High-quality and relevant data can be a powerful force for good, but flawed data only perpetuates inequalities under the guise of fairness.

At its best, data science can impact global societies in incredible ways. It can work to enhance ocean health, identify and deliver food surpluses to feed the hungry, and use cellphone data to standardize public transportation routes in developing areas like Nairobi.

Data scientists in both the public and private sectors must understand the underlying opportunities to use data in new applications, address potential ethical and bias risks, and weigh the need for data regulation.

Before algorithms can be used appropriately, it’s necessary to access good data sources and evaluate the quality of all available data. According to Vinod Bakthavachalam, a senior data scientist at Coursera, critical questions to ask before using a data set in any application include: Is there measurement error? Do I understand how the data was captured? Are there weird outliers or other abnormal numbers?

“Even if the data on its own is good, there’s always a chance it may be unusable if it’s not right for a specific purpose,” he says.

For example, you may have high-quality data on a consumer’s willingness to spend over $100 on shoes, but perhaps that data was collected during the holiday season when shoppers traditionally spend more and is thus inapplicable to predicting year-round shopping trends. In other words, it may be the best data in the world, but whether it’s the most relevant data is an entirely different matter.

Data scientists must also understand that although algorithms can make a positive difference in society, there is a risk that some algorithms instead further entrench cultural prejudice and bias.

Machine learning algorithms are one of the most common data algorithms in daily life. They are frequently used to suggest products for consumers on e-commerce sites, and they’re also increasingly applied in cases like hiring or lending decisions. Used correctly, such algorithms can remove racial or gender bias by focusing on internal characteristics that predict success, thereby ignoring the human tendency to prefer people who are similar to themselves.

However, used incorrectly, these models simply provide a veneer of respectability to an otherwise unethical process. An algorithm that sees bias in its training data will spit out biased conclusions when fed new data because machine learning algorithms don’t make the best decisions; they make the decision the human that “trained” it would have made. For example, if a company has only hired white males in the past and trains its hiring algorithm using that data, it will perpetuate such hiring practices. Biased data, then, leads to biased results.

To avoid such biases, Coursera deliberately chose to ignore gender when training its machine learning algorithms to recommend classes to potential students.

“In the U.S., women are less likely to enroll in STEM classes, so if we used gender, it wouldn’t recommend certain courses to women,” Bakthavachalam says. “We want to encourage women to enroll in STEM classes and avoid any biases in the algorithms.”

Coursera’s experience underscores the fact that although there is no silver bullet for avoiding algorithmic bias, it’s also not too complicated a problem to fix, either. In fact, it’s more a matter of awareness than a difficult engineering problem to solve, and it begins with the knowledge that artificial intelligence is by no means perfect. According to Bakthavachalam, data scientists must avoid treating machine learning algorithms as black boxes because “if you don’t know what’s going on under the hood, it’s hard to imagine and diagnose issues.”

Data scientists must also be vigilant in their initial examination of training data, a process that needs to have a diverse team and, in some situations, outside reviewers. The biggest risk, according to Bakthavachalam, is that data scientists realize the potential for data misuse, but don’t put in the necessary work to rectify potential issues.

“Everyone has different value systems, and being open and upfront about the algorithm can lead collectively to the right decision,” says Bakthavachalam.

On a positive note, data science makes it easier to eliminate bias by quantifying prejudices and highlighting trends that may otherwise go unnoticed. This allows data scientists to remove bias by analyzing only legitimately relevant information, therefore empowering companies to provide services to previously underserved populations, especially in the financial services realm.

An example is MyBucks, the fintech company powered by a machine learning-enabled, credit-scoring engine that serves the underbanked in 11 African nations. By aggregating large amounts of data, MyBucks has greater insight into which individuals are likely to default, allowing them to move beyond a reliance on more simplistic predictors like credit score.

In Kenya, for instance, data is pulled solely from an individual’s phone, and loans are paid directly into mobile wallets within minutes.

This service is especially important in nations where schools require full tuition payment upfront, historically a significant barrier to pursuing an education in some poorer countries.

Above all, data scientists must avoid getting lost in the techniques and methods of their trade. They must ask questions about who will be affected by the work and how are they ensuring that by doing “good” for one group, they don’t inadvertently harm another.

It’s through transparency about how data is collected, how it’s defined, and its limitations that analysts working together can get the most impactful results. Machines can learn, but it’s the human insights and supervision that enable organizations to balance power and fairness.

Common data science terms your manager will expect you to know.

Data science is, among other things, a language, according to Robert Brunner, a professor in the School of Information Sciences at the University of Illinois. This concept might come as a shock to those who associate data science jobs with numbers alone.

Data scientists increasingly work across entire organizations, and communication skills are as important as technical ability. Data science is booming in every industry, as more people and companies are investing their time to better understand this constantly expanding field. The ability to communicate effectively is a key talent differentiator.

Whether you pursue a deeper knowledge of data science by learning a specialty, or simply want to gain a smart overview of the field, mastering the right terms will fast-track you to success on your educational and professional journey.

