“Hey, Siri, Google ‘data science.’”
With these five simple words, a selection of pages with the definition of data science immediately appears. This search is just one of the 3.5 billion completed on Google each day. Since its start in 1998, Google has invested time and money into optimizing its algorithms to deliver the most relatable, timely search results for users. Google’s success hinges on the ability of its employees to use their data science skills to help users find the right information.
Data science is the process and art of using data to solve problems. Data scientists use various technologies and practices to collect, explore, analyze, monitor, and manage data. In the Google search example, the search query is just one data point sent to Google. To solve the problem of finding the best result for the searcher, Google uses dozens of data points in its algorithms, such as website speeds, popular sites, and voice-optimized webpages.
Google is just one company using data science as a competitive advantage. But, for many businesses, applying data science processes is out of reach. Without the expertise of data scientists and experts, companies struggle to make accurate, data-driven business decisions. In this guide, you’ll explore the most relevant data science skills needed to excel in the field.
Understanding Math and Statistics for Data Science
By 2020, the number of available bytes of data is set to exceed by 40 times the number of observable stars in the universe. This massive amount of data is known as big data, and it creates limitless opportunities for analysis. The first skill professionals need to have for data science is an understanding of math and statistical significance.
Statistics is the process of working with and analyzing a dataset to identify unique mathematical characteristics. Mean, variance, average, and other terms are used to describe the dataset. With this information, professionals, like analysts and data scientists, can make reliable decisions. There are two primary statistical methods: descriptive and inferential statistics.
The descriptive statistics method examines an entire dataset or a smaller subset. The goal of this method is to find the central tendency and variability, describing the dataset quantitatively. Common uses of descriptive statistics include calculating a class’s GPA and salary figures, like median salaries for data scientists in the U.S. Descriptive statistics are useful for taking large amounts of data and creating smaller, understandable pieces. However, this statistical method doesn’t provide details about the data, like the type of courses calculated in the GPA. While comparing large amounts of data is more manageable with descriptive statistics, risk is associated with using just this method.
Inferential statistics infer conclusions about the collected dataset to apply to other relevant datasets. The purpose of inferential statistics is to determine the probability that a result will recur. Using hypothesis testing methods, analysts will use a random sample of the collected data to test an idea. If that hypothesis proved incorrect, the analyst continues testing the data to find the true hypothesis, which could be different than what the scientist expects.
Using Statistics in Data Science
Descriptive and inferential statistics set the foundation for data science. Understanding both descriptive and inferential statistics is a fundamental data science skill that opens possibilities for machine learning, artificial intelligence (AI), and other advanced techniques. In machine learning, data scientists train algorithms to predict events based on relevant data. As an example, a manufacturing company can use machine learning to predict the quality of its product if costs decrease. With advanced statistics skills, you can integrate practices like Bayesian statistics to determine the probability of events based on historical data and the likelihood of recurrence. With other methods to use and analyze data, data scientists rely on computer programming to handle large amounts of data and complicated formulas.
What Is R Programming?
Mathematicians, scientists, statisticians — all reached a human limit when calculating large, complicated formulas or storing vast amounts of tabulated data. Not to be deterred, human ingenuity created a “brain” capable of making fast calculations with large amounts of data: the computer.
A Brief History of Programming Languages
Computer languages were a result of mathematical languages, and they’ve evolved with technology. The first programming language, FORTRAN, is still a common computing language, used for scientific modeling and mathematical calculations. FORTRAN led to the development of the primary statistical programming language: R.
Unlike FORTRAN, R programming is a language and environment specifically for data analysis. R is a dialect of the language S, which was created by Bell Telephone Laboratories in 1976. S was developed as a statistical analysis environment, focused on making data analysis easier. S developers wanted a coherent environment in which statisticians could easily implement statistical techniques.
Unlike S, R is free software. The code is available on most computer platforms for coders who want to customize their data analysis. A benefit for many R programming users is the ease of displaying their data in a visually appealing way.
