Guide to a Career in Analytics
As computers get more powerful and the predictive and analytic power of artificial intelligence (AI) and machine learning (ML) is being harnessed, analytics is one way to a lucrative, exciting, and meaningful career. Analytics is a step forward in data collection, quantitative and statistical modeling, and the many ways in which these insights can be applied.
Read on to learn more about analytics, its industry applications, and different types of analytics careers. You’ll also find out more about the best analytics tools, how to get into the analytics field, and finding the right data analytics job for you.
What is Analytics?
Analytics refers to the use of data to answer questions, drive decision-making, and predict unknown outcomes. Thanks to improved computer processing power, large datasets can be queried by use of statistics, computer programming, and operations research to quantify performance to drive decision-making to achieve better organizational outcomes. Analytics can be used to describe, predict, and improve business performance.
The ability of analytics to describe, power decision-making, and predict trends have been amplified in the AI/ML age. With advances in AI and ML, computers can learn patterns from data which can help make organizational decisions or even identify what factors are important in the decision-making process.
Companies today treat analytics as a strategic tool, and analytics is essential to many corporate and business roles. Analytics can be used to estimate one’s credit risk, prevent fraud, deliver products and services more efficiently, keep companies safe from hackers, or help retain clientele by investigating shopper behavior.
Analytics vs. Business Intelligence
Some experts think of business intelligence as a subcategory of data analytics, in which analysts work with smaller datasets and are likely to use standard software kits and toolboxes to analyze and visualize data. A person in the field of business intelligence may wish to improve employees’ individual performance using data analytics, for example. However, the field of business intelligence is rapidly changing as analytic technologies rapidly change and improve. Business Intelligence is, increasingly, being replaced with analytics.
Different Types of Analytics
Three main types of analytics are descriptive, predictive, and prescriptive analytics.
Descriptive analytics is the simplest form of data analytics. It refers primarily to data mining and business intelligence applications of analytics. Descriptive analytics is the practice of processing large quantities of data into smaller, more useful insights. Nearly all organizations use descriptive analytics to analyze both real-time and historical data to gain insight into how to approach future challenges. An example of descriptive analytics are social analytics, which allow organizations to see how many people have read, liked, and shared social media posts. For instance, descriptive analytics can be used to determine whether a social media campaign resulted in an increase in visibility of a company’s social media channels.
Predictive analytics is used to forecast trends and future events. Predictive analytics relies on probability estimation to, as its name implies, predict what is likely to occur in the future. Predictive modeling can be used to answer questions that cannot be answered by business intelligence. Some types of predictive modeling include root cause analysis to determine why an incident occurred; data mining to identify patterns in data; forecasting to reveal trends; data simulations to predict future events; and pattern identification and alerts to notify organizations about corrective actions that should be taken. Retailers can use predictive analytics to forecast customer trends such as buying behavior to set prices. The credit score is another example of predictive analytics which helps banks decide whether a customer would be likely to pay their credit card bills in a timely manner.
Prescriptive analytics is, like predictive analytics, an advanced form of data analysis that can be used to improve organizational and corporate processes. Prescriptive analytics relies on two main concepts: optimization and simulation. Optimizing corporate behaviors using prescriptive analytics can help improve the operations of a business. Data analytics used prescriptively can help companies understand how to improve their outcomes, identifying uncertainty and reducing it by using data analytics to help make more informed decisions. Simulations can be used to help pinpoint and overcome uncertainty using data analysis. Another way that prescriptive analytics can be used is in the health care sector. Prescriptive analysis has been used in the medical field to drive medical decision-making that can reduce rates of hospital readmission.
Industry Applications of Analytics
While companies rely on descriptive analytics, prescriptive and predictive analytics are less widely used. Other types of analytic applications in industry are enterprise decision management, retail and supply chain analytics, marketing optimization, web analytics, speech analytics, price and promotion modeling, credit risk analysis, and cybersecurity and fraud detection and monitoring.
A 2019 survey conducted by MIT and SAS revealed that 9% use insights gleaned from ML and AI. Companies who have dedicated data and analytics staff are more likely to have advanced analytics such as prescriptive and predictive methods. 59% of companies surveyed stated that their company leaders frequently look to analytics to support decision-making, while approximately 33% regularly assesses the workforce’s data literacy and/or make training available to employees in data science and analytics. About 38% of businesses surveyed train and/or place data analytics specialists in operational areas.
The industry applications of analytics are immeasurable. Every day, especially with the advent of the Internet of Things, devices capture millions of real-time, continuous data points. The main problem that this creates is that there is too much data to make any meaningful conclusions. That’s why analytics is so powerful. The IT industry can use data analytics to help predict whether someone gaining access to a website is a regular user or a malicious hacker. An e-commerce website can use analytics to set prices, run advertising campaigns, and help improve the overall buying process. Utilities companies can use data analytics to set prices, predict when outages might happen to develop contingency plans, and even help users find answers to their questions via AI- and ML-powered chatbots. As technology continues to improve, the applications of analytics will become even more common.