According to Vinod Bakthavachalam, a senior data scientist at Coursera, using the following data science terms accurately will help you stand out from the crowd:

1. Business Intelligence (BI). BI is the process of analyzing and reporting historical data to guide future decision-making. BI helps leaders make better strategic decisions moving forward by determining what happened in the past using data, like sales statistics and operational metrics.
2. Data Engineering. Data engineers build the infrastructure through which data is gathered, cleaned, stored and prepped for use by data scientists. Good engineers are invaluable, and building a data science team without them is a “cart before the horse” approach.
3. Decision Science. Under the umbrella of data science, decision scientists apply math and technology to solve business problems and add in behavioral science and design thinking (a process that aims to better understand the end user).
4. Artificial Intelligence (AI). AI computer systems can perform tasks that normally require human intelligence. This doesn’t necessarily mean replicating the human mind, but instead involves using human reasoning as a model to provide better services or create better products, such as speech recognition, decision-making and language translation.
5. Machine Learning. A subset of AI, machine learning refers to the process by which a system learns from inputted data by identifying patterns in that data, and then applying those patterns to new problems or requests. It allows data scientists to teach a computer to carry out tasks, rather than programming it to carry out each task step-by-step. It’s used, for example, to learn a consumer’s preferences and buying patterns to recommend products on Amazon or sift through resumes to identify the highest-potential job candidates based on key words and phrases.
6.  Supervised Learning. This is a specific type of machine learning that involves the data scientist acting as a guide to teach the desired conclusion to the algorithm. For instance, the computer learns to identify animals by being trained on a dataset of images that are properly labeled with each species and its characteristics.
7. Classification is an example of supervised learning in which an algorithm puts a new piece of data under a pre-existing category, based on a set of characteristics for which the category is already known. For example, it can be used to determine if a customer is likely to spend over $20 online, based on their similarity to other customers who have previously spent that amount.
8. Cross validation is a method to validate the stability, or accuracy, of your machine-learning model. Although there are several types of cross validation, the most basic one involves splitting your training set in two and training the algorithm on one subset before applying it the second subset. Because you know what output you should receive, you can assess a model’s validity.
9. Clustering is classification but without the supervised learning aspect. With clustering, the algorithm receives inputted data and finds similarities in the data itself by grouping data points together that are alike.
10. Deep Learning. A more advanced form of machine learning, deep learning refers to systems with multiple input/output layers, as opposed to shallow systems with one input/output layer. In deep learning, there are several rounds of data input/output required to assist computers to solve complex, real-world problems. A deep dive can be found here.
11. Linear Regression. Linear regression models the relationship between two variables by fitting a linear equation to the observed data. By doing so, you can predict an unknown variable based on its related known variable. A simple example is the relationship between an individual’s height and weight.
12. A/B Testing. Generally used in product development, A/B testing is a randomized experiment in which you test two variants to determine the best course of action. For example, Google famously tested various shades of blue to determine which shade earned the most clicks.
13. Hypothesis Testing. Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. It’s frequently used in clinical research.
14. Statistical Power. Statistical power is the probability of making the correct decision to reject the null hypothesis when the null hypothesis is false. In other words, it’s the likelihood a study will detect an effect when there is an effect to be detected. A high statistical power means a lower likelihood of concluding incorrectly that a variable has no effect.
15. Standard Error. Standard error is the measure of the statistical accuracy of an estimate. A larger sample size decreases the standard error.
16. Causal inference is a process that tests whether there is a relationship between cause and effect in a given situation—the goal of many data analyses in social and health sciences. They typically require not only good data and algorithms, but also subject-matter expertise.
17. Exploratory Data Analysis (EDA). EDA is often the first step when analyzing datasets. With EDA techniques, data scientists can summarize a dataset’s main characteristics and inform the development of more complex models or logical next steps.
18. Data Visualization. A key component of data science, data visualizations are the visual representations of text-based information to better detect and recognize patterns, trends and correlations. It helps people understand the significance of data by placing it in a visual context.
19. R. R is a programming language and software environment for statistical computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
20. Python is a programming language for general-purpose programming and is one language used to manipulate and store data. Many highly trafficked websites, such as YouTube, are created using Python.
21. SQL. Structured Query Language, or SQL, is another programming language that is used to perform tasks, such as updating or retrieving data for a database.
22. ETL. ETL is a type of data integration that refers to the three steps (extract, transform, load) used to blend data from multiple sources. It’s often deployed to build a data warehouse. An important aspect of this data warehousing is that it consolidates data from multiple sources and transforms it into a common, useful format. For example, ETL normalizes data from multiple business departments and processes to make it standardized and consistent.
23. GitHub. GitHub is a code-sharing and publishing service, as well as a community for developers. It provides access control and several collaboration features, such as bug tracking, feature requests, task management and wikis for every project. GitHub offers both private repositories and free accounts, which are commonly used to host open-source software projects.
24. Data Models define how datasets are connected to each other and how they are processed and stored inside a system. Data models show the structure of a database, including the relationships and constraints, which helps data scientists understand how the data can best be stored and manipulated.
25. Data Warehouse. A data warehouse is a repository where all the data collected by an organization is stored and used as a guide to make management decisions.

