Data analytics is analysing unprocessed data to find relevant patterns, trends, and insights that can guide company strategy, solve issues, and support decision-making.
Key Aspects
Machine Learning
Creating models and algorithms that, without explicit programming, can learn from data and make predictions or judgements. This covers reinforcement learning, supervised learning, and unsupervised learning.
Fill in some text
Data Collection
Collect pertinent information from a range of sources, such as social media, sensors, databases, and spreadsheets.
Data Cleaning and Preparation
Fixing mistakes, inconsistencies, missing numbers, and standardising formats in order to get the data ready for analysis.
Prescriptive Analytics
Suggesting courses of action or choices in order to maximise resource allocation and decision-making, based on the knowledge gained from data analysis.
Predictive Analytics
Employing past data to forecast trends, detect hazards, and streamline company procedures in order to foresee future occurrences or results.
Statistical Analysis
Using statistical methods to evaluate the information, draw conclusions, and forecast outcomes. Regression analysis, time series analysis, inferential statistics, and descriptive statistics may be examples of this.
Exploratory Data Analysis (EDA)
Employing methods like data visualisation and summary statistics to explore and visualise the data in order to comprehend its properties, connections, and trends.