Who is a Data Scientist? What do they do?
Data Scientist gather and analyze large sets of raw structured and unstructured data and turn them into valuable information for making decisions. They analyze, process, and model data to create advanced tools that help predict the accurate future.
They are able to mine, clean, and present data and possess analytical and business skills. Large amounts of unstructured data are sourced, managed, and analysed by businesses using data scientists. Additionally, the questions that need to be answered and the precise locations of the relevant data are examined by data scientists.
Data Scientist Job Description
The job description of a data scientist is as follows:
-
Use current data to discover opportunities- Use the current data to find trends and patterns that will affect future business.
-
Develop Analytical Methods and Machine Learning Models- Fitting the data into these models for better results.
-
Data Cleaning- Cleaning the data ensures that these models' usability is good, so they can get the best results in the best output when plugged into them.
- Conducting A/B Testing- Doing two independent tests getting two different results, and seeing which one gives you better results.
Differences and Similarities Between Data Analyst and Data Scientist
Similarity: Data analysts and data scientists use software to clean, process, and analyse data. They also use BI technologies to make business reports. Examples of some BI tools are Excel and Tableau.
In addition to this, data analysts and data scientists are experts at manipulating and displaying data.
Difference: Data analysts often use structured data to address real-world business issues. On the other side, the uncertain data is handled by data scientists. To create future forecasts, data scientists employ more sophisticated data tools.
Data Analyst vs. Data Scientist: Education and Work Experience
Education: A bachelor's degree in a subject like computer science, mathematics, statistics, or finance is typically needed for data analyst positions. Data scientists often hold a master's or doctoral degree in information technology, data science, mathematics, or statistics, as do many advanced data analysts.
Work Experience: A data scientist position is advised for people who desire to develop advanced models for machine learning and apply deep learning methods to simplify human jobs. A data analyst position is more suitable for those who want to begin a career in analytics.
Data Analyst vs. Data Scientist: Roles and Responsibilities
The position and duties of a data analyst or data scientist may change based on the sector and place of employment. A data analyst examines already-existing data, whereas a data scientist develops new methods for gathering and analysing data that analysts can use.
Here are some of the typical duties performed by data analysts and data scientists to better understand how they differ.
Data Analyst: The roles and responsibilities of data analyst are as follows:
-
Using several analytics techniques, such as descriptive, diagnostic, predictive, or prescriptive analytics.
-
Using Excel for data analysis and forecasting.
-
Using BI tools to create dashboards.
Data Scientist: The roles and responsibilities of data scientist are as follows:
-
Building ETL pipelines or applying APIs for data mining.
-
Up to 60% of a data scientist's effort may be spent cleaning data.
-
In order to construct and train machine learning models, tools like Tensorflow are used.
- Constructing big data infrastructures with Hadoop and Spark.
Data Analyst vs. Data Scientist: Skill Comparison
The common skills that a data analyst possesses are:
-
A good understanding of data modelling.
-
SQL, R, Python, and a few libraries in Python (Pandas, Numpy, and Matplotlib).
-
Data visualization tools like Tableau/Power BI.
The common skills that a data scientist possesses are:
-
Understanding of working with NLP(Natural Language Processing), both structured and unstructured data.
-
SQL, R, Python, and a few libraries in Python (Pandas, Numpy, Scikit Learn, and TensorFlow).
- Data visualization tools like Tableau/Power BI.
Data Analyst vs. Data Scientist: Job Outlook
The earning potential of a data scientist or analyst may differ based on the company and the industry. Data scientists have a promising job outlook and are expected to see an increase.
Glassdoor reports that the average data scientist's salary is $117,345 per year. However, the number can vary based on the years of experience a data scientist have.
According to Robert Half Technology (RHT) 's Salary Guide, data analysts have an earning potential of between $83,750 and $142,500 per annum.
Data Analytics vs. Data Science: How the Two Careers Are Different
If you enjoy both computer programming and math and statistics, any career route might be a suitable fit for your professional aspirations.
Data scientists gather and analyze large sets of raw structured and unstructured data and turn them into valuable information for making decisions. They analyze, process, and model data to create advanced tools that help predict the accurate future.
A data analyst examines data to find significant customer insights and potential uses for the information. They also advise the company's management and other stakeholders of the details.
Data Analytics vs. Data Science: Career Growth
There is a significant distinction between the two job profiles in data science, despite the fact that some of the tools and tasks they perform are comparable. The data scientist role typically requires more technical knowledge and is more senior.
Although it is simpler to become a data analyst, data scientists start with a higher base salary. Both positions are fantastic. Data analytics, which focuses more on business intelligence (BI), might be regarded as an entry-level field.
Many people get confused about what job they want because it is not always clear what kind of work they will do. The best way to choose which career you want is to know what you will be doing daily. You cannot learn about deep learning and artificial intelligence without knowing how to collect the data.
Data Science and Data Analytics are two sides of the same coin; both careers are in great demand and continue to grow quickly. Deciding on what career path would suit you the best, you should consider the following things
- Personal Background- Before choosing a career, it is better to explore a little about the background. A data scientist requires much more technical knowledge than data analytics. You should know what qualifications to pursue and which career needs more working experience.
- Personal Interest- If you want to devote your time to statistics, advanced mathematics, data science, and business, you should choose the data scientist. And if you want to continue with numbers, programming, and statistics, then you should go with data analytics.
- Professional Interest- Sometimes, you should choose the salary scale or career path with a better future.
Frequently Asked Questions
Is it easier to become a data analyst or data scientist?
It is comparatively easy to become a data analyst. A data analyst position is more suitable for those who want to begin a career in analytics. A data scientist position is advised for people who desire to develop advanced models for machine learning.
Does data analyst require coding?
Yes, a data analyst is required to have coding skills. It is recommended to have a solid fundamental understanding of programming languages such as Python and R.
Is data scientist above data analyst?
Data Analyst roles cater to beginners seeking entry into analytics, while Data Scientist roles empower professionals to excel in crafting advanced machine learning models and leveraging deep learning for task optimization.
What is the salary of data analyst vs data scientist in India?
Glassdoor reports that the average data scientist's salary is $117,345 per year. According to Robert Half Technology (RHT) 's Salary Guide, data analysts have an earning potential of between $83,750 and $142,500 per annum.
Conclusion
Though the requirements of both roles are different, data analysts can always upskill themselves to make a career transition to the data scientist role.
Recommended Readings
Recommended Courses
38+ registered 