Data science

It most certainly seems that the buzzword of the millennium is data, especially big data. Regardless of the event, result, or study reported, it all comes down to data science in determining the success or failure of a project or process. 

On any given day, we are creating massive amounts of data collected from a multitude of sources. Sensors, monitors, and detectors, part of the IoT (Internet of Things) network, around us are continually collecting data. While this may have the makings of a "big brother" scenario, rest assured that this collected data can ultimately improve the way we work, live, and even play. 

What is Data science?

From consumer purchasing habits and traffic cameras to energy consumption and weather reports, enormous amounts of raw data collected from computers and IoT devices around us must be effectively stored, processed, and analyzed to be of any benefit. 

The data gathering process is continually in motion, steadily increasing data volumes every minute.  The amount of big data compiled from businesses alone is monumental. In fact, by the end of 2020, volume is projected to reach 44 trillion gigabytes, the largest amount seen yet in the business world. The question is, how will this plethora of data be efficiently processed?

The answer to this question lies in the application of data science. Essentially, data science is how structured and unstructured data is converted into useful information by employing analytical tools such as algorithms, statistical analysis, machine learning, and other systems. The end goal is to garner insights resulting from these processes that can be used to make informed and strategic decisions. 

The Importance of Data science

In the business world, data science techniques are used to extract information to help improve revenue, reduce costs, and improve customer service. The processed information drives management's decision-making and often determines their level of success against the competition. Without a transformation process, however, raw data is useless. 

Companies also use data science for the development of innovative new products through the analysis of consumer feedback and reviews. Monitoring trends and customer behavior data contributes to more successful marketing efforts as well. 

The health care sector also continually generates large amounts of critical data for patients' electronic medical records. For this reason, data science is an essential tool used to help prevent errors in patient management. Analyzed data is also used to maintain and improve health facility operations, ensuring proper staffing and medical supply inventories. 

The Roles Of Data Scientists, Engineers, and Analysts

The tremendous growth of data science applications across many business and industrial sectors requires a technical expert to assist in making sense of the influx of raw data. The data scientist's role is to conduct processing and analysis, often with an arsenal of software tools, to transform this data into meaningful information.

To clarify the many interrelationships and characteristics of the data scientist, writer Stephan Kolassa created a Venn diagram representing the roles of the data scientist within the four pillars of expertise of communication, statistics, programming, and business. 

Specifically, several of the responsibilities and roles of a data scientist are as follows: 

  • Begin the discovery process with questioning to narrow down what is needed.
  • Acquire, process, and clean the data.
  • Integrate and store the data.
  • Conduct initial data investigation and exploratory data analysis.
  • Decide upon the appropriate models and algorithms to use in the process. 
  • Apply machine learning, statistical modeling, and artificial intelligence tools to the process.
  • Measure and improve results.
  • Present final results to stakeholders and adjust accordingly based on feedback. 

It's no surprise that data scientists will continue to be in demand for the above roles. The U.S. Bureau of Labor Statistics report substantiates this trend stating that the employment of these scientists is expected to rise 16 percent by 2028.

Two other valuable data experts are the data engineer and the data analyst. The data engineer's primary responsibility is to build and maintain the pipeline for incoming data for use by the data scientists. The engineers are responsible for cleaning the data that comes from a variety of sources. Additionally, they develop various software solutions for the extraction, transformation, and loading (ETL) of data. 
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While the data scientist and data engineer work with more intricate technical details, the data analyst's responsibility is to translate the data into accessible information. They are involved in the collection, analysis, and reporting of data to recognize trends and patterns. Data analysts are involved in the creation of reports, dashboards, and other visual tools for senior management decision-making. 

Components of Data Science

Before embarking on a data science project, it is essential that the team be knowledgeable in the four core components of data science. 

data strategy

Data Strategy
Because not all data is created equal, a strategy on how data will be gathered to meet business goals is an essential first component. The team works to prioritize data that is critical to decision-making that will be collected and sorted. Other data that may not be as significant may not be worth the resources to collect. 

data engineering

Data Engineering
This engineering component includes creating software solutions and other systems to access and organize the data. Developing, constructing, and maintaining a data system with the creation of pipelines to transform and transport data into a usable format also falls under this component.

