Data Science and Predictive Analytics

Data science is an interdisciplinary field of study that involves the process of using algorithms, data mining methods, and systems to extract knowledge and insights from various types of historical data. It applies advanced analytics (which includes programming skills and knowledge of mathematics and statistics) and machine learning to help users predict and optimize business outcomes.

Predictive analytics, as a quantitative discipline, is a branch of data science.

Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. Modeling provides results in the form of predictions that represent a probability of the target variable (e.g., profit) based on estimated significance from a set of input variables.

Predictive modeling techniques in common use are the following:

  • Decision Trees: Classification models that partition data into subsets based on categories of input variables. This helps understand someone’s path of decisions. A popular approach.
  • Regression: One of the most popular methods in statistics. Regression analysis estimates relationships among variables. Intended for continuous data that can be assumed to follow a normal distribution, it finds key patterns in large data sets. Common in financial models.
  • Neural Networks: Sophisticated techniques capable of modeling extremely complex relationships. They’re popular because they’re powerful and flexible. The power comes in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data. Neural networks are often used to confirm findings from simple techniques such as regression and decision trees. Neural networks are based on pattern recognition and some AI processes that graphically “model” parameters and attempt to mimic how the human brain functions. Considered a cutting-edge approach to predictive modeling.

Why Predictive Analytics & Risk Management?

Predictive analytics is no longer a career concept of the future. Predictive analytics has already arrived. The drive to implement a graduate program in Predictive Analytics & Risk Management is a result of a response from the financial services and other industries seeking professionals with the knowledge and skills to succeed in the industry both today and in the future.

Big data is here — and it is here to stay. Traditional data analysis in a business context has limitations in its inability to connect disparate data sources. With the continuing expansion of both the volume and source of data, organizations are turning to this field, and, in particular, machine learning, in order to apply principles of predictive analytics in analyzing this expanded data universe, to make rational predictions, and to provide more rigorous quantitative business solutions.

Value in predictive analytics includes, but is not limited to, the following:

  • Risk reduction and risk management
  • Creating operational efficiency
  • Creating cost efficiencies (including fraud detection)
  • Gaining a competitive advantage
  • Facilitating product development and innovation
  • Enhancing business planning and strategy (including problem solving)
  • Achieving and exceeding customer expectations

Predictive analytics, as a discipline, has actually been in existence for decades; however, the world has had to wait for the technology to catch up so that the application of predictive analytics could be possible in today’s business community. More rapid and cost-effective computing, various choices in user-friendly data management software, and ever-increasing competition in today’s economy have been driving forces toward the demand for these types of data analysis and machine learning.

Data science and predictive analytics will continue to expand and evolve. The future continue growth in this field can be attributable to various reasons such as these:

  • The expectation of continued expansion of data by volume and further migration to cloud data
    • Data growth is expected to be exponential
    • Internet use continues to grow
    • Connected devices and embedded systems across data continue to proliferate globally
  • The future impact of machine learning
    • A rapidly developing technology
    • Increased accessibility (based on lower-cost, affordable solutions) by firms to machine learning and AI technology
  • An increase in demand for jobs in data science and leadership positions such as chief data officer (CDO)
    • Data science positions are relatively new but already in high demand
    • Skill deficiencies have been identified in technology staff at thousands of firms; skill gaps have been identified in big data/analytics, security, and AI
      • Data platforms and tools
      • Programming languages
      • Machine learning algorithms
      • Data manipulation techniques — data preparation, building data pipelines, and managing ETL (extract, transform, load) processes are some examples
    • Higher demand means more competitive salaries
    • Although not well-defined as of yet, the chief data officer (CDO) is a C-level executive role that is becoming the norm within organizations that are shifting towards a data science environment
      • The issue of privacy remains at the forefront
      • Data security and privacy have always been a concern
      • Exposure is greater due to the proliferation of big data
      • There are increased risks of cyberattacks in both scope and complexity
      • Adherence to security standards has been inconsistent
  • Prioritization of fast and actionable data