6 Differences Between Data Scientist and Data Analyst

Introduction

 

Data analysts and data scientists both deal with data, but in different ways.

 

Due to the obvious actionable insights and outcomes that big data can provide, it has become a crucial component in the tech industry today. However, knowing and having the necessary tools on hand to sort through such massive datasets to reveal the right information is also required. 

 

Data science and analytics have moved from being mostly restricted to academics to being key components of Business Intelligence and big data analytics tools in order to better understand huge data.

 

In 2022, data analysts and data scientists will be two of the most in-demand, well-paying careers. While there is definitely a lot of interest in data professionals, the distinction between a data analyst and a data scientist isn't always evident. Both positions deal with information, but in distinct ways.

 

However, it can be confusing to differentiate between data analytics and data science. Despite the two being interconnected, they provide different results and pursue different approaches.

 

(Related reading: Business Intelligence vs business analytics)

 

 

Data Analysts vs Data Scientists

 

1. Job Description

 

Data analysts use technologies like SQL, R, or Python as different computer programming languages, data visualization software, and statistical analysis to address practical business problems using structured data. A data analyst's typical responsibilities include:

 

• Identifying informational needs in collaboration with organizational leaders

• Obtaining information from both primary and secondary sources

• Data cleaning and reorganization in preparation for analysis

• Analyzing data sets for trends and patterns that can be turned into useful information

• Informing data-driven judgments by presenting findings in an easy-to-understand manner.

 

Data scientists frequently cope with the unknown by predicting the future using increasingly complex data tools. They might create predictive modeling techniques that can handle both structured and unstructured data or automate their own machine learning algorithms. This position is often seen as a more advanced form of a data analyst. 

 

As explained by Simplilearn, the following are some examples of day-to-day tasks:

 

• Raw data collection, cleansing, and processing

• Predictive models and machine learning algorithms are being developed to mine large data sets.

• Creating tools and procedures for tracking and analyzing data accuracy

• Creating dashboards, reports, and data visualization tools

• Automating data collecting and processing by writing programs

 

 

2. Educational Requirements

 

A bachelor's degree in an area such as mathematics, statistics, computer science, or finance is required for most data analyst positions. A master's or doctoral degree in data science, information technology, mathematics, or statistics is often required of data scientists (as well as many advanced data analysts).

 

While a degree has always been the most common road to a career in data, some new opportunities for those without a degree or prior experience are developing.

 

In fewer than six months of study, you can gain the skills needed for an entry-level career as a data analyst by receiving a Professional Certificate in data analytics from Google or IBM, both of which are accessible on Coursera. You'll have access to a hiring consortium of more than 130 organizations once you've completed the Google Certificate.

 

 

3. Career Growth

 

A data analyst may begin their career as an entry-level position, with reporting and dashboard creation as their primary tasks. The next step might be to take on a strategy or advanced analytics-related function. 

 

Taking it a step further, an advanced analyst who has worked for over nine years would be interested in advancing to the position of analytics manager. A data analyst may choose to further their education and talents to become a data scientist in some situations.

 

In data science, there is now a skills gap, with many more unfilled roles than qualified individuals to fill them. Companies who are trying to fill these positions are searching for career changers who have undergone bootcamps, as well as educating current staff. A data scientist who is presently employed may opt to further their studies and obtain a Ph.D. in order to enhance their career in data science.

 

 

4. Salary and Career Prospects

 

Both groups have reason to rejoice. According to Indeed, data analyst positions will expand by 20% between 2018 and 2028, which is quicker than normal. This is due to the demand for improved market research in a variety of businesses. 

 

Jobs for data analysts are available in the IT, healthcare, finance, and insurance industries. A data analyst's average pay in the United States is $70,000. North Carolina has the greatest potential compensation for a data analyst, at $85,000 per year, described in the article by KDnuggets.

 

Data science job growth is clearly just as promising as data analyst job growth. Data scientists are expected to see a 28 percent increase in demand by 2020, according to IBM; the Bureau of Labor Statistics considers data science to be one of the top 20 fastest expanding jobs, with a projected 31 percent increase over the next ten years.

 

According to the US Bureau of Labor Statistics, the ability to develop models is worth $30,000 more. The average income for data scientists is over $100,000. To learn more about data science wages and how they are impacted by numerous circumstances, see our article How Much Do Data Scientists Make.

 

Both data science and data analyst positions are open for five days longer than the national average. Companies are hungry for all types of data analysis and science, and there isn't much competition.

 

 

5. Technology being used

 

Python is used by data scientists. Excel emerged as the most extensively used technical tool in the Digital Skills Survey, with nearly 81 percent of data professionals utilizing it. After that came SQL, Python, and Tableau.

 

It was surprising to find Excel at the top of the list, so we went a little further to see how these replies were divided down by job title. We looked at the distribution of tools used by the five primary groups of responder positions (Data Analyst, Business Analyst, Data Scientist, Researcher, and Data Analytics Manager).

 

On the other hand, Data Scientists used Excel far less frequently. In fact, respondents with this job description were the only ones who said Python was their most commonly used programming language. Additionally, Data Scientists reported using a significantly broader range of secondary tools, such as SQL and Tableau. 

 

As previously stated, the Data Scientist job title has traditionally implied a higher level of experience and training, and the findings of this survey appear to support this; additional knowledge and skills would provide more exposure to a programming language like Python, as well as any other relevant technology.

 

 

6. Skills

 

A master's degree is required for around 40% of data scientist roles. A master's degree or a doctorate, for example. Data scientists hold master's degrees in excess of 80% of the time. More than 45 percent have earned a doctorate.

 

Data Scientist Qualifications ;

 

• Python coding expertise is required. It is the most widely used language, and it includes Perl, Ruby, and other programming languages.

• SAS/R is a must-have skill.

• Working with unstructured data is a prerequisite for a Data Scientist. Whether it comes via videos, social media, or other sources.

• SQL database coding is a strong suit.

 

Data scientists should be well-versed in a variety of analytical operations. For instance, rank, median, and so on.

 

• A thorough understanding of machine learning is necessary.

• Hive, Mahout, Bayesian networks, and other data science tools should be familiar to a data scientist.

 

Data Analyst Qualifications -

 

• Excel, SQL, R, and Python are all skills that are required.

• Skills in communication and data visualization.

• In-depth understanding of data wrangling techniques.

• Skills in mathematics and statistics are required

 

 

Conclusion

 

People are progressively adopting this new normal and prefer to shop online rather than going to a store to buy their clothing, groceries, and other necessities. COVID-19 enhanced online buying, according to research by the United Nations Conference on Trade and Development, with the global e-commerce industry jumping to $26.7 trillion. 

 

Another effect of the COVID-19 is that it has accelerated the rate of automation throughout the world. As a result, many businesses are increasingly allowing artificial intelligence technology to be implemented in order to enhance their business operations. Given the considerable variances in function duties, training requirements, and career trajectory, data analysts and data scientists have deceptively similar job names.

 

A data analyst position might be thought of as a first step toward becoming a data scientist. With firms creating vast amounts of data on a daily basis, demand for data analysts and data scientists continues to rise and will continue to rise in the future years.

 

However, competent employees for data-focused occupations are highly wanted in today's job market, due to businesses' strong need to make sense of—and profit on—their data, regardless of how you look at it.

 

You may pick which job is the best fit for you and get started on your route to success once you've examined aspects like your background, personal interests, and desired compensation.