By Dr. Frank Appiah
Faculty Member, School of STEM
What is Data Science in Today's World?
Data science involves deducing insights from raw data to answer a business question, explore new ideas, or test hypotheses. A data scientist will use data science to acquire information buried in large amounts of data, so that an organization can make decisions based on that information.
With the ongoing usage of new data science techniques along with the many existing data science applications - including data analytics and data science algorithms - the world of data science is actually still in its infancy.
Big Data is just one of many applications of data science - but it's definitely front and center. In our lives, there is a constantly growing amount of data, ranging from real-time streaming data to the data stored in huge databases. Data sources are seemingly infinite.
During a data analysis, it’s important to have quality data for use in data science applications. Quality data – especially big data analytics – ensures that the insights derived from current and past data points are as accurate and up to date as possible.
Data Science: An End-to-End Process
Most datasets are messy and to get them to a high-quality standard, it’s necessary to do some data processing. In addition to big, messy data needing to be cleaned up before its use in models, data science also has a lifecycle of its own, known as the end-to-end process. For instance, a typical lifecycle involves:
- The start: Defining the problem to be solved through data analysis
- The middle: Acquiring new data or pulling existing data to perform a data analysis
- The end: Collecting insights from data analyses and channeling that information to decision makers to help them make informed, data-driven decisions
Discussing Data With Subject Matter Experts
These end-to-end data science processes are inextricably linked. For instance, a data scientist must first understand the problem to be solved. To do so, they must ask clarifying questions about the raw data and talk with subject matter experts (SME) on the team they’re working with. The data scientists will then map what they heard and learned from the SMEs during their initial probing of the relevant data and points.
Data Science Applications using Data Analysis
After a data science problem is defined, the data scientist is now in the second stage of the lifecycle. At this stage, that data scientist will perform exploratory data analyses (EDA) which involves - but is not limited to - cleaning the data, which means making the data readable and easier to analyze.
This second stage involves transformation of the data – for example, changing gender and coding data as male (0) or female (1). The latter can easily be consumed by a machine without it having to do the transformation on the back end.
A data scientist also uses data visualization, such as using graphs to display information that will otherwise be difficult to decipher.
For instance, a histogram showing the distribution of a feature or a variable (such as age) makes it easier for the reader to determine what the shape, center and potential outliers of the depicted data may be.
It can be challenging for many data scientists if they’re to extract the shape, center and outlier from raw summary statistics.
So at this point, the data scientist has reasonably confirmed that the data shows what the SMEs believe. The SMEs will also learn information that contradicts what they initially believe.
Univariate and Multivariate Statistical Analyses
The relationship between data scientists and SMEs in the middle of the whole data science implementation lifecycle cannot be overstated. After such discussions, univariate and multivariate statistical analyses may be conducted to solve the problem.
It may also be necessary to fit a data model to solve a problem. The model-fitting phase involves:
- Identifying the target or response variable of interest
- Identifying the type of model based on the target variable
- Performing feature or variable selection
- Testing out a few models before settling on a subset to use
The model-fitting phase also involves further analyses. For example, the data can be split into parts, so that the model can be trained on one part and tested on the other.
The rationale is to get a sense of how well the model will do in principle when it is deployed into real-life applications like ordering food using an app or making a bank deposit.
Summarizing and Simplifying Data Science Results
The final stage of the data science lifecycle involves packaging the insights in a way that is easily consumed by non-data scientists and also easy to interpret.
For instance, if a financial institution is using data science to predict who is likely to be a high-value customers due to their deposits, then the presentation will likely include a graph showing the characteristics of these high-value customers.
Such a presentation may also use separate graphs to include other information about high-value customers:
- Their probabilities of making deposits
- The number of people who are high-value customers
Creating a segment of a group based on their probabilities can be even more useful to decision makers, who can use this type of information to make data-driven decisions.
Forecasting Future Trends
Data science is not simply a comprehensive understanding of past data, but also predictive analysis, leveraging structured and unstructured data, to forecast future trends.
Using data science helps scientists solve complex questions through this predictive approach, employing machine learning algorithms and statistical analysis techniques to identify patterns within the data, driving business outcomes.
The customer lifetime value, for example, can be accurately estimated using these techniques, which in turn helps in refining targeted advertising strategies.
However, the vast scope of data science also encompasses domains such as medical image analysis, augmented reality, and advanced image recognition.
Each of these fields can use data science to leverage the power of data science algorithms and computer vision technology to offer innovative solutions to intricate problems.
Improving Internet Search Engines
Additionally, data science makes search engines more intelligent. By analyzing data mining a person's past behavior, location data, and previous search queries, search engines can deliver more relevant results.
Whether it's refining manufacturing data, enhancing customer data, or leading drug development, the power of data science is immense.
With the increasing volume of data, the role of data scientists, data engineers, and data analysts is becoming more vital than ever.
As we continue to produce more data points, the importance of machine learning techniques and data science only continues to grow. The transformation that data science brings is boundless, paving the way for a future driven by data-based decision-making.