Using R Programming for Data Science
Knowing R programming for data science is without question a critical skill for data scientists. R programming is the second most used tool in the profession. While the program holds standard expressions and functions, users are able to create their own.
Using R programming for data science is one of the top skills for data scientists along with math, statistics, critical thinking, and machine learning. It’s one of the programming languages that Maryville University’s online Bachelor of Science in Data Science students learn. The flexibility of R programming is useful when completing other data science functions, like data wrangling and visualization.
What Is Data Wrangling?
Big data contains two types of data: structured and unstructured. Structured data is already organized and formatted. It’s often numerical data like dates already stored in sortable columns and rows. Unstructured data doesn’t fit into these standard databases. Audio files, videos, and documents are unstructured and require a different process for collecting: data wrangling.
The Data Wrangling Process
Data wrangling is the process of obtaining the right data, combining relevant datasets, and cleaning the results for interpretation. Also known as data munging, data wrangling requires both data science skills and nontechnical skills to turn unstructured data into structured data. Below are a few of the critical steps in the data wrangling process:
- Clarify the data ask. The data wrangling process begins with a question, which can come from scientific researchers, academia, business leaders, and anyone else who wants to solve a problem. However, before you can dive into the data, they must clarify what’s being asked. They must confirm the time frame, types of data to collect, data subsets, and more before moving onto the next step.
- Collect the data. In some cases, you’ll need access to data that’s owned by other businesses, research firms, and governments for your wrangling projects. With legislation and pressure to protect this data, other owners may only provide scrubbed versions. The request process can take time, and if the provided data isn’t correct or is corrupted, the process has to start again.
- Remove object ambiguity. Data objects, also known as data entities, are the key data types in a dataset. A common key entity is customer ID. However, you must clarify what data will support this concept, such as customer address, bank account, and email. Without this clarification, later data modeling will be inaccurate.
- Identify relationships. This step of the data wrangling processes leverages data warehouses. Data warehouses are used for examining large amounts of historical data and can be aggregated to show relationships between various data sources.
- Create machine learning features. To leverage machine learning in the new data model, you’ll need to create features. Features are typically structured columns of data that algorithms use to find a result. If data is missing in the columns or time frames are mismatched, the model will fail.
- Explore data. The final step of data wrangling is to parse through the remaining data and remove redundancies. At this stage, redundancies can be tricky to spot. Algorithms may have a hard time selecting the correct data, which is why humans are still needed for the process.
Wrangling data is a significant part of a data scientist’s position, taking up to 80% of the scientist’s time. While the process is arduous, it’s a critical data science skill, as it’s often during this phase that many important discoveries are made.
Even if you’re not interested in becoming a data scientist, understanding the data wrangling process challenges you to ask the right questions, define the use case for the data, and help identify redundancies in data relationships.
Data analysis is an ongoing step in the data science process, involving the scrubbing and transforming of data into a visual form. This part of the process focuses on answering specific questions about a known dataset and validating the answer.
There are three primary techniques for data analysis. Descriptive statistics, the same process mentioned above to describe the dataset quantitatively is used together with exploratory data analysis and confirmatory data analysis. We’ll examine exploratory and confirmatory data analysis.
Exploratory Data Analysis
Exploratory data analysis (EDA) is the process of visually transforming a dataset to look for answers. A data scientist will transform the data into graphs, charts, and other visual models to see if the answer being searched for is correct. This analysis often finds errors in the data, as outliers — instances that fall outside the norm — trends, and missing data are identified and corrected.
Confirmatory Data Analysis
Answers found in the EDA phase of data analysis are only assumed to be true. In confirmatory data analysis (CDA), these assumptions are challenged by using the hypothesis theory, challenging the answer with degrees of variance, confidence, and significance.
Data Analysis Techniques in Practice
The exploratory phase tests the data and identifies clues about its meaning. If a pattern or a trend emerges, data scientists dig into this data, confirming that the trend is correct. The following are the steps that are performed chronologically and repeated until the data is accurate:
- Explore available data.
