In today’s digital world at least 1.7 MB of data is created per second. Data analysts transform an estimated 1.145 trillion MB of newly generated data every day into meaningful insights.
What do data analysts do with this data? Data analysts are modern storytellers who use mass amounts of data available to tell us what has happened, what’s currently happening, and what’s likely to happen in the future. This data encompasses everything from purchase behaviors and product preferences to healthcare statistics and population trends.
Data insights are helpful in almost every area of our modern lives. Data analysts tend to possess a combination of abilities, including technical skills, effective communication, and leadership qualities. As a result, careers in data analysis offer professionals diversified job opportunities, high earning potential, and exciting leadership roles with the power to shape the future.
What is Data Analytics?
Data analytics is the science of analyzing raw data to organize information, identify patterns, make correlations, and draw conclusions about the findings. Machine learning and artificial intelligence enable scientists to run data analytics at scale and quickly comb through mass archives of Big Data. Advanced technology and automation are vital, but organizations still need educated data analysts to organize and explain data for human consumption.
The democratization of data is underway, which means that data analysis is becoming more accessible and less technical so that professionals and consumers can better understand how data is used and why it matters. Organizations need more data analysts, and as a result, careers in data analysis continue to spike and the job outlook for data analysts and data scientists in 2022 is booming.
Different Types of Data Analytics
There are six different types of data analytics used in the field of data science and Big Data, each with a distinct goal and process. The six primary types of data analysis include:
- Descriptive Analysis: Used to describe and summarize a set of data in an easy to interpret way. Descriptive analytics is generally the first form of analysis applied and involves general summaries and measurements.
- Exploratory Analysis (EDA): An examination or exploration of the relationships between data. Exploratory analytics begin the process of hypothesis-building by finding patterns in data sets.
- Inferential Analysis: Aims to draw conclusions about large populations based on smaller data sets using accurate samples that avoid generalizations.
- Predictive Analysis: Employs historical data to predict future behavior, actions, or events.
- Causal Analysis: Establish cause-and-effect relationships through the examination of correlated data.
- Mechanistic Analysis: A precise method to isolate exact changes that lead to alterations in other variables.
Data analysts engage with several, or sometimes all, of the above analysis methods to perform their day-to-day job responsibilities.
What Do Data Analysts Do?
Data analysts help organizations make sense of information by organizing, visualizing, and explaining data sets. The role of a data analyst depends on the industry and the type of data analysis they use, but in general, data analysts have five primary responsibilities:
- Gather data: The first job of a data analyst is to find data or source new data, from surveys and company websites to third-party data providers. In many cases, a Data Scientist will provide analysts with workable data sets.
- Clean data: A data analyst will then organize data and sift out irrelevant data, otherwise known as “cleaning” data. This involves improving the data quality and removing errors while inputting it into a programming language or spreadsheet.
- Model data: Data analysts then categorize data, and organize the clean data into a well-designed format.
- Interpret data: Data analysts must then examine the structured data and draw insights, including predictive or prescriptive recommendations.
- Present data: Finally, analysts must use data visualization to map data in visually appealing graphs, charts, or other compositions and communicate their findings to teams, leaders, and stakeholders.
Day-to-Day Responsibilities of a Data Analyst
What do data analysts do in a typical workday? As mentioned, the job responsibilities of a data analyst vary, but the following list reflects some of the typical duties a data analyst may conduct on any given day:
- Producing and maintaining internal and external-facing reports
- Identifying patterns in data and tracking trends over time
- Collaborating with researchers, data scientists, and business leaders
- Using advanced software, tools, and programming languages to create a data infrastructure
- Visualizing data in graphs, charts, and other image-based configurations
- Applying business intelligence to translate data into tangible business insights
Standard Tools Used by a Data Analyst
Data analysts will interact with numerous software platforms and technologies that streamline their workflow and automate tasks. Many tools simply allow analysts to gather and display data efficiently. The most common tools used by a data analyst include:
- Microsoft Excel
- Google Sheets
- SQL
- Tableau
- R or Python
- SAS
- Microsoft Power BI
- Jupyter Notebooks
Data Analysts Vs. Data Scientists
Data analysts and data scientists work in similar fields but have different job responsibilities. The primary differences between data analysts and data scientists are categorized by how they use data.
A Data Analyst analyzes existing data to identify trends, explain patterns, and tell a story about the insights. Data analysts will often use visual representations to present their findings to internal and external teams and turn numbers into actionable steps to improve their business strategy.
A Data Scientist is more concerned with technical data skills, like hands-on machine learning, advanced programming, data modeling, and algorithms. Data scientists are experts in coding and mathematics. Many data scientists start their careers working as data analysts and transition to become scientists as they accumulate more technical knowledge.
Learn more and read: What’s the Difference Between Data Science and Applied Statistics?