Data Science Involves Both Machine Learning and Artificial Intelligence
At its core, data science also consists of machine learning (ML) and artificial intelligence (AI). Technically, ML is a part of AI. However, folks tend to separate these two concepts today, although one is a subset of the other.
ML Versus AI
AI is simply the automation of processes using business rules, such as automating a car-washing process.
By contrast, ML involves teaching mathematical algorithms to learn information and self-improve over time as more data is introduced to the algorithms.
In essence, it is necessary to automate the ML pipeline to make it useful. A data scientist calls that the deployment of machine learning algorithm to an endpoint. In other words, it means making the algorithm available for other services to use.
Fraud Detection Algorithm
But the key takeaway is that when a machine learning algorithm is built, it gets deployed to be used by the services for which it's intended.
A typical example of a machine learning algorithm in use today is the fraud detection algorithm used to catch fraudsters trying to illegally withdraw money from a bank account.
Note that after an algorithm has been deployed, it is also monitored for any decline in performance. This monitoring is typical because machine learning algorithms are trained on historical data.
Data Can Change, So Algorithms Should Change as Well
As we use algorithms in the future, the meaningful data may change and that - in turn - will affect the performance of the algorithms. In that type of situation, the algorithm will be retrained on the new dataset and redeployed.
ML is multidisciplinary and consists of mathematics, statistics, computer science, and business intelligence at its core. However, using ML does not translate to mastering each of these core areas. Instead, it means having enough domain knowledge of those core areas to reasonably perform required duties at the baseline. It also means a willingness to grow your skillset in this area from the baseline up.
Communication and Deductive Reasoning
ML also utilizes communication, deductive reasoning and interpretations of statistics models. However, being able to succinctly communicate the implications of a high dimensional data analysis is the key component of data science and its applications.
It is not sufficient to only know how to apply the core requirements to solving problems; even more important is the ability to communicate the outcome of your solution so that decision makers can act on it.
ML and AI: Business Applications of Data Science
Today, many executives and leaders are embracing ML and AI applications across businesses. But there are still many leaders who shy away from ML and AI because of the lack of clarity in applications.
Data scientists and data analysts should be able to clearly communicate results and valuable insights effectively to a non-technical audience. Otherwise, they risk losing key decision makers and supporters.
The Use of Data Science Continues to Grow
Data science has actually been around for a long time, given that ML and AI have been in use for many years. In recent times, though, the usage of ML and AI has significantly grown, and the amount of data available has also become much, much larger.
The rationale for this change is quite simple. Today, there are high-powered computers that can run algorithms effectively and provide useful outcomes in a reasonable amount of time.
How We Use ML and AI Today
In fact, ML and AI are woven into modern culture. If you use a phone, deposit checks at a bank, buy a bus ticket, shop online, or pump gas, you have used both AI and ML.
To put it mildly, ML and AI are inextricably linked to our way of life today from driving our cars to turning on the lights in our living rooms.
While there has been increasing use of these technologies, they also lead to a much larger amount of data than we used to have. This has also changed the way we typically store and interact with data.
Data Science and Cloud Computing Platforms
Today to compensate for the large amount of data streams that needs to be stored and used later, we use cloud storage infrastructure. Even so, there are still more insights to be discovered as the complexity and volume of Big Data continues to grow.
A data lake is a popular buzzword in the data science world and is defined by Google as "a data lake is a centralized repository designed to store, process, and secure large amounts of structured, semi-structured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits."
Cleaning up the Data
Data lakes are designed to minimize the time spent in collecting data to pushing that data out to different destinations for use. In a data lake, data can be cleaned up and made more convenient to use in a statistical analysis, for instance.
Also, data can be properly protected through the use of tight security to track who can access specific data. It also necessary to protect potentially sensitive data.
In addition, data lakes can also involve training complex machine learning algorithms how to perform certain automated tasks that can be recorded in a log. As a result, a data lake becomes a one-stop shop for any organization that thrives on speedy outcomes.
Autonomous Vehicles and Facial Recognition
In the future, there will be newer and better approaches for tackling data science processes, some of which include the use of different varieties of deep learning algorithms. Different versions of deep learning algorithms are already in many aspects of our lives, including autonomous vehicles and facial recognition technology at airports.
The Future of Data Science is Bright
As the field of data science expands, meaningful data will continue to help solve a wide range of technological problems.
These include the improving the accuracy of facial image recognition, improving disease detection (e.g. predicting the growth of a cancer tumor before it does), and predicting potential car crashes. These data science applications will all contribute to enriching the lives of humanity and making life better for people in many ways.
Data Science Applications Transforming Society
The potential of using data science and applications to transform society is indeed vast. As we advance towards an era dominated by data, data science is set to play a pivotal role.
We are already witnessing the impact of data science in sectors like health care, where machine learning models and statistical techniques are used in medical image analysis to detect diseases - contributing significantly to early diagnosis and better patient outcomes.
