Data Analyst and Data Scientist: How to Make a Career in Big Data
The volume of data in the business world today and the need to use it with insight have led to two specialized occupations: the data analyst and the data scientist. Although these roles overlap in some areas, they carry different sets of responsibilities and functions with regard to big data. Essentially, a data analyst finds the big questions raised by the data, and a data scientist formulates the answers.
Due to the specialized skills required, the job market currently has a shortage of data analyst and data scientist professionals. In fact, because of the number of openings, future demand, and average salary, Glassdoor named data scientist the third best U.S. job in 2020, continuing a four-year reign at the top. Data analyst is still in Glassdoor’s top 50 jobs, at number 44. Employment of data scientists and all mathematical science occupations—including data analysts—is projected to grow more than 30% through most of this decade, according to the U.S. Bureau of Labor Statistics.
What Is Big Data?
The sheer growth in data due to the growth of the internet and new technology—called big data—has created the need for data scientists and data analysts. This big data consists of larger, more complex data sets, especially from new data sources.
Data analysts and data scientists both tackle this large amount of data and tease useful insights out of numbers.
- Data analysts examine raw data and reach conclusions about that data.
- Data scientists take broad data sets and discover meaningful questions, rather than answers. Both skills are useful across many disciplines and industries.
Although data scientists and data analysts fulfill separate roles, the positions often work in tandem. In fact, through additional education and experience, a data analyst can be promoted to a data scientist role.
Within this framework, data analysts and data scientists are responsible for or have a hand in:
- Adjusting customer-based algorithm models to arrive at more specific findings
- Assisting with company financial reporting, including hitting key performance indicators (KPIs)
- Conducting consumer data research and analyzing its results
- Generating and maintaining analysis dashboards and creating other data visualization media to illustrate metrics, findings, and goals
- Providing regular quantitative analyses to shape company processes and decisions
- Utilizing programming languages and software, such as SQL, Tableau, and spreadsheets, to comb through and draw out actionable insights and apply algorithms to large swaths of data
What’s the Difference Between Data Analysts and Data Scientists?
Although both professions dig deeply into massive amounts of data, what they do with it marks the difference between data analysts and data scientists.
What Does a Data Analyst Do?
Data analysts, as the job title implies, spend a significant amount of time examining complex datasets and databases. In the process, they look for patterns and trends to compile data points and create charts and other visualization methods to illustrate what their findings indicate. Analysts typically love numbers and have a background in statistics, and programming skills are of growing importance in today’s workplace. Because practically all fields need data analysts, professionals also ought to be familiar with industry-specific data points and trends.
What Does a Data Scientist Do?
A data scientist builds upon a data analyst’s foundation. The patterns and trends curated by the data analyst influence the data scientist’s predictions or contribute to a solution, both of which help build future strategies. Data scientists often seek ways to improve data curation and analysis. These professionals spend time creating processes for data modeling and develop unique algorithms for custom, fine-tuned analysis. Because their job responsibilities run in a technical direction, having a background in computer science can be a benefit.
Here are other ways data analysts and data scientists approach data differently:
|Data Analyst||Data Scientist|
|Looks at data with an eye toward more involvement with customers, particularly tracking what they want or look for in a business||Looks at trends and organizes data to offer a solution or provide input toward new products and services meant to retain and keep customers interested|
|Spends more time with research and mathematics||Spends more time creating data visualizations, often beginning with dashboards and compiling data into presentations|
|Often examines one or a handful of sources; for instance, all data related to company sales||Often cultivates multiple, disparate sources and requires synthesis of this information|
|Spends more time using SQL and spreadsheets||Spends more time on machine learning, using predictive modeling and building statistical models|
What Key Skills Are Needed for Data Analyst and Data Scientist Jobs?