Mastering these terms is an excellent first step towards a durable data science career. Equally important is ensuring they’re understood throughout your organization so that data scientists can operate more efficiently and effectively with their non-data science partners. Like anything, this takes practice, but by putting these data science building blocks in place, you’ll be at a natural advantage when opportunities arise.

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Transformation is achieved through numbers, and nowhere is that understanding more important than in the exploding field of data science.

Data is to this century what fossil fuel was to the last: an accelerator of growth, disruption and change. Today’s world bristles with connected sensors in everything from wristwatches to wind turbines, which collect and transmit steady streams of data. The insights and knowledge pulled from those rapid, real-time flows of structured and unstructured data—which includes words, numbers, videos and photos—are creating new infrastructures, new businesses, new industries and new job descriptions. “Increasingly, you’ll see the merging of data science into the very patterns of our codes, creating new ways of solving really hard problems,” says Bob Lord, chief digital officer of IBM.

As more businesses look to extract value from data-driven technologies like artificial intelligence, the need for talented workers who can interpret the data is expected to rise across all industries. In fact, IBM predicts that the demand for data scientists will soar 28 percent by 2020. The responsibilities of those data scientists are shifting as well—becoming akin to the civil engineers of the 1940s and 1950s who designed bridges and roads—to create our nation’s latest form of infrastructure.

Technology will continue to accelerate transformation as cloud-based solutions allow easier and more secure access to big data sets, which in turn arm companies with the information they need to bring relevant products to market. As all of these changes come online, those who understand the underlying algorithms can have a huge effect on business and society. Here are some examples of those who are already making a difference.

Creating Customized Care

Our bodies are a complex biological roadmap, as unique as our fingerprints. “Our current one-size-fits-all healthcare mentality is often one-size-fits-none,” says Daniel Kraft, a physician and scientist who explores developing technologies in biomedicine and healthcare as chair of Exponential Medicine at Singularity University. “Right now, our healthcare is primarily based on intermittent data—the doctor checking your blood pressure and cholesterol levels in the office during a visit—and it’s very reactive. The move is to look at larger data sets to pick up disease early, then provide personalized care and therapy that maps to exactly what you need.”

Precision healthcare actually tailors treatments to a patient’s unique genetic code and can turn that data into meaningful actions. This data—gathered by connected devices ranging from blood pressure monitors and scales to smart watches and thermometers—will form the basis for personalized and proactive wellness plans that can even include recommended vitamins and exercise. Data scientists will store it on a cloud-based platform, where doctors can also upload the latest related information, such as lab results, to create a more complete health profile. This lays the groundwork for a revolutionary opportunity in personalized treatments—bespoke medicine, if you will—that will drive the next wave of medical breakthroughs.

Improving Urban Logistics

Every city relies on a complex web of data-driven systems and services to survive. And yet, myriad problems still plague the most advanced cities, including bad road quality. The data scientists in Kansas City, MO, however, are doing something about it. Their latest gambit: the development of “pothole prediction” technology.

Bob Bennett, the city’s chief innovation officer, says his teams have worked with Chicago-based Xaqt to create a system that uses various data streams to dynamically plan city operations. Predictor variables include the number of freeze-and-thaw cycles, traffic counts, bus routes and pavement conditions.

The hope is that work crews can focus on more preventative maintenance—stopping a pothole before it starts—rather than a full-scale street repair after a pothole has occurred. In other words, now, with the help of data scientists, your ride across town might be a lot less bumpy.

Up-Leveling Public Health

Data can be just as valuable as cash or equipment in slowing the spread of some of the world’s most vexing problems. When halting the spread of infectious disease, for example, the rapid scraping and analysis of digital data is literally a life-saving tool. Governments, companies, NGOs and specialists need to obtain good data quickly to know where the outbreaks are, how to target them and if the solutions are working.  

Nations work side by side with telecom companies worldwide, linking mobile networks with health services to create a powerful disease detection system. In Pakistan, for instance, health officials partnering with data scientists predicted local Dengue fever outbreaks weeks earlier using smartphone data than they previously would have through traditional means. The network used anonymized electronic surveys to create accurate predictive models that allowed for epidemic preparedness and containment of the virus.

Refining Education

Data scientists at the University of Maryland have begun to use predictive analytics to analyze student behavior, searching for undergraduates who are at risk of dropping out. University system officials say the practice—which may review anything from grades and financial aid information to how often students swipe their ID cards at the library or the dining hall—could help educators assist struggling undergrads. It could also help them identify roadblocks, such as a single difficult class or a combination of pressures that hit at the same time, that lead students to drop out.

The enormous value of data science is becoming clearer every day and so are the opportunities to directly impact people, companies and society. For those who love solving problems or transforming the ordinary into the extraordinary, data science is for you.