The best data analytics classes near me

Noble Desktop offers an immersive Data Analytics certificate program that is perfect for any student looking to learn all of the important professional skills needed to work with data. This course will provide students with hands-on experience working with data analytics tools and techniques and practical lessons in quantitative and qualitative data analytics practices. In this class, students will learn how to use Excel, Python, and SQL to build, query, and organize databases. Once students are comfortable with these tools, they will dive into the finer points of the Python programming language and learn how it can be used to automate data collection, create data visualizations, and build machine learning algorithms that can handle data analytics processes without the need for a human operator. Finally, students will learn how to use Tableau, one of the most versatile data visualization tools available, to create rhetorically persuasive and convincing graphs, maps, and charts.

This course is a career-focused program aimed to bring students up to speed on modern data analytics practices and provide them with the practical training they will need to find work in the industry or to apply these skills to their own professional lives. All of the lessons that students learn are modeled after real-world data analytics projects, and all of the course instructors have industry experience working with data analytics tools and techniques. Since all of the exercises are hands-on, students will leave the class with a series of sample projects, programs, and visualizations that they can add to their job materials, and all students will receive one-on-one career mentoring assistance to help them overcome the challenges of the job application process. Finally, students will be able to retake any or all of the component courses that make up the certificate program, either to get more practice or to cover skills they may have had difficulty with within one year.

If you plan to work with data, it is important that you learn how to write code and make use of the Python programming language since it is integral to almost all data-related tasks. In this course, you’ll learn how to write code in Python and how that coding knowledge can be applied to a host of different data analytics tasks. In this course, students will learn how to use Python to organize and collect data automatically from a range of different sources, how to use Python to query data (in complex ways), and how to create visualizations of your data. This course will also introduce students to the basics of machine learning and artificial intelligence algorithms, which can help significantly increase the speed and accuracy of your data analytics work. This course is a great introduction to the foundations of data science, and students enrolled in this program will learn the skills they need to start working with Python professionally.

While they often overlap, data science and data analytics are not interchangeable terms. Both require much the same skill set and training, but they serve different functions in the grand scheme of data-related tasks. Data scientists, for the most part, develop the tools and techniques that are used by data analysts to make practical use of the data they collect. For instance, a data scientist may be responsible for developing new Python applications that make querying data easier, while data analysts will use that new application to make their own data-related tasks more efficient. In this course, you will learn all of the skills you need to work as a data scientist and start building your own applications.

This course covers the use of Python and SQL programming languages in data science tasks, including machine learning, database construction, and querying applications. This course aims to provide students with the tools they need to start working with data applications at a high level, which is practical for any aspiring data analytics professional to understand. This course is taught by experts in the field and will provide students with practical, hands-on, career-focused training aimed at helping students prepare for real-world data science engineering tasks. This course will introduce you to all of the high-level aspects of data science and analysis, and it also offers career mentoring support and a free retake option.

In this course, students will get hands-on experience working with the most common data analytics tools and technologies that bypass the need to learn complex coding skills. In this course, students will learn how to use Excel, SQL, and Tableau, all of which are important tools for anyone looking to work in data analytics. Excel, the most commonly used spreadsheet program in the world, is the primary program for building databases and for organizing and querying data. SQL is a simple programming language that is utilized to write scripts and applications that query the data stored in an Excel database. Tableau is a data visualization tool that allows users to create informative and persuasive charts, graphs, and maps that help communicate data and make it easier to understand through interactive dashboards. This course will provide students with the hands-on training they need to utilize these tools in a professional context, and upon completing the course, students will be ready for more advanced data analytics training.

SQL (standard querying language) is a commonly utilized programming language that allows users to write complex queries to analyze huge amounts of data. Its primary function is to help users navigate databases and pull out specific data points that they can use to better understand what their data has to tell them. In this course, you will learn basic, intermediate, and advanced querying techniques, so even if you have no SQL experience when entering the course, you’ll be prepared to work with SQL to make the best use of your own data. You will learn how to write simple queries (such as finding certain value ranges), and you’ll build on these skills until you start writing complex subqueries with multiple, layered variables to find exactly the information that you need and to see correlations that would have been otherwise invisible.

