Data Science: Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data
Statistical Analysis: Statistical analysis is the science of collecting data and uncovering patterns and trends. It’s really just another way of saying “statistics.” After collecting data you can analyze it to: Summarize the data
Machine Learning Algorithms: Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence.
Business Intelligence: The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making.
Data Analysis: Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.
Text Mining: Text mining is an automatic process that uses natural language processing to extract valuable insights from unstructured text. By transforming data into information that machines can understand, text mining automates the process of classifying texts by sentiment, topic, and intent.
Visualization: Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity.
Data Ingestion: It is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization.
Data Wrangling: Data wrangling is the process of gathering, selecting, and transforming data to answer an analytical question. Also known as data cleaning or “munging”