Mastering Data Science – From Foundations to Advanced Analytics

Learn to extract valuable information for business decision-making and strategic planning. Master the fundamentals of databases and SQL, data mining, and artificial intelligence algorithms.

Visualize data in ways that tell compelling stories. Communicate your findings to stakeholders across the business.

Like a jumbled puzzle, data sets are made up of many individual pieces. Learn to manipulate these elements, like subsetting or aggregating, for easier analysis.


Data science has a wide range of applications, and it’s becoming increasingly important for businesses in every industry. However, the field can seem intimidating to beginners. Fortunately, even experts were once beginners, and mastering data science is a process that can be achieved with consistent learning and curiosity. This guide will walk you through the fundamental concepts and tools of this booming discipline.

You’ll learn to balance the theory and practice of applied mathematics and computer science to analyze and handle large-scale data sets. You’ll also learn how to prepare data for predictive analysis, which includes identifying patterns, validating data sources, and removing biases. Finally, you’ll learn how to communicate data-driven insights so that they can be understood and used by non-data scientists.

As part of the core curriculum, you’ll be mentored by expert data science professionals from leading organizations. These mentors will share their industry experience with you, helping you to understand how to apply your new knowledge at an organizational level and deliver the value that employers demand from this field.

In addition, our graduate program offers an optional 6-credit capstone project where you’ll be asked to apply your data science skills to a real-world problem that you or your organization is working on. You’ll work with a team of your peers to develop a solution that will make an impact on the business world.

If you’re interested in advancing your career with an MS in Data Science, you can apply now. There are no pre-requisites, and our unique “earn your way in” admissions process allows you to enroll regardless of your current level of professional experience. However, you should be familiar with programming concepts like data structures and algorithms in order to successfully complete the course of study. Additionally, you should be comfortable with math, including calculus and matrices. You should also stay up-to-date with the latest trends in data science by following industry blogs, attending conferences, and taking online courses.

Data Manipulation

Data manipulation is the process of altering structured data that’s read by computer systems to make it easier to understand. This process can be used by businesses in every industry to help them gain valuable insights. It may also include removing unhelpful data findings or cleansing erroneous information from the collected data to provide more accurate results.

To manipulate data, you’ll need to be familiar with the 4 main operations: create, read, update and delete. This can be done through a variety of programs, including spreadsheets and databases. There are also software programs that specialize in performing data manipulation, such as Power BI and SolveXia.

Manipulating data is important because it allows you to adapt the data in the way that it’s needed to display information or feed and train an analytics model. However, it’s important to remember that manipulating data doesn’t necessarily mean changing the data itself. For example, if you sort or arrange a dataset in alphabetical order without deleting any entries, that’s not considered data manipulation.

Some examples of data manipulation tools include Excel, SQL and Python. They all have different functionalities but they can all be used to perform the same task, which is organizing and reorganizing structured data so that it’s easier for humans to understand.

The last technical stage of data science involves studying the full range of data findings to identify helpful information. These can be things like consumer purchase patterns, profit insights or digital brand interaction. Businesses then use this information to inform their decisions, strategies and tactics.

As a data scientist, you’ll need to be familiar with these tools and the process of preparing data for analysis. For example, if you want to build a predictive model on a dataset, you’ll need to have it in the correct format and ensure that there are no errors or missing information. Data manipulation is also important for preparing data for visualization, which helps non-technical business leaders and busy executives easily understand otherwise complex information about their company’s performance. It’s an essential skill for every data scientist to have, and it can be executed in a variety of ways from simple spreadsheet programs like Microsoft Excel to more sophisticated database software platforms like SQL and Python.


Data science is an exciting, challenging and highly sought-after career field. The Bureau of Labor Statistics projects that the number of jobs in this field will grow by 30% between 2019 and 2029. The data science learning path is broad and encompasses a wide range of math, programming, computing and business analytics skills. It also includes state-of-the-art machine learning technologies.

The first step in the process of data science is identifying real-world business problems that data can help address. For example, a company might use data to predict customer churn or product demand. Once a problem is identified, the next step is collecting and preparing data. This can include data sourcing, cleaning and ensuring that the information meets standards for accuracy.

A key component of this step is developing a model that can produce insights and predictions, such as using regression analysis, clustering or classification. This is known as predictive analytics and allows businesses to understand what will happen in the future. Once the insights are created, they can be used to make decisions or take action. For example, a logistics company might use predictive analytics to optimize routes and reduce delivery times or a retail store may use it to predict inventory needs.

Another critical aspect of this stage is communicating the results to a variety of stakeholders. This involves creating reports and data visualizations that can be understood by non-technical people. This can be done through a data science programming language like R or Python, or dedicated visualization tools.

Lastly, the ability to interpret and explain complex data sets is also important. This can be done through visualizations, storytelling and other methods. This can be very helpful for business leaders who want to understand the implications of their data and how they can apply it to their business.

Advanced Analytics

Data science is all about using the tools and techniques at your disposal to uncover actionable insights, improve processes, make informed business decisions and drive organizational success. This is a field where the possibilities are truly endless, and it can take time to understand the fundamental concepts, develop the necessary skills and achieve proficiency.

This is where it pays to be open to learning and to be persistent with your career goals. Whether you choose to enroll in a master’s degree program or opt for self-study, focusing on the end goal and not putting too much pressure on yourself can help you stay motivated as you build your skillset.

To advance in this field, it’s important to stay abreast of the latest advancements in machine learning and data science, as well as keep up with current news and trends. One way to do this is by reading blogs and articles that cover topics related to the data science industry and technology. This can help you fill in gaps in your knowledge, as well as spark ideas for future projects that can impact the world around you.

Aside from staying updated on the latest developments in this field, it’s also a good idea to explore various types of data sets and applications. By doing so, you can learn new skills, gain a better understanding of how the different data sets work together, and build your problem-solving abilities. You can do this by experimenting with open source software, such as TIBCO Spotfire Analytics, which is designed to handle a wide variety of data-driven tasks.

For example, it’s possible to use Spotfire’s powerful data preparation and analytical modeling features to create a personalized product recommendation system that analyzes customer purchase history and recommends products or services. This type of system is a great example of how advanced analytics can be used to increase sales and customer satisfaction.

The best way to hone your data science skills is to dive into hands-on projects and real-world applications. By doing so, you’ll be able to bridge the gap between theory and practice and demonstrate your skillset in an interview. In addition, completing projects is a great way to add to your professional portfolio and build your resume. You can find numerous project-based opportunities through online resources, such as GitHub, Kaggle competitions and industry-specific challenges.