With Data Science jobs on the rise, there’s a question that often lurks in the minds of aspirants – What’s the difference between a Data Scientist and a Data Analyst?
Are these 2 the same?
Such questions have been a source of great confusion among youngsters who wish to make a successful career in Data Science. Today, we’re here to put these questions to rest and clarify the entire matter for you!
Before diving in deep into the job profile of a Data Scientist and that of a Data Analyst, let’s first understand the core difference between the 2 job roles.
Data Scientist Job Role – Data Scientists are expert professionals equipped with a combination of coding, mathematical, statistical, analytical, and ML skills. Even during a Data Science interview, most of the questions are in and around these concepts. They explore and examine large datasets gathered from multiple sources, clean it, organize it, and process it to facilitate the ease of interpretation. While they can do analyzing tasks of an analyst, they also have to work with advanced ML algorithms, predictive models, programming and statistical tools to make sense of data and develop new processes for data modeling. A Data Scientist can also be labeled as a Data Researcher or a Data Developer, depending upon the skill set and job demand.
Data Analyst Job Role – As the name suggests, Data Analysts are primarily involved with the day-to-day data collection and analysis tasks. They must sift through data to identify meaningful insights from data. They look at business problems and try to find the answers to a specific set of questions from a given set of data. Furthermore, Data Analysts create visual representations of data in the form of graphs, charts, etc., for the ease of understanding of every stakeholder involved in the business process. A Data Analyst can also labelled as Data Architect or Data Administrator or an Analytics Engineer, depending upon the skill set and job demand.
Gathering from this description of the two job profiles, it is clear that a Data Scientist mainly deals with finding meaning from incoherence (unstructured/semi-structured datasets), whereas a Data Analyst has to find answers to questions based on the findings of a Data Scientist. However, sometimes the job roles do overlap, thereby giving rise to a grey area. And while Data Analysts and Data Scientists both share some similarities, there are certain pivotal differences between the two roles.
Data Scientist and Data Analyst – A Comparision
Just a minute ago, we talked about the primary job responsibilities of a Data Scientist and Data Analyst in a nutshell. Now, we’ll talk about their respective job responsibilities in detail.
- Create & define programs for data collection, modelling, analysis, and reporting.
- Perform data cleansing and processing operations to mine valuable insights from data.
- Develop custom data models and ML algorithms to suit company/customer needs.
- To mine and analyze data from company databases to foster optimization and improvement of business operations (product development, marketing techniques, and customer satisfaction).
- To use the right data visualization and predictive modelling tools to boost revenue generation, marketing strategies, enhance customer experiences, etc.
- To develop new ML methods and analytical models.
- To correlate different datasets, determine the validity of new data sources and data collection methods.
- To coordinate and communicate with both IT and business management teams to implement data models and monitor the outcomes.
- To identify new business opportunities and determine how the findings can be used to enhance business strategies and outcomes.
- To create sophisticated tools/processes to monitor and analyze the performance of data models accurately.
- To develop A/B testing frameworks to test model functioning and quality.
- To take on the role of a visionary who can unlock new possibilities from data.
- To analyze and mine business data to identify correlations and discover valuable patterns from disparate data points.
- To work with customer-centric algorithm models and personalize them to fit individual customer requirements.
- To create and deploy custom models to uncover answers to business matters such as marketing strategies and their performance, customer taste, and preference patterns, etc.
- To map and trace data from multiple systems to solve specific business problems.
- To write SQL queries to extract data from the data warehouse and to identify the answers to complex business issues.
- To apply statistical analysis methods to conduct consumer data research and analytics.
- To coordinate with Data Scientists and Data Engineers to gather new data from multiple sources.
- To design and develop data visualization reports, dashboards, etc., to help the business management team to make better business decisions.
- To perform routine analysis tasks as well as quantitative analysis as and when required to support day-to-day business functioning and decision making.
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The role of a Data Scientist is highly specialized and versatile. Hence, Data Scientists mostly have advanced degrees such as a Master’s or PhD. According to KDnuggets, nearly 88% of Data Scientists have a master’s degree, and at least 46 % of them hold a PhD. Let’s take a look at the role requirements of a Data Scientist:
- A minimum of a Master’s degree in Statistics/Mathematics/Computer Science. Better if you have a PhD.
- Proficiency in programming languages like R, Python, Java, SQL, to name a few.
- In-depth knowledge of ML techniques, including clustering, decision trees, artificial neural networks, etc.
