Data Analyst vs. Data Scientist, which one is better, is one of hottest topics of discussion among tech bloggers today. Both are growing in importance and offer remarkable opportunities for the millennials.
“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.”
– Angela Ahrendts, Senior VP of Retail, Apple
Well, Angela Ahrendts was right, and both Data Scientists and Data Analysts are proving her right.Glassdoor’s 50 best jobs In America for 2018 include Data Scientist, Analytics Manager, Database Administrator, Data Engineer, Data Analyst, and Business Intelligence Developer.
Both Data Scientist and Data Analyst roles are very much in demand. In our discussion on Data Analyst vs. Data Scientist, I will highlight upon the individual aspects of each role, career prospects, and how they are related to each other.
Data Analysts and Data Scientists are two of the hottest jobs with impressive salaries to match. But while Data Analysts and Data Scientists share some similarities, there are key differences between the careers that need to be considered before selecting a path to follow.
While a Data Scientist’s work is to mine and analyze data from a range of sources, including customer transactions, click streams, sensors, social media, log files and GPS plots, a Data Analyst job is to analyze the data and use it to help companies make better business decisions.
A Data Analyst is responsible for unlocking valuable and predictive insights that will influence business decisions and spur a competitive advantage. In our discussion, I will compare Data Analyst and Data Scientist career prospects and salary figures.
Data analysis involves examining data to find useful information for achieving organizational goals. Analytics sorts data into things that organizations may or may not be aware of and can be used to measure events in the past, present, or future. Data Analytics often moves data from insights to impact by connecting trends and patterns with the company’s true goals and tends to be slightly more business and strategy focused.
The Data Scientist and Data Analyst are different. The Data Scientist starts by asking the right questions, while Data Analyst starts by mining the data. The Data Scientist needs substantive expertise and non-technical skills whereas a Data Analyst should have soft skills like intellectual curiosity or analytical skills.
Data Analyst vs. Data Scientist According to Definition
A Data Scientist is a professional who analyzes data from a business point of view and is in charge of making predictions to help businesses take accurate decisions. Data Scientists come with a solid foundation of computer applications, modeling, statistics, and math.
A Data Analyst is responsible for collecting, organizing data and obtaining statistical information out of accumulated data. Data Analysts are also responsible to present the data in the form of charts, graphs, and tables and use the same to build relational databases for organizations.
Both roles are expected to write queries, work with engineering teams to source the right data, perform data munging (getting data into the right format, convenient for analysis/interpretation) and derive information from data. However, in most cases, a Data Analyst is not expected to build statistical models or be hands-on in machine learning and advanced programming. Instead, a Data Analyst typically works on simpler structured SQL or similar databases or with other BI tools/packages.
Data Analyst vs Data Scientist Skill Sets
Data Analysts should have a strong background in statistics and be able to convert data from a raw form to a different format (data munging). The Data Analyst collects, processes and applies statistical algorithms to structured data. Primary responsibilities include Data collection and processing, programming, machine learning, data munging, data visualization, applying statistical analysis
Data Analyst vs. Data Scientist: Tools Knowledge
Quite similar to Data Analyst, a Data Scientist should have a thorough knowledge of R and Python. In addition, Data Scientist must also know other languages like SAS, Hive, MatLab, SQL, Pig, Spark, and Hadoop.
Data Analyst vs. Data Scientist: Interrelation
Data Scientists and Data Analysts are not two interchangeable roles. Both roles have a common goal: to draw insights from data. While their skills are most likely to overlap, generally
Data Scientists have a richer skill set, especially when it comes to their business acumen. They have a deeper familiarity with Hadoop, advanced statistical modeling, and machine learning, unlike Data Analysts.
Both Data Science professionals can transform data into answers business owners need to make better decisions, but what they’re starting with and the skills required to reach those answers will vary. Data Analysts can answer your business questions, but Data Scientists can help you formulate new questions to drive the business forward. a Data Scientist can answer the complex questions raised by Data Analysts.
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Business Analyst vs Data Analyst vs. Data Scientist
The International Institute of Business Analysis defines a business analyst as an “agent of change,” who identifies and executes new opportunities for businesses to capitalize on technology.
Business Analyst vs. Data Analyst vs. Data Scientist: Business Analyst Role
Business Analysts possess strong foundational Data Science skills as well as an ability to develop strategic business and project plans, identify key performance indicators, create use-case scenarios, and engage and communicate with stakeholders at all levels of the organization. They take a holistic view of a business challenge and work out a viable solution.
Business Analysts specialize in multiple roles such as business systems analyst, systems analyst, functional analyst, service request analyst.
Business Analyst vs. Data Analyst vs. Data Scientist: Data Analyst Role:
Data Analysts, make use of specialized analysis techniques and tools to determine how businesses can use data to make more informed decisions. Though quite similar business analyst role, a Data Analyst is more involved with the data.
They are also responsible for identifying important business challenges, applying the appropriate statistical techniques to harness structured and unstructured data, and performing complex data analysis to extract useful information and develop conclusions.
Data Analysts and Business Analysts work in liaison in many industries, especially in information technology. Data Analysts and Data Scientists are very much in demand in other industries such as agriculture, travel, food, oil, and auto insurance.
