Data science is one of the “sexiest jobs,” which is considered as a buzz on the internet. Data science is a field which is continuously growing and hot right now. We’ll require lots of data scientists than there are currently available.
However, there are a wide range of various jobs and roles under the data science tree to choose from. Here is a complete list:
A data analysts job is to handle various tasks involving data wrangling, visualization, and processing of large amounts of data. They make use of data for companies to make wise business decisions and also perform queries on the databases continuously. This is because they need to modify and create algorithms that can be used to obtain information from large databases without changing the data.
SAS, R, SQL, and Python are some of the requirements for data analysis. Whereas, doing certification in the mentioned fields can improve the skills for your job applications. You need to be good at areas of problem-solving.
Data engineers are responsible for building enormous reservoirs for big data. They construct, develop, test and also maintain architectures such as large-scale data processing systems and databases. Data engineers also update the current systems with enhanced versions of the existing technologies to boost the performance of the databases.
If you are curious about knowing which skills are required to become a data engineer, here are some of those which include NoSQL, Matlab, C++, Ruby, and Hive. It would be better to work on some of the popular ETL tools and data APIs, etc.
Database administrator’s job profile is mostly self-explanatory. They are in charge of the proper functioning of the databases of an enterprise and revoke or grant its services to the workers of the company relying on their requirements. They are additionally in charge of database reinforcements and recoveries.
The essential skills of a database administrator comprise of database recovery and backup, design, data security and data modeling, etc. It’s certainly a bonus if you are good at disaster management.
The scarcity of machine learning engineers increases due to the rise in the demand, although the job profile has its challenges. Besides having a broad knowledge of few technologies like REST APIs, SQL, etc. They are also expected to implement standard machine learning algorithms such as clustering, classification and perform A/B testing, build data pipelines.
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A data scientist is a person characterized by a particular set of traits, qualities, a way of thinking, and ambition, just like any other profession, not just by a set of skills. They have to figure out the challenges of a business and provide the best solutions using data processing and data analysis.
For example, they are believed to perform predictive analysis and fine tune them through a “disorganized/unstructured” data to present actionable insights. In another way, they do this by recognizing patterns and trends, which help companies perform better.
To lead your career as a data scientist, you should be expert in SQL, MatLab, Python, R and others supportive technologies. It can be a bonus if you have a degree in computer engineering or mathematics, etc.
A data architect manages data by creating blueprints so that the databases can easily be centralized, protected and integrated with the best security standards. They also safeguard that the data engineers have best systems and tools to work with.
The job profile of a data architect requires expertise in data modeling, data warehousing, and ETL, etc. You should also be well prepared with Pig, Hive, and Spark.
A statistician collects numerical data and then presents it, helping enterprises to make sense of quantitative data, make predictions and to spot trends. He should also have an excellent understanding of data organization and statistical theories. They develop new methodologies for the engineers to implement.
A statistician is good at a different database system like Data Mining, SQL, and various machine learning technologies. They have a passion for logic.
Key skills for statisticians:
The role of a business analyst differs from that of a data science job. Though they have a better understanding of how data- related technologies perform and how to control massive volumes of data, they also differentiate high-value from the low-value data. In simple words, they see how the Big Data can be integrated into actionable insights for the growth of a business.
Business analysts act as a bridge between the management executives and data engineers. So, they have a good understanding of business intelligence and finances, and also technologies such as Data visualization tools, data modeling, etc.
A data and analytics manager inspects the data science operations and allots the duties to their respective teams according to expertise and skills. Their pros should include technologies like R, SQL, SAS, etc.
Mainly you should have exceptional leadership qualities, social skills, and an excellent thinking attitude. You should be proficient in Data science technologies like SAS, Python, Java, R, etc.
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