Key Analytics Skills
Must-have skills for a career in data analytics include:
- Microsoft Excel is not just used for spreadsheets – thanks to Visual Basic, the popular productivity software can also be used to run powerful scripts (called Macros).
- Computer programming is an indispensable skill in the analytics field. Especially for more advanced applications of analytics, it is essential that professionals in this field can not only write computer programs, but that they are familiar with several programming languages. We discuss some helpful programming languages in the next section. Some of the most commonly used computer programming languages in analytics are Excel/VBA, SQL, Python, and R.
- Statistical Programming can be more useful than Excel or SQL as they can be used to create customized analyses in which a company might be interested. Python and R are two powerful computational platforms that can enable statistical programming.
- Critical Thinking Skills are essential for people who are interested in data analytics as the field requires the ability to create and test hypotheses using Big Data. A scientific mindset and problem-solving attitude is a must-have when working in the analytics field.
- Data Visualization is a large part of analytics as well. Data analysts must not only be proficient in the number-crunching aspect, but they must also be knowledgeable regarding how to present data, e.g., to higher-ups who are looking for decision-making insights from the data but may not have the technical knowhow to interpret complicated mathematical outputs from a program.
- Communication and Presentation Skills are a must for a data analytics professional as an important aspect of the job is translating complex ideas gleaned from Big Data to a general audience who may have very little science, technology, engineering, or mathematics background.
- Artificial Intelligence / Machine Learning is a rapidly evolving area of data analytics which can be used in predictive and prescriptive analytics. AI/ML skills give data analytics professionals an upper hand as ML, in particular, is used in the implementation of a number of major retail websites, such as Amazon.
- Data Mining and Scraping is another important skill relate to the quantitative analysis of large datasets. Data mining and scraping can be used to collect unstructured data from various real-world sources which can be processed and analyzed to gain decisionmaking insights. An important skill in the data mining and scraping realm relates to the use of ML and AI techniques to find inherent structure in such unstructured data in order to refine and prepare unstructured data (e.g., mined or scraped data) for processing.
Best Computer Programming Languages for Analytics
Professionals in the field of analytics use a variety of computer programming languages to help them work with data. Computer programming is used to scrape and mine data, to sort and analyze data for preprocessing, and of course, is also used to conduct the analytics computations themselves. The increasing power of computer processors means that programming will continue to be an important part of data science and analytics for decades to come. Commonly-used programming languages in this industry include:
- Structured Query Language or SQLis a commonly used relational database language. Relational databases are a special type of database that stores data but also keeps track of how data points are related to one another. SQL is the norm for dealing with relational databases, and has been around since the 1970s. SQL is used to read, access, and change data. Obtaining foundational SQL knowledge is a great starting point for people who wish to get involved in the analytics field.
- Python is an open source programming language that is useful for integrating data analysis and web apps or statistical code. Python’s advanced library can be used to implement advanced machine learning algorithms, e.g., in prescriptive or predictive analytics. Companies such as Google and Cognizant use Python in their analytics solutions. Python is also easy to learn, which makes it a useful skill for people interested in an analytics career.
- R is a statistical programming environment that is available as free, open source software. R features useful packages for Big Data analytics. R was created for statistical analysis, and was developed by mathematicians and statisticians, so people with less computing experience can get started easily in R. Accenture, Citibank, Google, and Facebook use R in their data analytics toolkits.
- Julia is a fast, high-performing programming languages that is available as a free and open source platform. Julia enables the statistical compilation of code and deployment on a webserver, among other functionalities. Programmers can also write powerful macros with Julia. Designed for parallel computing, Julia can work with hardware accelerators which are used in data analytics to speed up the processing of large datasets. Julia’s applications include machine learning, numerical and scientific computing, data science, and visualization. Multidimensional datasets can be loaded quickly, preprocessed in parallel, and saved easily in a convenient format.
- Java is one of the oldest computer languages used for the development of enterprise software. Though it can be complicated to learn compared to Python or R, the Java Virtual Machine is a fantastic platform which allows programmers to write code quickly and efficiently for multiple platforms. Java offers many tools and libraries for data analytics. Java is also fast – which is essential for data science applications which require working through large datasets efficiently. Second to SQL in the analytics sphere, people with a working knowledge of Java are well-positioned to find a job in analytics.
- Scala is a programming language with powerful data science applications. Apache Spark is a software tool commonly used in analytics which relies on the Scala programming language. Scala is an extension of Java that works seamlessly with Java and even runs on Java Virtual Machines. Compared to Java, Scala is much more geared toward functional programming, and coding is much more efficient and concise in Scala than Java. Companies such as Twitter and LinkedIn are using Scala for their platforms, which makes learning Scala a way to become more marketable to big tech companies.