Excel is, per Microsoft, the most commonly used desktop application in the world. This spreadsheet program is utilized in almost every field, and data analytics is no exception. Excel is often used to store the data that is collected and it has a number of rudimentary data querying and visualization features that allow users with a bit of training to make significant leaps in their data analytic skills. In this course, students will learn how data is stored and organized in Excel, how features like data validation and VLOOKUP can be used to query data, how Excel can be used to create charts and graphs based on data input, and how Excel can be utilized alongside other data analytics tools to create more complex and detailed analytic frameworks.

The final major tool you can attempt to learn as part of a data analytics education is Tableau, one of the most popular data visualization tools on the market. Unlike Excel, which can only be used to create static visualizations of the data in the sheet, Tableau can create dynamic, customizable, hands-on dashboards that collect the available data and update it in real time. This means that if you want to alter a variable, change a function, or look at a different set of subqueries, the dashboard will automatically update with the new information and create a new visualization of the data. This makes the application perfect for anyone who is looking to communicate their data findings in real time or have a constantly updating place to view and process the data they are working with (particularly as more and more data is added to the database).

Another option for an accelerated, immersive bootcamp is the Data Analytics Bootcamp from General Assembly. In this career-focused course, you’ll learn all of the major skills you need to become a data analytics professional, including how to use tools like Excel, SQL, and Tableau, how to write code in Python for databases and machine learning algorithms, and how to make quantitative and qualitative interpretations of your data. This course is focused on preparing students for a career in data analytics, so the lessons are designed to be applicable to almost any industry or field. Everyone who takes the course will receive professional training from experienced instructors and will receive access to the career support services offered by General Assembly. If you are looking for a comprehensive training program that will provide you with the skills you need to be job-ready upon graduation, this course is likely to suit your needs.

This course aims to provide students with all of the training they need to start working in the field of data analytics in a more condensed offering, even for students with no prior data analytics experience. Over ten weeks, this course will cover everything from the basics of Excel to advanced Python programming skills, with the aim of helping students find jobs in the field as soon as they graduate. Students will receive detailed, real-world training from expert instructors and will spend time building their own portfolios of data analysis projects. This will help prepare them for entry into the job market, and all of the enrolled students will receive access to General Assembly’s career support services, including their alumni network and job posting portal. This course aims to be an accelerated introduction to the professional skills used by analysts in almost all fields, making it a good course for those with the time and determination to commit to the program.

Fullstack Academy also offers a comprehensive data analytics bootcamp which aims to prepare students for a career in the field and for the kinds of regular professional tasks that they will be expected to handle. This course will provide students with training in practical data analytics techniques modeled on the kinds of work they will be expected to do in a professional context. Students will learn how to work with Excel and SQL, as well as how to write code and queries in Python. Students will also learn how to read and interpret data and how to use data visualization tools to create engaging and persuasive charts and graphs that will make their findings more apparent and convincing. As a career-focused program, this class aims to ensure that students leave the course confident in their skills and ready to enter the job market. To support students in these goals, Fullstack Academy offers a range of career services, including networking opportunities and access to a shared platform to meet alumni and search for jobs.

Students interested in an introductory course that still covers practical data analytics training may want to consider this foundation course. Here, students with intermediate Excel training will be able to learn how data analytics processes function, what role big data plays in a range of industries, and all of the various tools that professionals utilize to utilize this influx of data. Students will learn how inferential and descriptive analysis practices are utilized and how these techniques allow specialists to make predictions and forecasts based on their reading of the data. By the end of this course, students will be prepared for more advanced and practical data analytics training, and they will have an understanding of how their training can be leveraged in their personal and professional lives.

Business intelligence software, such as Microsoft's PowerBI, is designed to help businesses make better use of their data and become more adept at the process of reading and interpreting it. In this professional skills bootcamp, you’ll learn how to use all of the features of Power BI and how it can be used as an interactive data visualization tool to help you look over your data in new ways and better communicate your findings to invested stakeholders. In this course, you’ll learn how to turn data into interactive visual dashboards, how to use these visualizations to connect multiple databases and gain relational insight, and how to publish your findings so that others can look more closely at your data. If you are working with big data, it will certainly pay to learn how to use data visualization tools like Power BI.

While it isn’t the flashiest program (in fact, it might be the least flashy desktop application still in widespread use), Excel is a vital tool for most data analytics projects. Aside from Excel spreadsheets being one of the most common pieces of database infrastructure, Excel has a number of tools specifically aimed at making the process of data analysis seamless. This class will teach students how to utilize Excel as a complementary tool alongside other data analytics applications, including the use of Pivot Tables and VLOOKUP functions to better organize, interpret, and correlate data. Students will also learn how basic functions and formulas can be used to streamline the analytics process and how Excel can help improve workflow.