Data Analysis and Mathematical Models

Data Analysis and Mathematical Models
This component uses math and algorithms to describe how to use the data and provide insights and make predictions. Data analysis and mathematical models are also used to create tools, using machine learning to replace a human to think about and execute tasks.

Visualization and Operationalization

Visualization and Operationalization
The visualization aspect involves understanding how the product will integrate into the existing environment and how it can stand out against the competition. Data operationalization is the action component that comes in the form of human intervention, longer-term response, and recommendations.

The Data Science Lifecycle

The data scientist utilizes many tools to assist them in their work. 

  • Computer programming
    Programming languages, including Python ,R, SQL, Java, Julia, and Scala are an essential part of the scientist's toolbox. 

  • Statistics, algorithms, modeling, and data visualization. 
    Scientists use packaged solutions such as the Python-based Scikit-learn, TensorFlow, PyTorch, Pandas, Numpy, and Matplotlib.

  • Research and reporting
    Powerful notebooks and frameworks such as Jupyter and JupyterLab are the most commonly used by data scientists for research and reporting. 

  •  Big data tools
    Scientists are harnessing the power of big data tools such as Hadoop, Spark, Kafka, Hive, Pig, Drill, Presto, and Mahout. 

  • Database management systems
    Accessing and querying Relational Database Management Systems (RDMS), NoSQL, and NewSQL is a skill that data scientists should have. More commonly used ones are MySQL, PostgreSQL, RedShift, Snowflake, MongoDB, Redis, Hadoop, and HBase.

  • Cloud-based technologies
    Cloud-computing is often an essential part of the data scientist's work process for storage and access in addition to machine learning and artificial intelligence. Commonly used providers are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Compute (GDP).

  • Data Quality and Data Testautomation
    And don't forget a tool that ensures all the quality of the data and the automation in the test phases. 

Best Practices

Data science experts have to work with many complex variables and systems in dealing with unstructured big data. To increase chances of success in projects, here are some industry best practices that data science teams should follow. 

  • Leverage innovative open-sourced tools that avoid licensing issues and work on several platforms. These programs are also developed faster and have been found to be more suitable for many situations. 
  • Integrate the cross-industry standard for data mining (CRISP-DM) to help solve business problems. 
  • Involve IT and software developers early in the Proof of Concept (PoC) phase. 
  • Collaborate on a regular basis to communicate about processes, tools, and projects. 
  • Understand biases in data and how models make decisions. New open-source tools such as FairLearn, InterpretML, and LIME can assist data scientists in this area. 
  • Proactively address bias and fairness to avoid risks to competitive, financial, and legal areas. 
  • Ensure strict controls are in place before a model is put into production. 
  • Act on the analytics for measurable impact. 
  • Revisit the analytics periodically to ensure the data is still relevant. 

The Future of Data Science

With millions of devices delivering tremendous amounts of data in every sector, data science will remain a necessity to make sense of it all. As a result, the field of data science will continue to expand with the development of innovative tools and systems to help interpret data for business leaders.   

However, with this demand, come challenges that data science professionals must overcome to be successful in the field. The difficulty in integrating open-source data science tools is one such challenge. Unfortunately, a recent report shows that many organizations are slow to adopt open source tools. 

Another problem is that organizations are facing difficulties seeking qualified data scientist candidates. A report found a wide disparity between what students are learning in college programs and the abilities that enterprises need in their data science staff. 

Addressing bias in machine learning models represents another obstacle in the future of the field. A recent report reveals that only 15% of enterprises surveyed have a bias mitigation solution, with 39% having no plans to address bias at all.           

Though the above can present difficulties in the data collection and analysis process, data science continues to provide meaningful solutions to organizations and industries across the board.     

BiG EVAL offers a variety of information quality solutions for businesses of any size and sector. Contact us for more information on how our professional tools can improve your company's data to keep you a step ahead of the competition.

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Data science