Data is Gold, The amount of data in the universe is growing at an exponential rate and it is the most important valuable resource not only for individual but for businesses as well. But to get the best value from data you need to be using the best techniques and the best technology.
All the organizations are coupling with the power of data and the most important thing that to perform these duties who will be responsible whether Data Scientist or Data Analyst. Here you come to know the difference between the roles and duties of data scientist and data analyst. Firstly we need to know what exactly are Data Science and Data Analyst:
Before answering the question of what a data scientist is, let's first answer what is Data Science:
Data science is a process of analyzing data using creative ways and using algorithms to develop technologies and finding solutions to the complex issues. These involve pulling apart and putting together data sets to reveal hidden patterns such as consumer habits and preferences. It may also be simply figuring out the sales trends of a specific line of product.
Amazon, for example, mines user data patterns to determine the suggested products for each user. Doing this requires a combination of statistical expertise, programming, and business knowledge. Statistics lies at the heart of data science. The field requires someone with certain quantitative capabilities to figure out complex trends within a data set that may consist of more than 1 million rows.
Programming skills, on the other hand, work together with statistics. For statistical analysis to happen, you need someone well-versed in programming languages (such as Java, SQL, and Python) to break down the data set in more readable formats. Finally, business knowledge ensures that you're solving problems that are consistent with the organization's goals. At the end of it, a "data product" such as Amazon's recommendation system may be developed.
The role of a data analyst is similar to a data scientist in surprisingly many ways. They also analyze data and derive key insights from it. The main difference is that data scientists come into picture when an organization's data volume exceeds a certain scale, which requires the creation of data products to help analyze it.
So while this means that data analysts also do data science work, they are not required to know much about programming. But data analysts must still have knowledge in statistics and business operations. At the end of it, a data analyst usually produces digestible outputs like a report or a presentation.
Comparison between Data Scientist and Data Analyst:
Here are the broad differences between data scientists and data analysts
Note: These may differ across organizations.
As mentioned above to be a data scientist or data analyst, you will need to be skilled in statistics, programming (not so much for analysts) and have a certain level of business acumen. As such, working on the below points would be a good start:
Learn Mathematics and Statistics:
Having a strong foundation in statistics and mathematics is a must as mentioned earlier in the article. As part of building this foundation, you will definitely need to be an expert in calculus and linear algebra.
Aware with Big Data Tools and Database:
Most organizations use data management software such as MySQL or Cassandra, and it will be advantageous to be accustomed with them. Knowledge in big data tools such as Hadoop will also provide you with an edge.
Must know coding:
Knowledge in statistical programming languages such as Python, R, and SAS are essential and expected of all data scientists and, to a lesser extent, data analysts.
Gain business insights and develop presentation skills:
Despite the large number of requirements in "hard" technical skills, it's also important to be involved in business projects, gain exposure to how the business is run, and understand what makes your organization "tick."
Skills in the above three points will make you good, but being accomplished in the business side of things and knowing how to present your findings in a concise and understandable manner will make you great.
If you are from a business function, points one, two, and three will most probably require you to take up a course in data science. That said being considered accomplished is not as easy as going through a short one-week program. A lot of practice is required, and attending an established institution for an extended period of time will definitely be useful.
If you are from a technical function and are already familiar with the first three points, you can aim to participate in ad-hoc business projects within your organization to understand business terms and mindsets. There is also no harm in taking up a short executive program in finance, digital marketing, etc., and arming yourself with the areas of expertise that are valued within your organization.