With so much data being collected and processed every day, it is rapidly becoming impossible for humans to handle and interpret all of it. Machine learning is a subfield of data science that aims to address this issue by designing complex artificial intelligence algorithms that can interpret and learn for these datasets without needing a human operator to parse the individual bits of information. In this machine learning bootcamp, you’ll get practical training in how these algorithms work, how they are written and trained, and how they can be used for practical professional purposes. Students will need a baseline knowledge of Python, and they will need to be comfortable working with data libraries, but students who enroll in this advanced course will leave with practical professional machine learning skills they can apply to almost any task.

Artificial intelligence is poised to revolutionize the daily workflow of a range of different fields, so it makes sense that one of the major applications of AI would be in data analytics. Since artificial intelligence is itself a tool meant to automate the process of collecting data and ‘learning’ from that data, it makes sense that the tool would be particularly useful for professions that themselves deal with huge amounts of data. This is particularly the case for fields like finance and retail, where data is being collected so quickly that a human being simply won’t be able to keep up with the inputs. In this class, you’ll learn the best way to leverage machine learning applications and large language models in your data analytic tasks, and you’ll learn how to prompt these applications to provide you with the results that you are looking for.

High school students interested in learning the basics of Python in an effort to prepare themselves for work in any number of different fields or college majors should consider enrolling in this online summer data science program for teens offered by NextGen Bootcamp. Students will learn how to write data science-related applications in Python, including those involved in maintaining and querying databases, and they will learn practical data analytics skills that can be applied to a range of different fields and industries (so this course is good for aspiring scientists, sports managers, entrepreneurs, and computer scientists, among a host of others). Then, students will get a chance to work with cutting-edge machine learning algorithms to prepare themselves for the next wave of big technological changes that are poised to reshape the workforce they’ll be entering.

Data analytics is a vital skill to learn for anyone looking to wring every ounce of value you can out of the tools and resources available to you. This is true regardless of whether you are a data analytics professional or someone looking to get a small business or project off the ground. Data has become so simple to collect and query that virtually everyone who is looking for additional help in accomplishing their goals would benefit from learning how to track and analyze this data. Learning data analytics can assist in everything from analyzing sales figures to tracking the web activity of clients and customers to projecting long-term earning potential and calculating ROI to tracking viewership numbers on branded live stream content. No matter what kind of task you are undertaking, it pays to have an understanding of how data can be used to offer a more granular understanding of the state of your project and the information you need to improve your work.

It would likely be faster to list all of the industries that don’t utilize data analytics than those that do. Everything that a company or institution can track is tracked and analyzed, down to things you probably didn’t even think could be tracked. For example, Disney theme parks track the exact amount of time that visitors are in line at different attractions so that they can make minute-by-minute changes to park operations to hit the perfect equilibrium of wait time. This is made possible by skilled data analytics experts who have studied not only the data processing techniques but also understand the structures of their specific industry. 

Commerce and retail

Retail sales is probably one of the industries where the use of data is the most obvious and understood, particularly the field of digital marketing. Data analytics is used to track consumer behavior, including what websites they visit and what kind of search queries are bringing them to a website. Tracking consumer behavior is vital to predicting future trends, and getting ahead of the curve is an essential part of succeeding in retail and a vital part of maximizing your ROIs on any given promotion or business decision. In retail, data analysts will need to understand how to correlate different data points, particularly as they pertain to customer retention, and they will need to learn how to find data that translates to sales in order to produce a working plan based on the data they have collected.

Finance and investing

Data analytics has revolutionized the field of finance and investing by giving professionals and retail traders alike access to tools that allow them to access data (and cross-reference that data) at a moment's notice. Unlike the past, where most people would get stock information from a daily newspaper, you can now see stock prices updated by the second. You can buy and sell assets on your phone, and you can collect data on those assets in ways that would have been impossible even a decade ago. This has made the finance industry far more data-focused than it has ever been, and financial analysts are tasked with ensuring that companies and clients make sound investments based on data collected from millions of transactions a day. 