By mining vast amounts of previous data and leveraging machine learning algorithms, data scientists solve problems like forecasting potential car crashes, enhancing a user's past behavior based predictions for targeted advertising, and anticipating disease progression.
The value of predictive analytics extends to other areas too, such as estimating customer lifetime value, refining digital ads, and optimizing internet search engines.
Data science helps society in numerous ways and has become a critical aspect of our everyday lives - and data science will continue to shape the future. Data science applications such as search engines will continue to improve.
Augmented reality, image recognition and other data science applications - once the stuff of sci-fi movies will soon become a day-to-day component of the world we live in for most people.
Data Science Training Programs
Data science training programs, with a comprehensive data science course, may help to equip future data scientists with the necessary skills to perform these tasks.
From data extraction, data wrangling, data reporting to training data and creating data pipelines, scientists are trained to handle the full data science lifecycle.
A solid understanding of data science concepts and data science techniques, along with computing knowledge, is often essential in this ever-evolving field.
American Public University's Associate Degree in Data Science
If you're looking to learn about data science and data science applications, then American Public University's Associate Degree in Data Science is a great starting point. This academic program is tailored to provide foundational skills in data science.
Along with learning data science, the program is designed to introduce students to all aspects of this rapidly evolving field, combining mathematical and statistical models, methodologies and data-driven decision-making strategies.
A Comprehensive Curriculum
The program's curriculum is meticulously crafted to cover a range of relevant subjects in data science and data science applications. Over the course of 60 credit hours, students will delve into topics such as the principles of data science, how to use data science, database management systems, data visualization, and mathematics for computer science.
Furthermore, this course helps to enable students to acquire experience by applying theoretical knowledge to real-life data science problems, helping to prepare them for a multitude of data science scenarios.
The curriculum also emphasizes the significance of data science ethics and privacy, helping to ensure students are equipped to handle potentially sensitive information responsibly.
Selected Courses in American Public University's Data Science Associate Degree
Here are just a few of the classes on offer in the University's data science associate degree program.
- Introduction to Data Science
- Functional Methods and Coding
- Exploratory Data Analysis
- Data Visualization
Flexible and Asynchronous Learning Environment
American Public University recognizes the dynamic needs of its student population and offers a flexible and asynchronous class schedule.
This flexibility enables students to learn about data science and data science applications at their own pace, accommodating personal, professional, or other commitments.
Students can access course materials, assignments, and lectures anytime and anywhere, making this data science degree program suitable for those seeking a balanced approach to education.
Moreover, the asynchronous learning model fosters a student-centered environment. Students can engage with the course content, peers, and faculty in various interactive forums, thus helping to enhance the learning experience.
The Associate Degree in Data Science program benefits greatly from the expertise of the faculty at American Public University. Many of these professionals bring a wealth of real-world experience in data science and data science applications.
This diverse group of academics and professionals share their data science insights, experiences, and knowledge with students, offering a practical perspective on the topics covered.
Faculty members are dedicated to fostering a supportive and engaging learning environment. They provide personalized guidance to each student, facilitating a deep understanding of the subject matter and encouraging the development of analytical skills.
Their vast experience enhances the course content, effectively bridging the gap between theory and practice.
American Public University's Associate Degree in Computer Technology
American Public University also offers an Associate Degree in Computer Technology designed to provide students with a solid foundation in the world of computer systems. This program aims to foster technical proficiency and a deep understanding of computing technologies.
The program's curriculum, comprising 60 credit hours, covers a broad spectrum of topics. It introduces students to the fundamentals of computer science, information systems management, and cybersecurity.
Additionally, it includes practical modules that offer experience with hardware and software applications.
American Public University's Bachelor's Degrees Related to Data Science Applications
Additionally, American Public University's Bachelor's Degree in Information Technology Management emphasizes the application and management of information technology (IT) within organizations.
The program is tailored to instill in-depth knowledge of IT, focusing on the strategic role of technology in business.
The 120-credit-hour curriculum covers a wide array of topics including data management, IT project management, networking concepts, and information security. It combines theoretical foundations with practical applications, enabling students to understand and manage the dynamic landscape of information technology.
Then there is the University's Bachelor's Degree in Computer Technology program which offers comprehensive exposure to diverse areas of computer systems and applications.
The curriculum covers essential topics such as operating systems, programming, and data structures. This degree, supported by a flexible learning schedule and faculty with IT experience, fosters understanding of technological systems and concepts.
About the Author
Dr. Frank Appiah is a faculty member in the School of Science, Technology, Engineering and Math (STEM). He is a trained statistician with over 14 years of experience in industry and academia and very passionate about data science and its applications, ranging from teaching classes in data science concepts to uncovering new ways of modernizing medicine. Frank holds a B.Ed. in mathematics from the University of Cape Coast, an M.S. in mathematics from Youngstown State University, an M.S. in statistics from the University of Kentucky, and a Ph.D. in epidemiology and biostatistics from the University of Kentucky.