Business Analyst vs. Data Analyst vs. Data Scientist: Data Scientist Role
A Data Scientist is expected to create useful insights from information derived from the available data, through mining, building correlation models, proving causality, and searching the data for signs of anything that can deliver business impact throughout.
The Data Scientist role is a combination of business understanding, data handling, programming, and data visualization skills to drive better business results. A Data Scientist needs to possess multiple qualities such as business acumen, customer/user insights, analytics skills, statistical skills, programming skills, machine learning skills, and data visualization.
Data Analyst vs. Data Scientist vs Data Engineer: Job Profiles
The job profile of a Data Scientist includes mining and analyzing data from multiple sources, (that includes customer transactions, clickstreams, sensors, social media, log files and even GPS plots). Their prime focus is to unlock valuable and predictive insights that will influence business decisions and spur a competitive advantage.
Data Scientists are a rare combination of analytical skills, technical prowess and business acumen needed to effectively analyze massive data sets while thinking critically and shifting assumptions on the go, ultimately transforming raw intelligence into concise and actionable insights.
Data Analysts, on the other hand, interpret numbers or data into understandable language.
Every business collects data, irrespective of the industry. A Data Analyst job is to find out useful data and use it to help companies make better business decisions. This may include figuring out how to price new materials for the market, how to reduce transportation costs, solve issues that cost the company money, or even allotted budget for new recruitments.
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Data Engineers develop, construct, test and maintain architectures such as databases and large-scale data processing systems. Data Engineers work closely with Data Architects (to determine what data management systems are appropriate) and Data Scientists (to determine which data are needed for analysis). They often deal with problems associated with database integration and unstructured data sets. The ultimate aim of a Data Engineer is to provide clean, usable data to whoever may require it.
If Data engineers are the plumbers building a data pipeline, Data Scientists interpret the story of the data pipeline. To put in simpler words, data engineers clean, prepare and optimize data for consumption. Once the data becomes ready for use, Data Scientists can analyze and apply visualization techniques to understand the data.
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Data Analyst vs. Data Scientist: What do the Numbers Say?
According to a recent Forbes report, Data Scientist has been named the best job in America for three consecutive years, with a median base salary of $110,000 and over 4,524 job openings. According to Glassdoor’s 50 Best Jobs in America for 2018 research, Data Scientist jobs are ranked among the 50 best jobs based on each job’s overall Glassdoor Job Score.
According to Glassdoor’s Best Jobs in America list in 2016 and 2017, with 4,84 positions were available and with a median base salary of $110,000. DevOps engineer came in second, with a median base salary of $110,000 and 2,725 job openings.
Data engineer rounded out the top three, with 2,599 job openings and a median base salary of $106,000. The average salary for a Data Scientist with fewer than five years’ experience in 2016 was $92,000.
Highly talented, educated and experienced big Data Scientists can earn well over $250,000 per year with salary plus incentives. The hourly salary range for Data Scientist contract positions varies between $30-$85, depending upon skills and project requirements.
Data Analysts earn handsome compensation packages; between $77,500 and $118,750, according to RHT’s 2017 Salary Guide. additional programming skills, such as R and Python contributes to the higher take-home salary for Data Analysts.
Data Analyst vs Data Scientist Salary in India
The national average salary for a Data Scientist in India is ₹6,50,000. Salary estimates are based on 545 salaries submitted anonymously to Glassdoor by Data Scientist employees. On the other hand, the national average salary for a Data Analyst is ₹4,04,660 in India. Salary estimates are based on 1,226 salaries submitted anonymously to Glassdoor by Data Analyst employees.
Data Analyst vs Data Scientist salary, by comparative estimate
Data Analyst vs. Data Scientist: Which Career is Better?
Data Scientists score well above Data Analysts in terms of salary because Data Scientists have a broader and deeper skillset, especially when it comes to their business acumen. They create algorithms and model businesses use to predict future sales, make critical decisions, or launch products.
Data Analyst vs Data Scientist vs Data Engineer: Making a Choice
Do you aspire to be a Data Analyst, and then grow further to become a Data Scientist and Data Science influencer? Or does finding answers to complex business challenges interests you more than data cleansing? Whatever you want, start early to gain a competitive advantage. You must be fluent in the programming languages and tools that will help you get hired. Know more about the required skill sets of a Data Science Engineer.
Lastly, find a community and get involved! Check meetup.com for something in your area or local universities, which may have study groups that you can join. Keep a lookout for hackathons – they always need data specialists.
You may read How to Create a Killer Data Analyst Resume for creating CVs that leave an impression in the mind of the recruiters.
You may start as a Data Analyst, go on to become a Data Scientist with some years of experience, and eventually a data evangelist. Data Science offers lucrative career options. There is enough scope for growth and expansion
You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Science. Taking up a good Data Science or Data Analytics course teaches you the key Data Science skills and prepares you for the Data Scientist, Data Scientist role (that you aspire for) in the near future. Do not forget to include all your skills in your data scientist’s resume.
You may also enroll in a Data Analytics Course for more lucrative career options in Data Science. industry-relevant curriculum, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.