5 Reasons to Become a Data Analyst in 2022
The top data analytics predictions for 2022 anticipate a rise in predictive analytics, organizations increasing budgets for analytics, continuous growth in the Big Data market, and new Cloud solutions for analysts. However, perhaps the most important trend is that despite the perpetual advancements in AI, the need for human capital remains crucial. Careers in data analysis are skyrocketing and the reasons to become a data analyst in 2022 continue to multiply.
1. Learn in-demand skills
Those with careers in data analysis learn relevant in-demand skills that span industries and add value to every digital-enabled organization. The top technical skills needed by data analysts include math, programming, analytical tools (SQL, Spark, and Hoop), machine learning, data visualization, data wrangling, and business acumen.
Given that the most vital technical skill for data analysts is math, many professionals pursuing careers in data analysis start by obtaining a Master of Science in Applied Statistics. An advanced degree in statistics teaches students everything from programming and advanced technology to communication and leadership skills. Data analysts are life-long learners and often transition into careers as Senior Data Analysts, Data Scientists, or Consultants.
2. Earn a higher salary
As of 2021, the average data analyst salary in the United States is $67,405 per year. This means that data analysts earn approximately 26% more than the national average salary in the United States ($53,490 per year). Experienced data analysts can earn up to $87,000 per year and transition into higher-paying jobs as Senior Data Analysts, Data Scientists, or Analytics Managers.
When it comes to calculating potential salary, location matters; data analysts located in San Francisco, California, earn up to 32% more than the country’s average. Evidently, a rewarding reason to pursue a career in data analytics is to boost your lifetime earning potential and expand job opportunities.
3. Expect a positive job outlook
In addition to earning a higher than average salary, data analysts can look forward to copious career opportunities from working in a prosperous industry. The data analytics market is predicted to hit $132.90 Billion USD by 2026, and studies show that the COVID-19 pandemic accelerated the adoption of data analytics solutions and services. As a result, the job outlook for data analysts and data scientists is positive.
The World Economic Forum listed data analysts as number two in growing jobs in the U.S. Additionally, The Bureau of Labor Statistics (BLS) reports that market research analysts are expected to grow by 22 percent, and mathematicians and statisticians by 33 percent from 2020 through 2030.
4. Become a leader
Collaboration is an essential part of being a successful data analyst. Data analysts must understand how to work well with others and communicate complex findings to team members, business leads, and stakeholders to turn insights into action. Data analysts with job experience, professional certifications, and advanced degrees could be promoted to the role of Data Manager.
Rather than analyzing data, a Data Manager is responsible for developing standards of operation, overseeing the adoption of new technologies, managing analysts, and assessing performance. As a manager, you would have an opportunity to enter high-level leadership roles in an organization, earn a higher salary, and pioneer organizational transformation from the inside out. Data Managers can earn an average salary of $83,978 per year, depending on factors like location, education, and industry.
5. Shape the future
Data analysts can transform organizations and solve critical real-world problems. During the COVID-19 pandemic, data analytics was crucial for organizations. Leaders embraced data analytics and AI to aid in change management and these capabilities ushered in between $9.5 trillion and $15.4 trillion in annual economic value. McKinsey & Company says,
“In these uncharted waters, where the tides continue to shift, it’s not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool. Analytics supports numerous urgent tasks facing businesses today: forecasting demand, identifying potential supply-chain disruptions, targeting support services to at-risk workers, and determining the effectiveness of crisis intervention strategies.”
Statistics support better decision-making because they allow leaders to use objective data rather than subjective assumptions. Data science and data analytics are changing the world as we know it. Data analysts are at the forefront of this shift, as they help the world respond to a global pandemic, track climate change, promote ethical data usage, forecast threatening events, and save human lives.
How a Master’s Degree in Statistics Can Lead to a Career in Data Analytics
How do you launch a successful career in data analytics? One of the greatest entryways to becoming a data analyst is through a Master’s Degree in Applied Statistics. The coursework required to earn a Master’s in Applied Statistics delivers insight into a broad spectrum of statistical science and methods that can be used to make predictions, devise experiments, and draw accurate conclusions. As a student, you’ll work with industry-standard statistical and data analysis software and conduct a thorough analysis of real datasets to solve real-world problems.
Learn more and read: What Can You Do with a Master's in Applied Statistics?
Benefits of Obtaining an MS in Statistics with Michigan Technological University
Michigan Tech is a recognized and accredited school, ranked #33 in Top Public American Universities by Niche. At Michigan Tech, a graduate degree in statistics allows you to set yourself apart in a competitive job market by building specialized, quantitative skills to meet the growing demand for statistics and data professionals. At Michigan Tech, an MS in Statistics offers students the following program benefits:
- 100 percent online; ideal for busy professionals
- Three start dates per year
- Accelerated format; 7-week courses
- 30 credit hours; 10 courses
- Earn a Graduate Certificate in Applied Statistics along the way
- National Graduate Service Reduced Tuition Rate available to Military, AmeriCorps and PeaceCorps personnel
Earn your MS in Applied Statistics online with Michigan Tech University today.