Best Analytics Software Tools
There are many different analytics tools out there for those who are interested in a career in the field. Here are a few analytics tools that you may need in a career in this exciting field:
- Microsoft Excel’s powerful Macros and Visual Basic functionality can help automate aspects of your data analysis, but it is not as useful for large-scale data mining.
- SASis a commercially available statistical program that can help you crunch various statistical calculations. SAS has many new modules relating to analytics, including SAS Analytics for IoT and SAS Analytics Pro. SAS offers an advanced AI and analytics platform that is one of the most widely used in the world, including by companies such as Lockheed Martin and Volvo. Python can be used in conjunction with SAS.
- Tableauis commercial software that can be used to analyze Big Data.
- Apache Spark is a unified analytics engine for the processing of large datasets. Apache Storm offers analytic functionality for large, continuous datasets. Apache’s products are used by companies such as Groupon, Facebook, Pinterest, Uber, and Netflix. Apache products use the Scala platform which has high interoperability with Java.
- MATLAB is a commercially available software with many powerful analytics capabilities. The Matlab programming environment also allows users to work with different programming languages such as C, C++, Java, and Python. MATLAB can simplify data science by making it easier to preprocess data, deploy models, and use machine learning algorithms to glean novel insights.
- Octaveis an open source, scientific programming language which is compatible with MATLAB and can be useful for data analytics.
How to get into a career in analytics?
A career in analytics can begin with a STEM or computer science degree, or one of many specialized programs geared toward data science. If you’re interested in analytics, you will want to look for careers as a data scientist, data analyst, or business analyst.
Data scientists use statistics, business knowledge, and computer programming skills to obtain insights from Big Data. Data scientists are involved in data collection, preparation, data analysis, and the development of models to explain patterns and trends in data.
Data analysts are another type of career available in the big data analytics industry. Data analysts are responsible for interpreting trends and insights revealed by the very large datasets with which they work. Data analyst positions typically require a bachelor’s degree in information technology, computer science, or statistics.
Finally, business analysts are tasked with understanding and keeping up with the evolving needs of a business, and providing technology-driven solutions to improve systems and processes. Business analysts can be thought of as a liaison between the business and IT departments of an organization.
Essential programming skills for a career in analytics include Python, R, and SQL. There are a variety of free online courses in computer programming that can teach you the essentials of these and other programming languages and toolkits which are useful for analytics professionals.
Searching for an Analytics Job
Looking for an analytics job doesn’t have to be difficult. Discover Data Science has compiled a helpful guide for people who are interested in finding a job as a data scientist.
First, make sure you acquaint yourself with the different roles available – including data scientist, data analyst, and business analyst, as discussed in the previous section. Then, think about your qualifications and whether you will need to obtain additional training in data science or a STEM-related discipline, such as computer programming. Typically, people who have earned a STEM degree in college are well-suited for a career in data science. If you have taken college classes in advanced statistics or programming, you likely do not need additional training as you have the skills needed for a data science job. If not, you may wish to learn about essential data science tools, such as R, Python, and SQL. Finally, consider the industries to which you wish to apply. Data science jobs are available in a variety of industries, such as finance, e-commerce, tech, biotech, utilities, and more.
Informational interviews can be a helpful tool in the job search process. In your research, identify a few people who have careers in data science that would be willing to speak with you regarding their day-to-day roles as a data analyst. Ask them about how they got their job and data science in general, what they do in a typical day, and other questions that you may have. Informational interviews’ purpose is to allow you to learn more about data science positions, and can also be a helpful networking tool.
Finally, remember to keep up-to-date on the happenings of the field. Data science and analytics is a field that evolves rapidly, and even if you had a career in data science or a related field previously, the trends in data science and analytics even three or five years ago may not be the same trends in the field today. Stay updated on trends in data science by learning new skills, reading about data science applications, and even networking in the field of data science to learn about what the biggest technological challenges of analytics are in the present moment.
Conclusions
Data Analytics careers are becoming more commonplace, especially with the advent of big data and improved ways to analyze it. A background in STEM and particularly, computer science is needed. Familiarity with a variety of programming languages such as SQL, Python, R, Scala, and others will give candidates an advantage in the job search process.
A college degree in computer science or a related field in STEM can be a useful start in the analytics industry. However, there are many ways beyond college education to gain expertise in data analytics, such as online courses, degree programs in data analytics, and internships. Data science and analytics will continue to expand as computer technology improves, which makes this area of the high-tech sector an exciting and potentially lucrative industry. Analytics jobs are rapidly improving multiple sectors, including IT, e-commerce, etc. The U.S. Bureau of Labor Statistics predicts that demand for mathematical science occupations, such as data analytics careers, will increase by 7.4% bet