There are a number of specialized skill sets that overlap both data analytics and data science jobs. The common skills include:
- A background involving mathematical modeling, database management, programming, and predictive analysis
- A science, technology, engineering, or math (STEM) undergraduate degree, although more companies are now requiring advanced degrees
- Advanced Excel skills
- An understanding of data mining, machine learning, and the latest data analysis technologies
- Experience using Python, R, and SQL/CQL
Data analysts and data scientists should also:
- Be well-versed in tools such as Google Analytics, Google Ads, Coremetrics, and Facebook Insights, as well as Hadoop, Hive, and Map/Reduce
- Have a solid foundation in statistical concepts
- Have an undergraduate degree or experience in computer science
- Have experience and a portfolio of presentations created through Periscope, Google Dashboards, ggplot, Business Objects, and similar tools
- Know how to create data architectures, develop statistical models, and manipulate data sets
- Know standard machine learning techniques, like artificial neural networks, clustering, and decision tree learning
- Understand linear model and regression, boosting, trees, text mining, and social network analysis
Soft skills are also highly recommended in these data positions. It is recommended that candidates hone their written and verbal communication, creativity, problem-solving, and teamwork abilities.
What Skills Are Needed to Build a Data Analytics Career?
Skilled data analysts are highly sought-after in the business world. The demand is strong, and the supply is limited, so data analysts can command substantial salaries and excellent perks, even at the entry level.
Data analyst jobs are available in a wide variety of companies and industries. Any company that compiles data needs analysts to make sense of it. Some of the top jobs in data analysis involve using data to assess risks, decide on capital allocations, make investment decisions, or target customers.
Here are some of the requirements and roles typically expected of someone making a career of data analysis:
Requirements for a Data Analyst
- Accomplished at using statistics for analyzing datasets
- Adept at queries and reporting
- Competent at problem-solving and being a team player
- Experienced with collecting, analyzing, and disseminating information
- Familiar with data mining
- Knowledgeable in coding, databases, and reporting technologies
- Proficient with data models and designs for database development
- Skilled at communication
- Versed at accuracy and attention to detail
Role of a Data Analyst
- Clean and filter data to isolate problems and troubleshoot them
- Develop, implement, and maintain databases for primary and secondary data
- Perform analysis on data sets and get results
- Present results with the help of data visualization tools
- Prioritize business issues and work on them
- Seek patterns and trends in data
- Work with the management
What Skills Are Needed to Build a Data Science Career?
Data scientists also begin with vast amounts of data, but they usually have more responsibilities that often entail coding and mathematical modeling. Professionals often have advanced degrees or certifications that support their expertise in programming languages and machine learning.
Data scientists must discover problems that might occur in the industry where they work and find a solution for them. Once solved, these solutions should show a positive impact on the industry. These professionals help companies make better decisions and keep an eye on the overall performance.
Here are some of the requirements and roles typically expected of someone making a career of data science:
Requirements for a Data Scientist
- Accomplished with data frameworks, such as Hadoop
- Adept at critical thinking, communication skills, and team dynamics
- Experienced with data mining and data wrangling
- Familiar with business needs
- Knowledge of programming languages, such as Matlab, Python, R, Scala, etc.
- Proficient with SQL
- Skilled at data analysis and visualization tools
- Well-versed in machine learning
Role of a Data Scientist
- Analyze data for trends
- Build models for prediction and use ML algorithms for drawing insights
- Clean and validate data
- Identify data sources and collect them
- Present the result with the data visualization tools
- Process all data, both structured and unstructured
- Propose solutions and strategies
What Industries Need Data Analysts and Data Scientists?
The use of big data analytics enables companies to implement high-performance data mining, text mining, predictive analytics and other methods to drive information to make the best business decisions. With the expertise of data scientists and data analysts, big data can be used to discover the information most relevant to businesses and make informed critical decisions.
One of the most prominent users of big data now is retail—specifically, e-commerce. As more consumers have transitioned to online purchasing, data science has come more into play.
