What is Data Analytics
Is a process of identifying trends, KPI, future prediction from large data set that help business to make more strategic decision. DA utilize data to draw meaningful insights and solve problems. e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, product recommendation, etc
Hard Skills: Statistics, Math, Excel, SQL, Python, Data visualization tool, ML
Soft Skills: Critical Thinking, Presentation Skills
- Descriptive analytics examines what happened in the past: Monthly revenue, quarterly sales, yearly website traffic, and so on. These types of findings allow an organization to spot trends.
- Diagnostic analytics considers why something happened by comparing descriptive data sets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome.
- Predictive analytics seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action—like reaching out to a customer who is unlikely to renew a contract, for example.
- Prescriptive analytics attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.
Data Analytics Process
- Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making
- Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
- Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
Artificial Intelligence: It deals with giving machines the ability to think and behave like Human Beings.
Machine Learning: It is the process of enabling a machine to learn from its past, in order to produce better results.
Data Science: define and construct new process, model, and algorithm that can be used in DA.
AI requires ML, and ML requires Data Science. ML is a subset of Artificial Intelligence but Data Science isn’t exactly the subset of ML but it uses Machine Learning to analyze data & make predictions about the future. It combines ML with other disciplines like Big Data Analytics & Cloud Computing. Data Science is a practical application of ML with a complete focus on solving real-world problems.