As part of data analysis, you identify, analyse, and model data according to fundamental principles, concepts, and techniques. A key goal of this course is to enable candidates to define data requirements with detailed understanding and rigor.
The science of data analytics involves analysing raw data to draw conclusions. The techniques and processes of data analytics have been automated into mechanical processes and algorithms that operate on raw data for human consumption.
You will gain the understanding and apply specific measures of data analytics needed for organisations and individuals such as:
Completing this course will enable you to enter the data industry with the knowledge, skills, and understanding needed for roles such as:
We’ll cover data, internal datasets, open datasets, data formats for analytical purposes, and data architecture.
We’ll cover statistical methods, data trends, algorithms, and filtering data.
We’ll cover methods for communicating data, methods relating to data, presenting data, and visualisation tools and techniques.
We’ll cover data’s role in business, communication tools, working in a multi-functional team, and continuous professional development (CPD).
We’ll cover Python variables, data types, constructor functions, IF statements, Lambda functions, arrays, and database tables.
We’ll cover structures of R data, and arithmetic and logical operators.
We’ll cover SQL in websites, relational databases, database tables, and SQL language basics.
We’ll cover K-Means Clustering unsupervised techniques for grouping similar objects, Regression Analysis, Hadoop and MapReduce, further topics on SQL, Time Series Analysis, Classification, Association rules unsupervised learning method, and Text Analysis.
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