Contents
Artificial intelligence [AI].
Machine learning [MI].
Cyber security.
Coding Python, C++, Java, Javascript, HTML.
Describe data science
To discover the hidden actionable insights in an organization’s data, data scientists mix math and statistics, specialised programming, sophisticated analytics, artificial intelligence (AI), and machine learning with specialised subject matter expertise.
Strategic planning and decision-making can be guided by these findings, Data science is one of the fields with the quickest growth rates across all industries as a result of the increasing volume of data sources and data that results from them. Analysts can gain practical insights from the data science lifecycle, which includes a variety of roles, tools, and processes. A data science project often goes through the following phases:
Data ingestion
The data collection phase of the lifecycle involves gathering raw, unstructured, and structured data from all pertinent sources using several techniques, These techniques can involve data entry by hand, online scraping, and real-time data streaming from machines and gadgets. Unstructured data sources like log files, video, music, photos, the Internet of Things (IoT), social media, and more can also be used to collect structured data, such as consumer data.
Data processing and storage
Depending on the type of data that needs to be gathered, Businesses must take into account various storage systems. Data can have a variety of formats and structures. Creating standards for data storage and organisation with the aid of data management teams makes it easier to implement workflows for analytics, machine learning, and deep learning models.
Using ETL (extract, transform, load) jobs or other data integration tools, this stage involves cleaning, deduplicating, transforming, and merging the data. Before being loaded into a data warehouse, data lake, or another repository, this data preparation is crucial for boosting data quality.
Data analysis
In this case, data scientists perform an exploratory data analysis to look for biases and trends in the data as well as the ranges and distributions of values.
The generation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to evaluate the data’s applicability for modelling purposes in predictive analytics, Machine Learning, and/or deep learning.
Organizations may depend on these insights for corporate decision-making, enabling them to achieve more scalability, depending on the model’s accuracy.
Communicate
Finally, insights are provided as reports and other data visualisations to help business analysts and other decision-makers better comprehend the insights and their effects on the organisation.
Data scientists can create visuals using built-in features of programming languages for data science like R or Python or by using specialised visualisation software. (Data Science)