- Remove data anomalies, outliers, and patterns.
- Transform the remaining data visually.
- Identify anomalies, outliers, and patterns.
- Repeat steps 2-4 until data is cleaned.
But data analysis doesn’t stop there. As new data enters, it’ll go through the same analysis process so it can be compared over time. Enhanced data science skills and R programming makes it easier to explore large datasets and test various outcomes. Students in Maryville University’s online Bachelor of Science in Data Science program apply data analysis to examples often seen in the workplace such as uncovering efficiency improvements to customer experience, and increasing profit margins by improving product quality.
What Is Data Visualization?
Data visualization is the representation of data in an easy-to-digest format, such as graphs, charts, and images. There are a variety of areas where data scientists transform data to view it visually. However, these visualizations are simple, lacking color, fonts, and other aesthetically pleasing features.
After collecting, processing, cleaning, exploring, and modeling the data, the data is transformed from a basic visual form into an interactive display for an end-user. Users can be business leaders, scientists, policymakers, or the public. The purpose of creating a pleasing visual display is to communicate data clearly and effectively.
Data visualization is what makes sense to our brains. Humans gravitate toward images because it takes our brains less time to comprehend information when it’s presented visually. There are countless examples of data visualization, including line graphs depicting Google search trends, maps with various size circles to indicate population density, and bar charts that depict age demographic changes in a country.
Making Decisions with Data Visualizations
Regardless of which method you use, you must determine which visualization fits the dataset and expresses the data most effectively. Data scientists leverage their data science skills to answer the question, What is the user trying to answer with this data?
Data visualization is a powerful method of understanding the key functions of a business and predicting changes. The following are only a few examples of how businesses use data visualization:
- Viewing sales volumes over time to look for seasonal changes
- Monitoring customer satisfaction rating
- Watching production costs across product lines
- Reporting how customers use the company’s website
With easy-to-understand visualizations, the business can identify patterns in each of these areas and take action to capture more sales, increase customer satisfaction, decrease costs, and improve website presence.
Data visualization isn’t unique to the business world. Scientific research and academic studies also use data visualization to help researchers understand trends in their data and make predictions, such as disease progression or responsiveness of medical treatments.
Tools for Data Visualization
Data scientists, or data visualization engineers, use various skills and tools for data visualization. Mathematics, engineering algorithms, and data analysis skills are combined with knowledge of graphics, design aesthetics, and visual coding to create engaging visuals. Programming languages, such as Python, C, and Java, and to some extent R, are popular languages used for data visualization.
The internet of things and big data have made it easier to create visually appealing, functional displays without advanced data science skills. Commercial products (Tableau, Highcharts, and others) have become popular with organizations that don’t have dedicated data scientists. Commercial products are a great step for smaller organizations that want to leverage the power of data. However, there’s one final data science skill that data scientists and enthusiasts alike should master: communication.
Data Communication: Effectively Explaining Findings to Management
While algorithms, machine learning, and data visualizations have turned once inaccessible data into actionable insights, the data still needs a storyteller.
Communication is an essential data science skill for anyone working with data. Data scientists are in a unique position. They have to translate mathematical languages and technical knowledge into understandable, actionable insights for management.
As in the data science process, you need to start with the question, Why is this important? Using this lens, you can explain to management how the data, algorithms, challenges, and results came to be and the value for the business strategy.
Inspiring Others with Data
Contrary to common belief, data can be exciting. Managing a growing financial portfolio, watching the numbers on the scale drop — these are simple examples of viewing exciting data results.
And that’s what excites most people: results. Positive results are rewarding and motivating; this is especially the case for management. Executives like watching sales and profits climb and costs decrease.
You’ll need to tap into this reward paradigm to inspire business leaders to make decisions. It can be difficult to balance inspiration and excitement. Sharing the step-by-step process of how a problem was solved by using a brand-new algorithm can be intrinsically rewarding for you, but less interesting to management.
