Data is becoming one of the most valuable assets for businesses across all sectors. For instance, data generated during marketing campaigns can inform company leaders of valuable consumer insights to better craft more effective advertisements. As more and more information is generated from key stakeholders such as companies and consumers, businesses and organizations are able to improve strategies and engage target audiences.
That is where data science lends a hand. Though falling under the computer science umbrella, data science heralds specifically from experts who expanded statistics methods to apply to information interpretation and technology. The roots of this subject area go back to 1962 with John W. Tukey’s “The Future of Data Analysis” that argued statistics should be applied to data analysis as a science as opposed to a mathematical method.
In modern memory, data science has emerged as a critical component of advancing technology. As more consumers embrace casual tech like smart phones and tablets, the potential to capitalize on data has accelerated exponentially. Data scientists (data science practitioners) are professionals dedicated to assessing information using statistics, informatics and overall analysis to facilitate how data is utilized and integrated in critical decision-making.
What is Data Science?
Data science is a multi-disciplinary field that entails collecting, analyzing and interpreting organizational data. Preparing data for evaluation includes various steps, including cleansing, aggregating, and manipulating information so that it is set for particular processing actions. Proper data analysis entails developing and using algorithms and analytics.
Data science is typically applied by leveraging software that carefully peruses data to identify patterns. The purpose of this exercise is to transform these patterns into predictions informing critical decisions. These predictions are then tested and certified with scientific methods and experiments.
So, what are data scientists? They are professionals who extract data, perform analysis and often present results to key company stakeholders. Data scientists are required to be well-versed in computer science and hard science skills transcendent of data analysts. These skillsets includes application of mathematics and statistics, knowledge of scientific method, the ability to evaluate and prepare data (for example, SQL, data mining and data integration), extraction of insights from information using predictive analytics and artificial intelligence (AI), programming and designing applications to automate data processing, outlining narratives to showcase compelling results to company leaders and stakeholders, and translating complex outcomes into workable solutions.
What Processes do Data Scientists Implement?
Under the computer science umbrella, data scientists leverage several foundational skills, including programming and application design. To understand abilities required of data scientists, it’s crucial to understand the data science lifecycle—widely referred to as the data science pipeline— that includes several parallel and continuing processes. The most common elements of a data science lifecycle are:
- Capture: This step entails generating raw structured and unstructured data from all corresponding sources through various methods— for instance, manual entry, web scraping, and capturing data from systems and devices in real time.
- Prepare and maintain: This process demands translating raw data into uniform formats for analytics, machine learning or deep learning models. Preparing and maintaining entails everything from cleansing, deduplicating, and reformatting information to using extract, transform and load (ETL) and other data integration technologies to synthesize data into aggregate collections.
- Preprocess or process: These operations are when data scientists analyze biases, patterns, ranges and distributions of values to determine the information’s compatibility with various deep learning algorithms.
- Analyze: This step requires data scientists to perform statistical analysis and other assessments to derive insights from the prepared information.
- Communicate: Lastly, data scientists present information as reports, charts and data visualizations that provide insights easily digestible for decision-makers. Data scientists often incorporate dashboard visualizations.
What Skills Do Data Scientists Utilize?
To tackle the many complex steps involved in effectively applying data science, a variety of integral skills separate data scientists from other computer science professionals. These include:
- Advanced Statistics and Probability – These concepts are the backbone of data science. Understanding descriptive statistics like mean, median, mode, variance and the standard deviation is a requirement. Data scientists should also understand statistical significance and how to decipher relevant data.
- Data Analysis and Manipulation – Data analysis requires cross-tabulating information to understand the story behind the data. Also known as ‘data wrangling’, manipulation entails cleansing information to transform it into a format ready for analysis at later stages.
- Data Visualization – This step is crucial for presenting and interpreting data. Using programming tools and charts, data scientists paint narratives with relevant data points to express key recommendations to company stakeholders.
- Machine Learning – This term essentially refers to how data scientists craft predictive models. Professionals run tests to determine scenarios based on prepared information to gain insights into future possibilities.
- Storytelling – As a data scientist, you will most likely need to consistently describe, explain and recommend data solutions. Apart from visualizing information, you will need to know how to articulate findings and carefully determine appropriate directions for your organization.
Is There a Demand for Data Scientists?
The demand for data scientists has remained steady, as most businesses and institutions are increasingly relying on data-driven decision making. According to Glassdoor, ‘data scientist’ is the number two best job in America in 2021. As more companies discover the benefits of leveraging data to increase sales, attract new audiences and drive decisions, more data scientists will be required by employment markets. Data scientists are generally attractive hires, as they combine several skills of classic computer scientists with more innovative approaches of experienced statisticians and strategists.
What's Driving the Demand for Data Scientists?
As the most profitable and largest companies in the world reveal their successful leveraging of data, their market shares have increased substantially. In order to compete, other businesses are discovering the need to employ data strategies into their daily operations. For example, companies like Penn Mutual are leveraging data to improve client experiences. The supply of data scientists is fairly low, thus the job market is wide open and ripe with opportunity.
Information is One of the Most Important Assets
As data-driven decision making becomes more standardized, data science will remain a growing profession. Data science careers require rigorous study in statistics, probability, machine learning and fundamental computer science subject areas. As data science has only recently been utilized and integrated on a commercial scale, there’s no doubt this field will continue to grow at a rapid rate.