- In-depth knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, etc.).
- Experience in working with statistical and data mining techniques (linear regression, random forest, trees, text mining, social network analysis, etc.).
- Experience in working with as well as creating data architectures.
- Experience in manipulating data sets and developing statistical models.
- Experience in using web services such as S3, Spark, Redshift, DigitalOcean, etc.
- Experience in analyzing data from third-party providers like Google Analytics, AdWords, Facebook Insights, Site Catalyst, Coremetrics, etc.
- Experience in working with distributed data/computing tools like Map/Reduce, Hadoop, Spark, Hive, MySQL, etc.
- Experience in data visualization using tools like ggplot, Tableau, Periscope, Business Objects, D3, etc.
For the job role of a Data Analyst, the minimum requirement is to have an undergraduate STEM (science, technology, engineering, or math) degree. Having advanced degrees is excellent, but it is not a necessity. If you have strong Math, Science, Programming, Database, Predictive Analytics, and Data Modeling skills, you’re good to go. Here’s a list of all the essential requirements for a Data Analyst:
- Undergraduate degree in Mathematics/Statistics/Business with a focus on analytics.
- Proficiency in programming languages like R, Python, Java, SQL, to name a few.
- A solid combination of analytical skills, intellectual curiosity, and business acumen.
- In-depth knowledge of data mining techniques and emerging technologies including MapReduce, Spark, ML, Deep Learning, artificial neural networks, etc.
- Experience in working with the agile methodology.
- Experience in working with Microsoft Excel and Office.
- Strong communication skills (both verbal and written).
- Ability to manage and handle multiple priorities simultaneously.
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According to a PwC study report, by 2020, there will be around 2.7 million job openings for Data Scientists and Data Analysts. It further states that the applicants for these job roles must be “T-shaped”, as in, they must possess not only technical and analytical skills but also soft skills including communication, teamwork, and creativity. Since it is difficult to find such talent with the right skill set and the demand for Data Scientists and Analysts exceed the supply by a large margin, these roles promise handsome salary package.
However, the job of a Data Scientist being much more demanding than that of a Data Analyst, the salary of Data Analysts is naturally lower than Data Scientists. Glassdoor maintains that the average annual salary of Data Scientists is Rs. 10,00,000, whereas that of a Data Analyst is Rs. 4,82,041.
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Considering all the points mentioned above, the job title of Data Scientists and Data Analysts seem deceptively similar owing to the few similarities in skill sets and job responsibilities. For instance, if you have a STEM background with a flair in programming, analytics, and statistics, you are ideally suited for a career in Data Science. However, the subtle differences between the two give rise to the significant disparity in the salary level.
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If you are still cannot make a choice, let’s make it simpler for you – suppose you are great with numbers, but you still need to go a long way to perfect your coding and data modelling skills, you’d better start your career as a Data Analyst. Gradually, you can upskill and then become a Data Scientist. This way, the job of a Data Analyst can become a stepping stone to becoming a Data Scientist. All in all, both the options are emerging and highly lucrative career choices, so you’ll have a promising career in Data Science no matter what you choose.
Is it advantageous to start as a data analyst if one wants to become a data scientist?
A data analyst's and a data scientist's common objective is to figure out how to use data to make better business decisions. Data analysis has become one of the most in-demand professions on the planet. As a result, the pay of a data analyst in India is substantially greater than the salaries of other software experts. A bachelor's degree in a discipline such as mathematics, statistics, computer science, or finance is required for most data analyst positions. Working as a data analyst initially might be an excellent approach to start a career as a data scientist if you're just getting started.
What is the average time it takes to become a data scientist?
Those who pursue a data scientist degree at a university can do so in 3–4 years. The most common degree among data scientists is computer science. Direct entrance into Data Science through a four-year Bachelor's degree is becoming more prevalent. It may take an extra 1–2 years for those who want to pursue a master's degree in data science. A year of training is required for an entry-level job. The entire amount of time can be adjusted to 5–6 years.
Is there a certain age requirement to work as a data analyst?
Data analysts are among the most blooming jobs on the planet. Data analysts command high pay and great benefits, especially at the entry level, because demand is so high and the number of individuals who can genuinely execute this work well is so limited. To work as a data scientist or data analyst, you must have a thorough understanding of the necessary skills and tools. Age is not a barrier for you if you are excited for the opportunity to grasp the needed skills. Anyone who is eager to gain the skills necessary for a data analysis career can do so.