Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Become proficient in Microsoft Excel and go from beginner to pro with our hands-on Excel Bootcamp. Learn functions, formulas, Pivot Tables, macros, and time-saving techniques to conquer all the key skills needed to excel in Excel.
Learn how to use Tableau, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. This course will teach you how to connect to datasets, analyze and structure data, customize visualizations, and publish your work.
Learn how to use Power BI, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. Learn how to connect to datasets, analyze and structure data, customize visualizations, and publish reports.
Learn the fundamentals of data analytics, including descriptive and inferential statistics, and how different industries and organizations use data for forecasting and decision-making. Explore statistical analysis and modeling, including statistical algorithms, theorems, and models.
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Automate and enhance the data analysis process with artificial intelligence (AI). In this course, you'll learn how to utilize AI tools to collect, preprocess, analyze, visualize, and interpret data without the need for extensive coding knowledge.
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn Tableau for data visualization and become a certified Tableau Desktop Specialist in this hands-on certification course.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Learn Power BI for data analysis & visualization and become a certified Power BI Data Analyst Associate in this hands-on certification course.
Boost your resume with a Microsoft Excel Certification. Our program offers three courses, private training, the Specialist Exam fee with free retake, lifetime access to Excel video courses, and free exam proctoring.
Data analytics involves examining raw information to uncover patterns, trends, and insights that help answer key questions. This process applies across virtually every industry and draws from large, diverse sets of data—whether structured, unstructured, or semi-structured—that are constantly being produced at incredible speed and volume.
The field often deals with big data, which is typically measured in terabytes or even petabytes—one petabyte equals about one million gigabytes, or roughly 250,000 full-length movies. The datasets used in modern data analytics can span from hundreds to millions of petabytes, highlighting just how much information analysts work with.
A skilled data analytics professional uses these massive data sets to interpret historical trends, forecast future outcomes, and support informed business decisions. Advances in machine learning, affordable computing power, and cloud technology have made it easier than ever to process and analyze complex information, allowing organizations in every sector to extract valuable insights and turn data into strategy.
Talk to any Data Scientist or Analyst, and they’ll likely tell you that data analytics isn’t just a career—it’s a passion and a way of thinking that can be applied to almost any aspect of life. For example, if you notice recurring problems in your neighborhood, you could analyze urban data—such as traffic patterns, pavement conditions, weather, and accident reports—to predict and even prevent infrastructure issues like potholes. Whether you’re serving on your local HOA board or presenting insights to City Council, data-driven findings can help shape smarter, more effective community decisions.
Data analytics can also bring a fun, competitive edge to personal interests. Sports enthusiasts, for instance, might use predictive analysis to assess player performance and team dynamics, making more strategic choices in fantasy leagues. At its core, data analytics blends pattern recognition, mathematics, and statistics—skills that can reveal insights hidden in everything from city infrastructure to your favorite weekend hobby. Once you start seeing the world through data, you’ll find that analytics is everywhere.
Data Analytics courses can teach you about the data analytics process (including collecting, wrangling, mining, and visualizing data), different data roles, and data structures. These courses will cover a vast array of information, but the topics listed below are some of the most important ones you’ll study to become an expert in data analytics.
As the phrase suggests, Data Visualization is the graphical representation of information and data. The process of translating complex, high-volume, or numerical data into a visual representation, data visualization makes data easier to process. Raw data can be hard to understand and use, so Data Scientists have to prepare and present data in the right context. Giving it a visual form helps decision-makers identify the relationships between data and detect hidden patterns or trends. Data Visualization creates stories that advance business intelligence and support data-driven decision-making and strategic planning.
There are many programming languages that you will need to understand to be a Data Analyst. Python is considered a must-have since it offers a remarkable number of specialized libraries, many of which pertain specifically to artificial intelligence (AI). You will also need to master R to work with data analytics. R’s syntax and structure were created to support analytical work, and the programming language also appeals to businesses because it can handle complex or large quantities of data.
Classes may also cover additional languages like SQL and NoSQL. In modern analytics, SQL persists as the standard means for querying and handling data in relational databases. As the name suggests, NoSQL systems are the opposite of SQL and don’t organize their datasets along SQL’s relational lines. NoSQL frameworks can effectively structure their information in any way, provided the method isn’t relational.
Excel is more than just your everyday budgeting and organizing tool. It’s equipped with robust tools that can perform complex data analysis, especially when you include some add-ins, making it suitable for both novice and experienced professionals. Most classes will go over the basics of how to use Excel for data analytics purposes.
