Data science teams are facing with many problems. They might be asked to analyze whether the Tweets sent to a business are positive or negative, or they may need to trace where the sales come from. Different organizations will have different data problems: every problem comes with its own complexities. Solving various data science problems may require different sets of skills.
Data science teams meet to resolve some of the most challenging data issues an organization could face. Each individual will have a different part of the set of skills required to complete an end-to-end science project. Roles within the science of data are really a set of complementary roles that each have a specific vocabulary. There are data scientists, but there are also data engineers and data analysts!
We realize that this can be confusing for a newcomer on the ground. We want to demystify the different roles within the data science so that you can understand the nuances in the field
The Data Scientist
A data scientist is probably one of the most popular work titles that you can put on your business card, and the more you join Silicon Valley, the more valuable this role is. A data scientist is as rare as a unicorn and works every day with the mind of a curious data wizard. He / she has a range of skills and talents ranging from being able to manage raw data, analyzing data using statistical techniques, and sharing knowledge with peers in a convincing way. No wonder these profiles are highly sought after by companies like Google and Microsoft.
The Data Analyst
The data analyst is Sherlock Holmes of the data science team. Languages like R, Python, SQL and C are paramount to it. Like the data scientist, the skills and talents needed for this role are diverse and cover the entire process of data science combined with a sound attitude of “discovery.” Data analysts are sought by companies such as HP and IBM.
The Data Architect
With the increase of important data, the importance of the work of the data architect increases rapidly. The person in this role creates plans for data management systems to integrate, centralize, protect and maintain data sources. The data architect mastered technologies like Hive, Pig and Spark, and must be aware of all the new developments in the industry.
The Data Engineer
The computer engineer often has experience in software engineering and likes to play with large databases and processing systems. Thanks to these interests, he / she can easily master the technologies and is therefore familiar with a diverse set of languages that cover both statistical programming languages and languages oriented more towards web development. Your data engineer is your jack of all trades.
Oh, the statistician! The historical leader of the data and his knowledge. Although often forgotten or replaced by exceptional titles, the statistician represents what the field of data science means: obtaining useful information about the data. With its solid background in statistical theories and methodologies, and a logical mindset and stats oriented, it reaps the data and transforms it into information and knowledge. Statisticians can handle all kinds of data. Moreover, with their quantitative experience, modern statisticians are often able to quickly master new technologies and use them to enhance their intellectual capacities. A statistician brings mathematics to the table, and his knowledge is capable of radically transforming businesses.
The Database Administrator
People often say that the data is gold again. This means you need someone who operates this valuable mine. Enter the database administrator. Your DA ensures that the database is available to all affected users, works correctly and keeps safe. Thinking about disaster prevention is natural for him / her. A DA ensures that all backup and recovery systems are in place, that security is supported, and that tracks the different technologies used and how to support them.
The Business Analyst
The commercial analyst is often a little different from the rest of the team. Although often less technically oriented, the commercial analyst consists of his in-depth knowledge of the different business processes. (S), he is skilled at linking data ideas to useful business ideas and can use narrative techniques to spread the message across the organization. They often act as an intermediary between businessmen and technicians. Companies looking for business analysts are diverse and active in very different industries. Some examples are Uber, Dell and Oracle.
Data and Analytics Manager
The cheerleader of the team. A data analyst leads the direction of the data science team and ensures that the right priorities are defined. This person combines solid technical skills in a diverse set of technologies (SQL, R, SAS, …) with the social skills required to manage a team. This is a tough job, but if you think it suits you, make sure to review the job offers from Coursera, Slack or Motorola.
Any query on Data Science, chat with us.
For more queries regarding Data Science, feel free to contact or whatsapp @ 8019479419.
source : kdnuggets