Data Science
Data science is one of the most rapidly expanding disciplines in the information technology industry. It provides unrivalled business value by isolating statistics, trends, and insights from massive amounts of structured and raw data. Data is driving corporate decisions, lowering costs, increasing operational efficiencies, propelling product development, and enabling the deployment and expediency of game-changing technologies like machine learning.
Only professionals who understand how to find, gather, wield, interpret, and synthesize data into meaningful and actionable assets relevant to your industry can wield this power.
The key components in any Data Science Project are:
- Data Exploration
- Data Modelling
- Testing the Model
- Deploying Models
Data Engineering
Data engineering is the complex task of making raw data usable to data scientists and groups within an organization. Data engineering encompasses numerous specialties of data science.
In addition to making data accessible, data engineers create raw data analyses to provide predictive models and show trends for the short- and long-term. Without data engineering, it would be impossible to make sense of the huge amounts of data that are available to businesses.
There are four key phases of the data pipeline that data engineering directly deals with:
Ingestion - This is the task of gathering data. Depending on the number of data sources, this task can be focused or large-scale.
Processing - During this phase, ingested data is sorted to achieve a specific set of data to analyze. For large data sets, this is commonly done using a distributed computing platform for scalability.
Storing - This takes the results of the processing and saves the data for fast and easy retrieval. The effectiveness of this phase relies on a sound database management system which can be on premise or in the cloud.
Access - Once in place, the data is available to users with access.