“You can have data without information, but you cannot have information without data.
Daniel Keys Moran
Data science overview
The data science is a comparatively new field when compared to business analytics. The Data science is a vast field and is a mixture of many subjects and concepts from math, statistics, coding, and analytics are mixed up together to work on data. With the internet and technology growing by leaps and bounds, there is also an unprecedented growth in the availability of data.
There are the internal data on customers and leads as well as data from third party via surveys and other means. There are many roles in data science with the two primary and popular roles being data scientist and data analyst.
The data scientist is the most popular role in the field of data science. It is also a very demanding role and requires a high level of education and skills when compared to other jobs. The data scientist has a very ambiguous role.
They are expected to formulate problems that might occur in the industry where they work and find a solution for them. The question formulated should once solved should show a positive impact on the industry and this is a minimum requirement. They help companies make better decisions and keep an eye on the overall performance.
Here’s a road map to becoming a data scientist. Check out what you need to learn to become a data scientist.
Role of a data scientist
Identify data sources and collect them.
Process all data- structured and unstructured. Clean and validate them.
Analyze the data for trends.
Build models for prediction and use ML algorithms for drawing insights.
Present the result with the data visualization tools.
Propose solutions and strategies for the company to follow to improve their performance.
Requirements for data scientist
Experience in data mining and data wrangling.
Understanding of machine learning
Knowledge of programming languages- Python, R, Matlab, Scala etc.,
In-depth knowledge of SQL
Experience with data analysis and visualization tools.
Experience with data frameworks- Hadoop.
Good knowledge of Math and Statistics.
“The goal is to turn data into information, and information into insight.”
This quote hits the nail on the head for the description of a data analyst. The data analyst takes up the data and transforms it into a format that is understood by the end process. This data is validated and its integrity is verified.
The data analyst will ensure that the data is clear of noise and redundancies.The data analyst is involved in the design, requirements and the activities conducted during the life-cycle of data analysis. The analyst analyses the data and prepare reports.
Role of a data analyst
Develop, implement and maintain databases for primary and secondary data.
Clean and filter data to isolate the problem and troubleshoot.
Perform data analysis on the data set and get the results.
Analyze these results for patterns and trends.
Present results with the help of data visualization tools.
Work with the management. Prioritize business issues and work on them.
Requirements for data analyst
Expertise with data models, designs for database development.
Experience with data mining.
Strong knowledge of coding, DBs and reporting technologies.
Using statistics for analyzing datasets.
Experience with collecting, analyzing and disseminating the information.
Strong analytical skills. Maintain accuracy and give attention to detail.
Expertise with queries, and reporting
Problem solving aptitude.
Differences between the Data scientist and data analyst
There is a popular opinion that data analyst is just being hyped up with the title of data scientist. That’s not so. They are vastly different roles within the field of data science. The data science skills required by a data scientist may look the same, but if analyst has some skills, then know that the data scientist will need the advanced level of each skill.
The salary of a data analyst is around Rs. 4,20,000/- per annum, whereas the salary of a data scientist is around Rs. 7,00,000/- per annum on an average in India.
The data scientist works from a business point of view. Their scope for problems are much larger than the data analysts.
Usually, data scientists handle problems with no clear solution. A data analyst in contrast works with a set of data and draw out insights and makes them into reports and delivers them.
Business acumen is a strong and must have skill for every data scientist whereas a data analyst needs more of business intelligence skills than business acumen.
The data scientist does not just look into solutions but also search for problems which will be value addition when solved. The data scientist will formulate questions that they will bring maximum profit to the business. In contrast, the data analyst will look for the optimal solution to the problem or an issue faced by the business and figure out how to improve the current situation.
The data scientist also needs advanced level of data visualization skills to convert their insights into a story that the organization will buy and implement their recommendations, whereas a data analyst needs visualization tools to create reports and showcase trends and patterns.
Every data scientist uses much more of maths, and statistics when compared to data analyst. Predictive modelling is an integral part of the work of the data scientist while data analyst has nothing to do with predictive modelling.
Machine learning is a must learn for data scientists and while it is a desirable skill, you don’t need to be an expert at it to be a data analyst.
Data scientists need to be familiar with distributed computing software like hadoop whereas data analyst needs tools to analyse the data and prepare reports on it.
A common aspect of working as a data scientist is working with whereas a data analyst is not exposed to big data. There is a separate role- Big Data analyst who are data analysts who will handle big data analysis.
The data analysts and data scientists are kindred jobs. You can consider data scientist as a superset of the data analyst. Let’s how upskilling can take one from a data analyst to data scientist.
Skills a Data Analyst should Upgrade to become a Data Scientist
Advanced Statistics- Data scientists depend upon the statistical knowledge far more than any other data science role. If it’s been a while, then you need to brush up on your skill with statistics. If you want a full refresher course, then it is best to take an online course on data science for the certification.
Machine Learning-while for a data analyst this is a desirable skills as a value addition, there is no in-depth application as a data analyst. But, a data scientist drowns in ML concepts everyday to make their living. Learn the differences between ML and Data science. ML is a subset of AI. If you’re confused about the difference between AI and data science, clear up all your doubts with our article on it.
Programming languages- You may already be skilled with R and Python. Everyone knows that there are data scientists who love Python. So, if you don’t know Python, check out our online course on Python. But, to be a data scientist especially in a research oriented job means you may also need languages like Matlab.
Scala is another good option to learn. You can never go wrong with taking an online course on Java, which is also a useful skill for a data scientist. You can also choose R for your data science project if there is a requirement.
Hadoop and other tools- There are more responsibilities of a data scientist than of a data analyst. More tools are also required for the same. Know what data science tools are required for making the jobs of a data scientist easier. You can learn tools effectively from experts with Instructor-led offline data science course.
Big Data- Big data is working with gigantic amounts of data. A common skill for data scientist the data analyst has to learn to work with big data and its related tools.
In Conclusion, data science is a trending field and the data scientists and data analysts are the two more sought after roles. Though a few tools and few of the work they do are similar, there is a huge difference between the two job profiles in data science. Both jobs are great and while it is easier to become a data analyst,
The base pay of data scientist is higher. If you aspire to get a job with data science specialty, the profiles of data analysts and data scientists sounds great.