Think data science, think opportunity. Data science helps organisations make better futuristic decisions. With terabytes of data generated every minute, organisations want to ensure this data helps them make impactful decisions. Here is where the data scientist adds value.
Data science is all about extracting knowledge from data. Analysing data, providing context to statistical insights, and construction of organisational data architecture are the tasks of a data artist. However, the roles of a data scientist and a data analyst are miles apart. While one helps explain, the other predicts which profile is better suited for you.
Here is a compiled a list of prominent roles in the world of data science:
A data analyst helps others make decisions and prioritise their work. For instance, data analysts assist the marketing department in determining if their campaign is effective. The real value of data science lies in the data, according to Dave Elkin, CEO of InsideSales.com.
This makes the data analyst’s task a crucial one. Most of a data analyst’s work is to clean data, which involves eliminating bad data. Other aspects involve writing algorithms, gathering data from sources, and ensuring accuracy and usability. They normalise and visualise processed data without modelling it.
Skills required: SQL (a programming language designed for managing data), PySQL, Data Visualisation using COTS (Tableau, PowerBI), R (ggplots), and Python (Matplotlib).
In simple terms, a data scientist’s job is to analyse data for actionable insights. They must have a clear understanding of analytical functions. The work mainly focusses on data modelling using aggregated and cleaned processed data for building predictive models, feature engineering, tuning model performance, combining models, and optimising prediction results.
Skills required: Machine Learning, Deep Learning modelling like Linear/Logistic Regressions, Random Forest, Gradient Boosting, SVMs, PCA, LDA, Neural Networks, CNN, RNN, LSTM, Time Series Forecasting, R, Python, SPSS Modeler, and SAS EG/EM.
Data engineers build reservoirs of big data. They develop, construct, test, and maintain architectures such as databases and large-scale processing systems. They are responsible for deploying data models into the production environment, integrating them into business IT infrastructure, and optimising model prediction and runtime.
Skills required: PySpark, Sparkling H2O, NGINX, and ScalaR.
A data architect builds an organisation’s data architecture. This architecture is an information technology discipline concerned with designing, creating, deploying, and managing an organisation’s database. The primary tasks include setting up database infrastructure, liaising with IT backend developers for data collection, storing and so on.
Skills required: Data Infrastructure and Architect, Databases RDMBS, NoSQL, MongoDB, Hadoop, HD Insights, Amazon Redshift, and RDS.
The business analyst designs or modifies business or IT systems. They interact with the business stakeholders and subject matter experts to gauge challenges. Major tasks in this role are visualising data, generating reports and charts either from processed data or predictions/insights from modelling and communicating results to stakeholders and business users.
Skills required: Data visualisations Tableau, SAS VA, PowerBI, R (ggplots), Python (matplotlib), modelling concepts, modelling performance evaluation metrics, and communication skills.
It’s a great time to be in the field of data science right now.
These days, many organisations, like Accenture are actively seeking talented data scientists. They provide extensive training to their data science and analytics teams. No wonder the Harvard Business Review calls it the “Sexiest Job of the 21st century.”
Original news is from e27.