Portal eMASCO

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Alternative Names

Tasks

proposing new uses for existing data sources and structures

extracting data from data sources

preparing documentation to outline data sources, models and algorithms used and developed

Performing data checks and quality control reviews

Facilitating data cleaning and enrichment

integrating multiple data sets to build large and complex data sets

M
Professional, Scientific And Technical Activities
J
Information And Communication

IT-082-5:2016
Data Mining Management & Analysis

0541
Mathematics
0542
Statistics
0611
Information Technology and Information System
0613
Computer Science

Skills

Asas

Active Listening

Proficiency in giving full attention to what other people are saying, taking time to understand the points being made and asking questions as appropriate.

Critical Thinking

Proficiency in using logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems.

Speaking

Proficiency in communicating effectively as well as convey information verbally and in a way that can understand.

Reading Comprehension

Proficiency in understanding written sentences and paragraphs in work related documents.

Active Learning

Proficiency in applying new knowledge.

Khusus

Analysis

ability to collect and analyze information, problem-solve, and make decisions.

Data Analysis

proficiency in inspecting, cleansing, transforming and modelling data to get useful information, suggesting conclusions and supporting decision-making.

Analytics

ability to deconstruct information into smaller categories in order to draw conclusions.

Machine Learning

proficiency in application of artificial intelligence (AI) that provides systems the ability to automatically learn.

Data Visualization

proficiency in representing of data or information in a graph, chart, or other visual format.

Data Management

proficiency in all the disciplines related to managing data as valuable resource.

Data Mining

proficiency in discovering patterns in large data sets involving methods at the intersection of machine learning and statistics.

Python

proficiency in using programming language for web development, AI, machine learning, operating systems, mobile application development, and video games.

SQL

proficiency in a domain-specific language used in programming.

Related Courses

Training Programmes

e-Latih Online Courses

TVET Madani Courses