Analytics without machine learning depends on human Analysts to create the analyses, find insights, and make recommendations based on their interpretations. The results depend greatly on the Analyst’s professional experiences and opinions. Using machine learning with analytics improves the analytics process in two main ways: it augments traditional analytics by identifying patterns, trends, and correlations that wouldn’t be noticed by traditional analytics, and it helps decision-makers be more efficient with their analytics processes by automatically evaluating datasets and making visual recommendations. Training courses will typically cover at least an introduction to leveraging machine learning for data analytics.
An equally important skill, critical thinking is the ability to evaluate information, understand it, and make informed decisions based on facts. Critical thinking in data science is essential because without it, a Data Scientist might quickly draw incorrect conclusions from the data or fail to notice essential patterns or trends. Data analytics classes will help you sharpen your critical thinking so that you can be a precise and strategic Data Analyst.
Given the field’s complexity and the diverse skill set required to excel, data analytics is considered somewhat challenging to learn, blending elements of statistics, mathematics, computer science, and specific industry knowledge. In data analytics, both the initial learning curve and the requirement for continuous learning are substantial, largely because of the breadth of the field and the continual advancement of technology. While data analytics comes with its challenges, it is ultimately a field that anyone can master. With the right amount of passion, dedication, and time, anyone can become an expert in data analytics.
One of the most challenging parts of data analytics is understanding the data that’s been accessed. Then, once the data has been gathered, it is often messy and must be cleaned. Pre-processing data can be time-consuming for Data Analysts since it involves tedious tasks like encoding variables and deleting outliers. This portion of the analytics process is often considered the worst part of data analytics, but it’s essential because it ensures that the models used are constructed based on high-quality, clean data.
Data analytics is a broad field; the time it takes to learn depends on several factors. You can master the basics of data analytics in less than a year, but your level of expertise will include various skills and practice in a particular role. You shouldn’t expect to become an advanced data pro in a matter of months.
The time required also depends on your existing knowledge and how much time you can dedicate each week. With full-time dedication (30-40 hours per week), you can become proficient in around three months, while part-time learners (15-20 hours per week) might take six months or more.
One factor to consider is the type of training you choose. Self-guided data analytics education can take much longer than a targeted bootcamp or certificate, so choosing the right course that fits your schedule is paramount to your success and efficiency.
There are many factors to consider when answering this question. Firstly, you’ll need to decide what learning environment is best suited for you. Do you excel in a collaborative environment that allows you to interact with your peers and receive instant feedback from your Instructor? Or, do you prefer a more solitary learning environment, perhaps one where you can learn at your own speed?
You’ll also need to decide how much time you can commit to your education and what level of flexibility your schedule requires. Are you looking to dive deeply into an intensive program? Do you work full-time and require your classes to be scheduled for evening and weekend hours?
In-person classes offer face-to-face interaction with both your fellow students and your expert Instructor. This collaborative environment is available for both full-time and part-time classes and provides structure and accountability, two things that can help you succeed. Online courses may lack this in-person interaction, but they make up for it with increased flexibility. Live virtual courses still offer instant feedback from your Instructor and interactions with other students, while on-demand virtual training allows for the most flexibility since you can decide where and when you learn.
The good news is that, no matter which version you attend, you can obtain a quality Data Analytics education with a top-notch curriculum and expert Instructors.
There are several other skills that you can start working on while studying data analytics. While you’ll most likely have the experience needed in mathematics and statistics, it can’t hurt to brush up on those two subjects. Additionally, programming languages like Python, R, Java, C, or Perl will be very useful.
Hadoop is the most popular Big Data framework. Most professional arenas will expect (or hope) that you have experience in Hadoop, so it can’t hurt to tackle it alongside data analytics. Likewise, SQL is the most common way of getting information from a database and updating it, so if you can master SQL as well, you’ll be a step ahead of your career competition.
Known for some of the best Tex-Mex cuisine in the U.S. and cowboy-centric culture, Dallas is also a modern metropolis with a variety of industries. The Dallas-Fort Worth Metroplex is the most populous metropolitan statistical area in the state of Texas, with aerospace and engineering, healthcare and life sciences, and energy and natural resources topping their industry charts.
