselines, determining database needs, modifying database structures, troubleshooting, updating permissions, and implementing database security measures. The current average salary in the US for a Database Administrator is $75,000-$77,000, though this pay rate varies significantly based on location of employment. Those employed in Philadelphia earn well above this average and make about $84,000 each year.
Marketing Analysts who work in Philadelphia are tasked with helping their organization review market trends to make more informed decisions based on current market conditions. These professionals may be hired to work as part of a bank or investment firm or to offer financial advice to private individuals or smaller businesses. Some Marketing Analysts research foreign exchange or stock market trends, whereas others may study informed pricing to ensure that marketing campaigns can be optimized. The findings they gather are then shared with business owners or high-ranking executives to help them make more sound financial decisions. The current average national salary for a Marketing Analyst is $62,000. Those employed in Philadelphia earn slightly under this value and can expect to make closer to $61,000 annually.
In Philadelphia, Data Engineers are involved with designing data infrastructures and formatting the data they contain into a system that can be used by Data Scientists to gather and analyze huge volumes of information. These individuals sometimes focus on warehouses, pipelines, or platforms. On a daily basis, Data Engineers write queries or code, combine data sources, create data pipelines, or Architect data stores. Data Engineers earn a competitive salary of approximately $131,000 a year in the US. In Philadelphia, average pay rates are closer to $121,000 annually.
If you’re ready to get started learning data analytics and are interested in finding coursework close to home, you can search for data analytics classes in Philadelphia to find the learning options most suited to your needs. Classes are available from many top providers for those who are just getting started working with data, as well as professionals interested in advanced-level content to take their data analytics skills to the next level. In the following sections, several popular local course offerings will be discussed, as well as the basic requirements for each program.
Noble Desktop’sData Analytics Certificate is a great option to consider if you’re looking to study several core data analytics tools and software. This comprehensive program combines several of Noble’s shorter bootcamps and classes: Data Analytics Foundations, Excel for Data Analytics, Python for Data Science Bootcamp, Python for Automation, Python Machine Learning Bootcamp, SQL Bootcamp, Python Data Visualization & Interactive Dashboards, and Tableau Bootcamp. In this intensive class, you’ll have the opportunity to learn how Excel is used to manipulate and visualize data and how to use Python’s scientific libraries. Instruction is provided on working with SQL to retrieve data from relational databases and use Tableau to visualize data. This certificate is available in-person in New York City and live online. All participants can retake this class for free for one full year. Tuition includes eight 1-on-1 mentoring sessions that can be used as the student wishes, either to revisit complex course material or for professional development. Financing and payment plans are available.
If you’d like to focus your studies on data visualization, Noble also offers a Tableau Certification Program.This class is intended to prepare you to pass the Tableau Desktop Specialist certification exam. This comprehensive program requires completing two classes: Tableau Level I and Tableau Level II. In addition, you’ll receive six hours of private tutoring. This class begins by providing you with an overview of the Tableau interface and how this software can be used to design a range of charts. You’ll then move into more difficult data visualization skills, including how to map data and create dashboards. During the private training sessions, you have the chance to dive deeper into complex class material or spend more time reviewing the content that will appear on the exam. Tuition includes the cost to sit for the Tableau Desktop Specialist exam and a free exam retake (if needed), as well as exam proctoring. Learners can retake this class for free for up to a year. If you pass the Tableau Desktop Specialist certification exam at the end of this program, you’ll earn professional Tableau certification that can be included on your resume to demonstrate your expertise to employers. If you’d rather not sit for the exam, you can choose an additional hour of private tutoring instead. This program is available live online and in-person in NYC.
In Data Analysis and Visualization Boot Camp, which is available from Penn Boot Camps, participants receive a thorough overview of how to analyze and visualize data. This comprehensive program covers core data tools like Tableau, Python, JavaScript, SQL, and Git/GitHub. Instruction is also provided on how Hadoop is used for big data analytics, machine learning, and MySQL. During this introductory-level class, those enrolled gain real-world experience in intermediate Excel concepts, fundamental statistics, Python programming, databases, front-end web visualization, business intelligence software, data ethics, and deep learning. This in-person program is available in Philadelphia.
Those who want to learn how to use SQL for data analytics can enroll in Certstaffix Training’s SQL Querying— Basic Course. This beginner-friendly class provides learners with training in how to group and summarize data, join clauses, write basic SELECT queries, build unions and subqueries, and work with this language’s built-in expressions and functions. This program is taught in the live online environment; participants can complete coursework remotely or use the computer lab provided in Malverne, PA. Tuition includes a free course retake for up to six months.
Certstaffix Training also offers Excel-specific training options for aspiring data analysts. In Excel—Dashboards Course, those enrolled learn core spreadsheet skills like how to create dashboards, worksheets, and workbooks. Instruction is provided on the basics of data analysis and how to use Excel to visualize data. Participants use PivotTables to analyze data and learn about various functions and formulas. By course completion, those enrolled will be able to use Excel dashboards to present visual insights. This program is taught live online; students can use the computer lab in Malvern, PA to complete their training.
In General Assembly’s Data Analytics Short Course, students explore core data analytics best practices and skills such as how to use SQL, Tableau, and Excel. Those enrolled learn how to collect, cleanse, analyze, and visualize data. Instruction is provided by data analysts who can share their real-world experience in transforming the data they collect into engaging, effective visualizations. All learners complete a portfolio-ready capstone project as part of this program. This class is available live online. Those enrolled can opt to enroll in this class at an accelerated, full-time pace or complete their studies part-time in the evening. Individuals who are new to data analysis can complete three hours of pre-work so they’ll be ready for the material covered on the first day. As a prerequisite, it’s suggested that learners take a short Excel workshop. Tuition includes live instruction and access to teachers and TAs during office hours.
Corporate data analytics training is an excellent way to provide those at your workplace with the skills needed to fully explore data to gather meaningful and useful insights. If your employees would benefit from learning the current best practices and skills needed to analyze data, Noble Desktop can help. Noble offers corporate and onsite Data Analytics training options for those at your workplace who are just getting started working with data, as well as experienced data professionals interested in mastering advanced data analysis and visualization practices. Coursework is available in popular data analytics tools like SQL, Excel, Microsoft Power BI, Tableau, and Python.
You can select from in-person or live online training options to ensure your employees find a training format that fits with their work schedule and availability. Noble can provide flexible corporate training options that occur on weekends or weeknights to ensure your employees can complete their coursework around full-time work or personal commitments. Coursework can even be customized so the training your employees receive is applicable to their professional needs. If you’re interested in training multiple employees in data analytics, discounted bulk vouchers can be purchased for Noble’s regular classes. If you’re ready to find out more about the exciting corporate training options Noble can provide for your employees, don’t hesitate to contact Noble today.