What Does a Data Scientist Do?
Data scientists have become necessary assets and are present in almost all organizations.
These professionals are data-driven individuals with high-level technical skills such as Big data skills (Hadoop, Pig,Hive, NoSQL, Spark) , Machine learning Skills, Algorithms, Statistics etc.
They are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization.
This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
They must also be able to utilize key technical tools and skills, including:
Python
R
Apache Hadoop
MapReduce
Apache Spark
NoSQL databases
Cloud computing
D3
Apache Pig
Tableau/Power BI
iPython notebooks
GitHub
Command Line
Linux/Unix
Algorithms
Data Scientist
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning
Data Analyst
Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization
Data Engineer
Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)
Machine Learning Engineer
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. They apply various algorithms and statistical techniques on data and extract information from data.
Skills needed : Programming languages (Python, R), statistical and mathematical skills, Visualization, Algorithms, Keras, Tensorflow, Pytorch.
Today, in India, these roles are very much overlapped. One might see job advertisement as a Data Scientist, but the actual work might be of that data analytics or machine learning engineer or a data engineer or mix of all.
It all depends on the organization, its size and its requirement. So, in order to secure a job in this field, one need to learn as many technologies as they can in order to fit in any role as per industry requirement.