The longstanding home of well-known corporations like AT\&T, ExxonMobil, and Southwest Airlines, the tech scene in this metro area has been rapidly emerging as one of the best places for IT jobs for entrepreneurs and enterprises alike. Because of this, it’s no surprise that IT job growth in the Dallas metro area is expected to grow by 10.5% by 2027 to nearly 203,000 jobs.
The finance and insurance industry contributes over $26 billion annually to the economy, and the industry employs over 87,000 people in Dallas alone. With companies like American Airlines Credit Union and Comerica Bank (among others) calling Dallas home, this sector is one of the best in the city. The day-to-day operations in finance and insurance involve managing and investing money, as well as providing financial protection for individuals and businesses.
Manufacturing is also alive and well, contributing over $10 billion annually to the economy and providing jobs for over 40,000 people. Big hitters like the General Motors Assembly Plant in Arlington, Lockheed Martin in Fort Worth, and Texas Instruments in Dallas all help contribute to the manufacturing industry, accounting for 7.1% of the regional economy by employment. Goods produced in DFW range from boots and clothing to bricks, steel, plastics, SUVs, and aerospace components.
Always a worthwhile sector for the financially savvy, the real estate market in Dallas contributes over $8 billion annually to the economy and employs over 35,000 people. Rental rates in Dallas increased 9.4% year over year, and industrial rentals in Dallas experienced a whopping 20% increase.
There are several exciting career options available for both those who are just getting started in the field of data analytics and those looking for advancement. Each position will have a unique specialty or focus, so you’ll want to have a clear understanding of how you’d like to apply your data skills professionally.
Risk Analysts help organizations identify, assess, and prioritize potential risks that could impact their business operations. They study organizations’ investment portfolios to gain insights into the level of risk associated with various decisions and are a vital part of a business’s strategy and decision-making. You can expect to make around $80,000 in Dallas as a Risk Analyst.
Financial Analysts guide individual stakeholders, businesses, and companies on how best to invest money and resources to gain the maximum profit. They will analyze multiple factors like business environments, market trends, financial status of companies, expected outcomes of operations, and past financial data to make the most informed and calculated decision. According to the U.S. Bureau of Labor Statistics, Financial Analysts make a median salary of $99,890 per year, with Dallas sitting slightly below this number at $81,000.
Database Developers study database processes with the goal of updating them, improving their efficiency, and getting rid of inefficient coding. They keep track of how existing databases are performing and use code and web architecture to create data systems, analyze and maintain existing databases, and implement new user features. You can expect to make around $117,000 as a Database Developer, Administrator, or Architect.
Do you also have an interest in weather systems? Weather Analysts study atmospheric occurrences and analyze data pertaining to meteorological events to provide forecasts and reports. It’s important they have a sound understanding of math and statistics, and are also familiar with meteorology. Meteorologists and Atmospheric Scientists make around $92,000 per year, so you can expect to make near that as a Weather Analyst.
Offered by Noble Desktop, this in-depth course can take anywhere from four to 20 weeks to complete. You’ll learn all the skills needed to become a professional Data Scientist, like how to manipulate databases and perform data analysis, master the fundamentals of Python programming, and use its main Data Science libraries to analyze data. You’ll also try your hand at creating machine learning models, making dashboards and visualizations, and deploying your projects online with GitHub.
Held live online, this course includes access to 1:1 mentoring, a free retake within one year, and a verified digital certificate upon completion. Small class sizes and live project-based training with industry experts help ensure you gain all the knowledge and experience needed to land your dream Data Scientist job.
This project-based course from Noble Desktop teaches you to gather, wrangle, analyze, and visualize data using various tools and technologies to help businesses in decision-making and strategy. You’ll work on multiple real-world projects to explore both predictive and prescriptive analytics and gain practical experience. Plus, you have access to 1:1 mentoring, a free retake, and setup assistance when you enroll in this course. Payment plans are available to make this certification more accessible.
If you’re looking to offer in-person or online training to your office or team, Noble Desktop can help. It offers a variety of corporate training programs, including in social media marketing. You can order vouchers in bulk at a discount, choose which courses are available to employees, and even let your employees choose their own schedule. Plus, you can choose to have your training hosted at a location of your choosing, live online, or at Noble Desktop’s Manhattan office.
With a top-notch customizable curriculum and Instructors who are experts in their industries, Noble Desktop’s corporate training is designed to cater to your business or organization’s needs. For more information or to schedule a free consultation, you can reach out to Noble Desktop at corporate@nobledesktop.com.