Both data scientists and analysts work with data, although they do so in distinct ways. Today’s tech business relies heavily on big data because of the clear-cut actionable insights and results it can produce. However, it also calls for having the knowledge and tools necessary to sift through such enormous databases and extract the relevant data.
To better interpret massive amounts of data, data science and analytics have evolved from being primarily the domain of academia to being essential elements of business intelligence and big data analytics technologies.
Data scientists and analysts will soon be in high demand and offer competitive salaries. Although there is a lot of interest in data professionals, it isn’t always clear what sets a data analyst apart from a data scientist. Information is dealt with in both jobs, but in different ways.
Differentiating between data analytics and data science can be challenging, though. Despite the fact that they are connected, the two produce different outcomes and go different paths.
Data Scientist vs Data Analyst
To solve real-world business problems with structured data, data analysts employ technologies like SQL, R, or Python as diverse computer programming languages, data visualisation applications, and statistical analysis. The typical duties of a data analyst include:
- Together with organisational leaders, identify information needs.
- Gather data from primary and secondary sources.
- Clean and reorganise data in preparation for analysis.
- Analyse data sets for trends and patterns that can be converted into information.
- Inspire data-driven decisions by clearly presenting findings.
Data scientists usually deal with the unknowable by making future predictions using ever-more-complex data technologies. They might develop machine learning automation tools or develop predictive modelling methods that can handle both structured and unstructured data. This occupation is frequently considered to be a more specialised type of data analyst.
For the majority of data analyst positions, a bachelor’s degree in a subject like math, statistics, computer science, or finance is necessary. Data scientists (as well as many advanced data analysts) frequently need to have a master’s or doctoral degree in data science, information technology, mathematics, or statistics.
The most typical path to a career in data has traditionally been with a degree, but there are some new options emerging for those without a degree or prior experience.
The key responsibilities of an entry-level data analyst employment can be reporting and dashboard building. The following phase can involve taking on a role in advanced analytics or strategy.
An advanced analyst who has been employed for more than nine years can be motivated to rise to the role of analytics manager. In some circumstances, a data analyst may decide to enhance their education and skills to become a data scientist.
There is now a skills gap in data science, with many more open positions than qualified candidates. Companies looking to fill these positions are training current employees as well as looking for career changers who have completed bootcamps. A data scientist who is already employed may decide to continue their education and earn a Ph.D. in order to advance their data science career.
There is good news for both groups. Indeed projects a 20% increase in job growth for data analysts between 2018 and 2028, which is a faster rate than average. This is as a result of the need for better market research across various industries.
Data analysts might find employment in the IT, healthcare, financial, and insurance sectors. In the US, a data analyst has an average salary of $70,000. According to the KDnuggets post, North Carolina has the highest possible salary for a data analyst, at $85,000 per year.
Clearly, the future of data science employment is equally as bright as that of data analysts. According to IBM, demand for data scientists will rise by 28% by 2020. The Bureau of Labour Statistics lists data scientists as one of the top 20 fastest growing occupations, with a projected 31% growth over the next ten years.
The US Bureau of Labour Statistics estimates that modelling expertise is worth $30,000 more. Data scientists make above $100,000 on average. See our post How Much Do Data Scientists Make for additional information on data science salaries and how they are influenced by a variety of factors.
Positions in data science and data analysis are available for five more days than the national average. There is little competition and a huge need for all kinds of data analysis and science from businesses.
The data scientists utilise Python. In the Digital Skills Survey, Excel was found to be the technical tool that was most frequently used, with over 81 percent of data professionals using it. Then came Tableau, Python, and SQL.
Excel being at the top of the list was unexpected, so we looked a little deeper to see how these responses were broken down by job title. We examined how the five main categories of responder jobs distributed their tools.
Data scientists, on the other hand, tended to use Excel far less frequently. The only respondents who claimed Python was their most frequently used programming language were those with this job description. The use of secondary technologies like SQL and Tableau was also substantially more widespread among data scientists.
The findings of this survey seem to support the notion that the job title “Data Scientist” has historically implied a higher level of training and experience; more knowledge and skills would give one more exposure to a programming language like Python as well as to any other pertinent technology.
About 40% of data scientist positions demand a master’s degree. a PhD or master’s degree, for instance. More than 80% of data scientists have master’s degrees. More than 45% of people hold doctoral degrees.
Data Scientist Qualification:
- Knowledge of Python coding is necessary. Perl, Ruby, and other programming languages are included in it because it is the most popular language.
- Knowledge of SAS/R is essential.
- A requirement for becoming a data scientist is experience working with unstructured data. Regardless of whether it originates from videos, social media, or other sources.
- Coding for SQL databases is a strength.
- It’s essential to have a solid understanding of machine learning.
- A data scientist should be knowledgeable about Hive, Mahout, Bayesian networks, and other data science tools.
Data Analyst Qualification:
- Excel, SQL, R, and Python proficiency are all needed.
- Data visualisation and communication abilities.
- Comprehensive knowledge of data wrangling methods.
- Knowledge of statistics and maths is necessary.
People are gradually settling into this new norm and prefer to shop online instead of visiting a store to get their groceries, clothing, and other needs. The worldwide e-commerce market increased to $26.7 trillion as a result of COVID-19, according to data by the United Nations Conference on Trade and Development.
The COVID-19 has also sped up the global rate of automation, which is another effect. As a result, more and more companies are enabling the use of artificial intelligence technologies to improve their operations. Data analysts and data scientists have misleadingly similar job names considering the significant differences in function responsibilities, training needs, and career trajectories.
One could consider working as a data analyst as the initial step towards becoming a data scientist. Because businesses generate enormous amounts of data every day, demand for data analysts and data scientists is growing and will continue to grow in the coming years.
Nevertheless, competent workers for data-focused jobs are in great demand on the job market today because, regardless of your perspective, organisations have a strong desire to make sense of their data and profit from it.
Once you’ve looked at things like your background, interests, and desired salary, you can decide which career is the best fit for you and begin on your path to success.