Politics and public advocacy

Increasingly, politics and public advocacy are less rhetorical as they are data-driven. Between issue and candidate polling, demographic information, and various other data collection processes, the art of winning an election or advancing a public policy is more about leveraging data than ever before, and virtually every campaign or non-profit will hire several experts in data collection and analysis in order to eke out every tiny advantage that they can. This has become so targeted that larger campaigns will use data to determine which streets in a ward they should be more aggressively targeting, and data is at the heart of most contemporary get-out-the-vote efforts. If you are interested in working in politics or activism, it pays to understand data.

Sports

Over the last two decades, data analysis has become central to the professional sports industry, with teams employing thousands of data analytics professionals to run simulations, determine successful strategies, and find undervalued players that they can leverage. With the rise of SABERmetrics and Amazon’s partnership with the NFL, advanced statistics have become a part of the general culture of a number of sports leagues and their viewership, meaning that if you are interested in blending an interest in sports and math, you are likely to find a place in both worlds by learning data analytics.

Most data analytics is, unsurprisingly, undertaken by Data Analysts. They are hired by companies, firms, institutions, and basically any organization that wants to take advantage of the immense amount of data they are collecting in order to gain a competitive advantage in the field and become more adept at completing their primary functions through the use of this data. Most data analysts will need to understand the inner workings of their field in addition to the data science and analytic techniques used to parse data since if you don’t understand what you are looking at, you can’t make reasonable inferences from the data. In addition, data analysts will need to know how to communicate their data to invested stakeholders in order to provide compelling and insightful analyses of the data. The pay of a data analytics expert is going to vary somewhat wildly based on the field in which they are employed and the institution that employs them, but in the US, data experts make about $100,000 annually.

Specialized data analysts work in a range of different fields as well. For example, data analysts who specialize in finance and investing can find work as financial analysts or investment analysts. These professionals offer advice and recommendations on finance and investing strategies to business and individual clients based on their assessments and understandings of asset markets derived from data-based analyses. This requires an understanding of investment strategies and market behaviors as well as knowledge of how to work with FinTech applications and software. In the US, financial analysts earn about $100,000 a year.

Related to data analytics, data science is the field focused on building and developing the tools and architecture that allow Data Analysts to perform their jobs. Data Scientists tend to be less focused on working in specific fields since rather than working with raw data and attempting to interpret that data, they are working with big data as a concept in and of itself (so they are less interested in, say, what today’s stock prices are and more interested in what is the most effective way to analyze stock prices). This makes their work more technical in nature since they aren’t going to be analyzing the data they work with, and skills like computer programming and advanced Python language skills will be more important to their daily work. In the US, data scientists can expect to earn about $110,000 a year.

As mentioned earlier, machine learning and artificial intelligence have become the new major players in data-related fields, given that they can do things that would be impossible for human beings, given the speed at which they read and interpret data. However, these machines are still built by humans and need to be taught and refined by them to avoid bad processing logic, bias, or recursive thought. These machine learning engineers are responsible for making sure that the artificial intelligence algorithm produces useful responses to queries and that they work to develop the next generation of artificial intelligence applications. The field is still in flux relative to other data-related fields that have established themselves within various industries, but the future's looking bright for machine learning engineers, who earn an average annual salary of $140,000.

Learning data analytics can open the door to a lot of new and interesting career opportunities, or it can help you achieve your goals in other ways, such as by giving you more tools to market yourself, your brand, or a new business. If you are hoping to take a more analytical and logic-driven approach to your career or your future investment opportunities, learning data analytics may be right for you. These classes will prepare you for a high-paying career in an in-demand field that crosses over to a wide range of different professions and industries. By learning the art of data analytics, you’ll be setting yourself up for future success and building your long-term earning potential.

The main reasons to opt for a data analytics class, as opposed to trying to self-teach, is that the guidance provided and the expert instruction you receive will help you avoid common pitfalls and give you the assurance that you are learning how to do things the right way. While you can reasonably self-teach the analytic part of the equation (learning statistics and quantitative and qualitative reasoning in a specific field isn’t impossible to self-teach, especially if there are a lot of resources available to help), the technical side of data analytics training is much more complex and will require you to learn a lot of computer science skills (and programming languages) in order to succeed. This training will ensure that students feel comfortable with the skills that they need to develop in order to find work in the field.

