Data science is an interdisciplinary field that combines statistical, mathematical and computer science skills to extract insights and knowledge from structured and unstructured data. It involves the use of tools and techniques such as machine learning, data mining, data visualization and statistical analysis to uncover patterns, trends and relationships in data. Data scientists work with large and complex datasets to develop models and algorithms that can be used to make informed decisions and solve complex business problems. Data science has applications in a wide range of industries including finance, healthcare, marketing and telecommunications.

The broad subject of data science employs statistical and computational techniques to draw conclusions from data. Techniques for data analysis, data visualisation, machine learning, deep learning and artificial intelligence are all examples of data science material.

Techniques for data analysis assist in extracting patterns, trends, or correlations from data that can be utilised to generate insightful conclusions. To make data easier to understand, data visualisation entails representing the data through 2D or 3D graphs and charts. Computers may learn from data and carry out a certain task without explicit instructions thanks to machine learning. Deep learning is a branch of machine learning that is used to handle complex data, including speech or image recognition. The development of intelligent machines that can carry out tasks that usually require human interaction is known as artificial intelligence.

Since data science material offers insights into consumer behaviour, financial trends and disease diagnosis, it is crucial for a variety of industries like healthcare, banking and marketing. The discipline of data science is expanding quickly and is predicted to produce a lot of career opportunities in the future.

Data acquisition: It is the procedure for gathering, compiling and analysing unprocessed data from numerous sources.

Data cleaning: It is the process of locating and rectifying incorrect, insufficient, or irrelevant data.

Exploratory Data Analysis (EDA): A technique for analysing and visualising data to find trends and connections.

Data visualisation: The method of displaying data in a graphical, pictorial, or tabular format to make it simpler to interpret and derive conclusions from.

Machine learning: A method for creating artificial intelligence that entails teaching computer algorithms to make predictions or judgements based on information.

Natural Language Processing (NLP): It is a branch of machine learning that focuses on how computers interact with human language to recognise, decipher and create natural language.

Data mining: A technique for finding patterns, connections and anomalies in huge databases.

Big data: It is a term used to represent extraordinarily huge datasets that are too complicated and demand specialised tools and analysis methods.

Statistical analysis: The process of utilising statistical techniques to analyse numerical data in order to discover patterns or relationships.

Predictive modelling: The process of predicting outcomes based on past data using statistical algorithms and machine learning approaches.