According to a BARC study, all companies that have started harvesting and analyzing big data can see these benefits:
- 69% better strategic decisions
- 54% improved control of operational processes
- 52% better understanding of customers
- 47% cost reductions
Today, data analysts and data scientists are helping retailers focus their efforts for maximum impact. There are many ways big data can be used in retail:
- Analyzing shopping patterns—examining customer preferences, spikes in demands, and timing of shopping
- Easing online payments—managing one centralized payment platform that is convenient and secure
- Enhancing customer service—identifying major issues, resolving them, and pleasing customers
- Making strategic decisions—planning long-term decisions such as store placement and short-term decisions such as product merchandising and display
- Optimizing prices—using innovations such as real-time pricing
- Personalizing customer experience—factoring in average bill value, frequency of visits, purchase history, and search history
- Predicting future operations plans—managing forecasting, inventory, supply chain, and sales strategies
Retail isn’t the only area making use of big data with the help of data analysis and data science. Industries with big data jobs include:
- Business—The use of data can improve efficiency, drive development of company processes, track and manage inventory, reduce the number of production errors, and cultivate customer loyalty.
- Farming and food—The application of data has further revolutionized how food is grown, stored, and delivered, helping reduce the amount of food wasted and streamlining supply chain operations in the process.
- Finance—Data can be used to help oversee and manage accounts, predict trends, and keep track of transactions.
- Government—The use of data can help identify trends that inform decisions and influence policies.
- Media, marketing, and telecommunications—Data harvested from social media has changed online advertising, created a channel for identifying customer satisfaction, and added more variables for updating website or service experiences.
- Medicine—Data is changing the way diagnoses are made and how accurate they are.
What Careers Are Available in Big Data?
Depending upon their industry and responsibilities, data analysts and scientists hold a spectrum of big data analytics job titles, including:
Outside of finance, business analysts examine company data across multiple departments to make recommendations and predictions and impact company strategy.
Business Intelligence Developer
Professionals often have a top-level role in which they create company strategies, conduct research to support their findings, and use that research to orchestrate the company’s future structure.
Professionals analyze, manipulate, and transform large datasets to gain insights and make business predictions.
Data architects ensure that the systems built for analyzing and examining information are operating and performing optimally and delivering the results other data analysis professionals need to draw effective conclusions.
Utilizing a range of programming skills and analysis tools, data engineers often clean the available data for processing, gathering, and storing information.
Data Mining Specialist
Using a mix of research, data analysis, and modeling techniques, professionals assess data to identify patterns and relationships to predict future industry trends and influence a company’s direction.
Often focusing on unstructured data, data scientists analyze the wealth and variety of information a company has available, creating their own formulas or conducting investigations to find unique, actionable insights that then affect key business decisions.
Machine Learning Specialist
Because algorithms can always be refined, machine learning specialists continue to find ways to improve existing technologies, particularly in relation to the rate of Big Data’s growth.
Especially where campaigns and digital products are concerned, marketing analysts routinely examine and look for trends in sales and marketing data, using a mix of modern tools like Google Analytics and traditional statistical techniques.
Focusing on improving internal processes, operations analysts seek to streamline how a company runs by examining and visualizing data regarding systems, products, and services and pinpointing areas of improvement.
Closely involved in financial analysis and risk prediction, quantitative analysts use statistical methodology and machine learning to answer business questions, solve financial problems, and predict markets or industry trends.
A more mathematical role, statisticians compile, interpret, and analyze statistical data and formulas, often with the intent to shape business decisions or contribute to research.
Professionals are responsible for identifying organizational problems and conducting the research to find a solution, utilizing programming, statistical techniques, and data analysis to examine information and draw conclusions.
Start Your Data Analyst or Data Scientist Career
If you’ve been in an analytical or programming role and are seeking to take the next step, get on the path to a career as a data analyst or data scientist with the Wake Forest University School of Business’s Online Master of Science in Business Analytics degree. To learn more about this program, request additional information today.