In addition, if you enroll in a bootcamp or other more advanced course, you are likely to receive guided career support services that will help you find a job upon graduating. These support services will vary from program to program, but you can expect to receive one-on-one career mentoring, networking opportunities, and support in the form of lessons and seminars aimed at ensuring that students understand the hidden aspects of the job market they will need to manage. Some programs offer job guarantees, and a few work with companies and sponsors to provide micro-internships to graduates in order to help them build their skills.

The other main alternative to a data analytics class is to enroll in a degree program in a field related to statistics (like computer programming or just statistics). These programs are going to be the most immersive learning option available to you, but they are also going to be the most expensive and time-consuming (even continuing education programs can last multiple semesters, and degree programs cost tens of thousands of dollars). While you will learn a lot, this simply isn’t practical for many students, and these programs will be far less career-focused (inherently) than their bootcamp counterparts.

Is data analytics training worth the investment?

Data analytics training is worth the investment if you are looking for a new career in an in-demand field or you are planning to oversee the data analysis aspects of a project that you are involved in long-term. The training you receive will open the door up to many future opportunities, and the skills are largely transferable to other fields and projects, presuming that you are able to spend time studying what kinds of data you’ll be analyzing (understanding the stock market doesn’t inherently lend itself to understanding SABERmetrics, but once you know how to use Python and Tableau, you can at least transfer those skills between both tasks).

However, data analytics training does require learning a lot of different skills and applying them in different contexts, so it isn’t something that you can spend a few days learning and consider yourself done. While taking introductory courses is a great way to learn the basics, discover if data analytics is right for you, and set yourself up for future training, it probably isn’t the best idea to jump head-first into a professional skills course if you aren’t fairly confident that you are looking for a career that involves data analytics training. 

Common payment options

There are a lot of different options available for students looking to find ways to pay for their data analytics training, meaning that you can be sure that you will get the training you are looking for without too much of a financial burden. While you can opt to pay for the course in full, up-front (which is probably for the best if you are taking an introductory course since those often don’t cost more than a few hundred dollars), you may want to look for other options for paying for a career-focused bootcamp or certificate program, since those can cost a few thousand dollars.

The most common way to pay for these programs is through installment programs, where you break the payment up into multiple different monthly, quarterly, or yearly chunks, and financing plans, which is akin to putting the class on credit and paying for it over time (usually well after the class is completed) with a bit of compounding interest added on top. Both of these options let you spread the cost of the course out over a longer period of time. These options will vary from provider to provider, but they are good to look into when deciding how you plan to pay for a longer course.

There are also scholarship options available from various training providers, nonprofits, and government organizations aiming to help members of specific communities have an easier time finding the training they need to expand their skills. One of the most significant of these programs is the GI Bill, which offers a range of different benefits and support options for veterans and their families looking to gain professional civilian skills through courses like in-person tech boot camps. You’ll need to look for whether or not you qualify and whether the program in question is approved by the VA, but these resources can be immensely helpful for anyone who qualifies.

When it comes to picking the best training method for you, there are several things you’ll want to consider, including how quickly you need to learn the skills, how much money you can spend, whether you want to learn in-person or online, and whether you want to learn all of the major skills at once or spread your training out over a longer period of time.

The first question that students will want to answer is whether they are looking to enroll in a career-focused course (and therefore, commit the time and energy to learn long-term career skills) or if they are looking to learn specific skills (like SQL or Excel) in a more accelerated course. Career-focused courses will have the most long-term benefit for aspiring data analytics professionals, while shorter classes are better for students who are looking to learn specific skills in a shorter time frame.

The next question you’ll want to consider is whether or not you want to learn in-person or online since both options offer advantages and disadvantages for students looking to learn data analytics. In-person training offers students the chance to work face-to-face with their instructor and learn the skills they want to learn in a controlled environment that is designed to help them succeed. Many students report that they prefer in-person learning because they can talk directly to their instructors and work alongside their cohort. These courses also give students the ability to get live, instant feedback on their work in a way that can make it easier for them to overcome significant hurdles in the learning process. The drawback to these classes is that, because they are held at specific training centers, there are logistical concerns that students will need to address. This not only means finding a class that they can actually attend (since an in-person class held in Chicago is of little use to someone who lives in Louisville) but also accounting for the extra time, effort, and cost of commuting to and from a class.