The following tips can improve your data communication skills and prepare you for business presentations to management:
- Speak simply and in business terms. Most employees and executives won’t know programming or advanced mathematical languages. Even with a basic understanding, management may struggle to relate the findings to the business. Data scientists can provide context by speaking the “business language.” Using the same terms will foster collaboration and increase understanding.
- Use emotional storytelling techniques. Numbers don’t tell a story; humans do. That stories over 4,000 years old are still being told today is evidence of our need for stories. To tell an emotionally engaging story, you should structure data in four parts: the situation, problem, solution, and next steps. In each section, remember who the character of the story is and how you’re trying to help the character.
The Future of Data Science
“I found this on the web for ‘data science.’”
Siri may have pulled a search result that talks about the future of data science. Many businesses and industries are really excited about the potential for machine learning and AI. But should you focus on learning about these technologies?
Not yet. While automation and AI are exciting, the reality is that many of these companies don’t have the infrastructure or the data to support their goals.
With the majority of time spent on collecting and cleaning data, there’s the ethical dilemma of managing and using it responsibly. Europe’s General Data Protection Regulation (GDPR) and other examples have raised critical questions about privacy. Data scientists will need to navigate the GDPR and other data privacy laws to use data in a beneficial and ethical manner.
Data science is still a very technical field. You must apply advanced statistical methods to datasets and know programming languages to collect, clean, analyze, visualize, and store data. Through a practical, hands-on program like Maryville University’s online Bachelor of Science in Data Science program, you can gain the data science skills that can set you apart and hinge on your ability to communicate the value of your technical skills to the rest of your organization. Explore your future in data science by visiting the online Bachelor of Science in Data Science program page to learn more.
Building Skills for the Future of Machine Learning
The Future of Programming and the Top Languages Programmers Should Know
The Future of Artificial Intelligence in Work and Everyday Life
Altair, What Is Data Wrangling?
BBC, “Our Fiction Addiction: Why Humans Need Stories”
Big Data Framework, Data Types: Structured vs. Unstructured Data
Bookdown, History and Overview of R
CIO, “Essential Skills and Traits of Elite Data Scientists”
CIO, “What Is a Data Scientist? A Key Data Analytics Role and a Lucrative Career”
CMSWire, “5 Communication Skills Every Data Scientist Needs”
Computer Hope, Computer Programming History
Data Science Central, “Using Data Visualization Graphs for Better Decision Making in Digital Marketing”
Elder Research, “What Is Data Wrangling and Why Does It Take So Long?”
Forbes, “Data Is the Foundation for Artificial Intelligence and Machine Learning”
Gemalto, “Gemalto Research Reveals Businesses Collect More Data Than They Can Handle”
Harvard Business Review, “What Data Scientists Really Do, According to 35 Data Scientists”
Informatica, “What Is Data Warehousing?”
Internet Live Stats, Google Search Statistics
Investopedia, Descriptive Statistics
Investopedia, Hypothesis Testing
KDnuggets, “New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll”
Microsoft, Data Entities Overview
Pat Research, What Is Data Analysis?
R for Data Science, Exploratory Data Analysis
Sisense, “Data Science vs. Data Analytics — What’s the Difference?”
Sisense, “Exploratory and Confirmatory Analysis: What’s the Difference?”
Tableau, “Data Visualization Beginner’s Guide: A Definition, Examples, and Learning Resources”
Towards Data Science, “9 Data Visualization Tools That You Cannot Miss in 2019”
Towards Data Science, “Bayesian Statistics for Data Science”
Towards Data Science, “Fundamental Techniques of Feature Engineering for Machine Learning”
Towards Data Science, “Statistics: Descriptive and Inferential”
Towards Data Science, “Storytelling for Data Scientists”
Towards Data Science, “The 5 Basic Statistics Concepts Data Scientists Need to Know”
Towards Data Science, “Visualize Programming Language Popularity Using Tiobeindexpy”
Towards Data Science, “Why Data Science Is the Career of the Future”
Verywell Mind, “Motivation: Psychological Factors That Guide Behavior”