By contrast, online learning gives students the option to learn from their own homes, meaning those aspiring data analysts in Louisville can learn the skills they want to learn from a training provider in Chicago. They will also learn how to do data analytics work from their home office, which can be particularly useful for anyone looking to find remote work opportunities. With the advances in remote pedagogy over the last few years, online classes are no longer playing second fiddle to in-person classes. The drawback to these courses is that students will need to be more self-motivated to not fall behind in their work, as the elimination of things like the commute and the time spent in the class can make the course feel less real and therefore, it can be easier for students to fall behind on their work. In addition, students will need to be more on top of things like troubleshooting tech issues and acquiring licenses for software (and ensuring that their personal set-up can run a complex set of data analytics applications while also running a digital classroom.

How long is a data analytics class?

The length of any given data analytics class will depend on what skills are being taught and how career-focused the class is. Classes like the Noble Desktop Data Analytics Certificate program or the Data Analytics Bootcamp will run for several weeks (or months in the case of part-time classes), but they will cover all of the subjects that you need to know in order to become a professional data analyst. By contrast, shorter courses, like an introduction to SQL class (that is a part of the larger certificate program), may only take a few days of study, but students will need more training in order to become professionals. Thus, you can spread the courses out over a longer period of time, depending on the nature of your schedule and the timeframe you want to spend learning data analytics skills.

There are also a range of different on-demand classes that let you learn data analytics at your own pace. These classes, which are most often recorded lectures and video explanations of various skills, techniques, and tools, are designed to let students learn data analytics skills as quickly or as slowly as they need to learn them. This gives students significantly more control over the length of their data analytics training, but these courses come with drawbacks. Most notably, without access to a live instructor, students will need to find alternative options for dealing with roadblocks or concepts that don’t make sense. In addition, these classes aren’t always kept up-to-date with the newest versions of applications or the latest trends in data analytics (for example, will the video lessons account for the rise of AI tools). These classes can be a useful supplement to live courses, but unless attending a live class is completely out of the cards, you’ll want to use these as added training rather than the primary mode of learning data analytics.

Over the course of a data analytics training course, you can expect to learn a lot of different skills that are important to your success as a professional analyst. While career-focused classes can expect to cover most of these skills together, it is also useful to know what kind of skills you’ll need to learn if you are working on learning data analytics at your own pace.

Excel

Learning the basics of Excel is one of the most fundamental skills for aspiring Data Analysts since database management is so dependent on Excel. Learning how to use advanced Excel features can help you streamline the data management process, and it can help you supplement training in other areas since you’ll be able to work on simple queries without relying on SQL or basic data visualization tasks without relying on Tableau. Excel training isn’t enough to completely replace the other tools you’ll learn in data analytics training, but it will certainly supplement them.

SQL

Structured Querying Language is a database-centered programming language that provides users with the tools they need to write complex, multi-stage queries to look up and correlate massive amounts of data, letting you get a much more granular picture of the datasets you are analyzing. SQL can also be used to write conditional queries and subqueries to make the data analysis process even more precise. If you plan to work with large datasets, learning SQL will significantly ease the process of querying your data.

Python

Python, one of the most popular programming languages in the world, is also one of the most important tools for advanced data analysts, and anyone who wants to work in the field professionally is likely to want to learn at least the basics of Python. Python lets you write programs that query databases and automate data organization and collection, as well as machine learning algorithms that allow data to be read and interpreted without the aid of a human operator. This language also allows you to build complex applications intended to make the data analysis process easier.

Data Visualization

For the most part, data isn’t particularly useful if you can’t communicate your findings to invested stakeholders, particularly when you are dealing with large amounts of financial, commercial, or demographic data. Data visualization training will help you transform raw data into easily understandable graphs, maps, and charts that allow non-experts to better understand the data and are rhetorically persuasive enough to convince them to follow your preferred course of action. With the help of tools like Tableau, you can even create interactive dashboards that let you or other users update and explore the data in real-time with live updating charts and graphs that will make complicated analysis tasks even easier.

Quantitative and Qualitative Analysis

It is all well and good to know how to query data and build databases, but if you don’t understand how to actually analyze the data, then all the Python training in the world won’t help you. You’ll need to learn how to make qualitative and quantitative assessments of your data, and you’ll need to learn how to translate these assessments into actionable insights that can be communicated to other stakeholders. This training will involve learning skills like logical reasoning, statistical analysis, and creative interpretation, as well as learning when a given mode of analysis is more valuable than another.