(SCTP) Data Science Professional (Classroom & Synchronous e-learning)
About This Course
Learners will be able to:
1. acquired a solid foundation in both Data Science and Probability/Statistics.
2. acquired proficient in data preparation and programming for data science in Python.
3. Work with big data, different data sources and databases usch as Hadoop and Spark, distributed computing with Hadoop and Spark, data warehousing and ETL, NoSQL databases like MongoDB and Cassandra, and stream processing with Kafka and Storm. This learning unit also covered the types of data sources and databases, data modelling and normalization, structured data sources and databases, relational databases, SQL queries, MySQL and PostgreSQL installation, configuration and usage, and data warehousing and OLAP.
4. Develop a foundation in the different Machine Learning techniques and be able to apply them in real-world scenarios.
5. acquired a solid understanding of the principles of data visualisation, and be able to create interactive and compelling data visualisations using different tools and techniques for effective communication of insights to stakeholders.
6. acquired the knowledge and application skills in problem solving, to identifying a real-life problem or an opportunity in their companies or businesses and to develop a solution or a plan to capture the opportunity.
7. apply knowledge to gain insights, know how to communicate effectively as a leader that is increasingly important to drive adaptability and change.
What You'll Learn
In this course, students will be introduced to the process of data analysis from start to finish – from collecting data and refining it for analysis to communicating the results of their research. The goal is to equip them with an understanding of key data science principles and techniques as well as an appreciation for the challenges associated with real-world applications.
In addition to exploring fundamental concepts such as descriptive statistics and visualization methods, students will learn how to apply sophisticated algorithms and the Python programming language in order to bring powerful insights from complex datasets.
They will explore topics such as supervised learning models for predictive analysis (e.g., linear regression), unsupervised machine learning approaches for clustering or pattern recognition (e.g., K-means clustering), nonlinear models for nonparametric problems (e.g., decision trees), natural language processing techniques for text analytics (e.g., sentiment analysis), deep learning networks for image recognition/classification tasks (e.g., convolutional neural networks), reinforcement learning algorithms for autonomous robotic systems (e.g., Q-learning), time series analysis for forecasting methods such as ARIMA models or Holt-Winters smoothing techniques, and much more.
Entry Requirements
Academic Level Required:
At least 3 GCE ‘O’ Level Passes including English Language with C6 pass or Higher Nitec or Equivalent
Language Proficiency:
Be able to listen, speak, read and write English at a proficiency level equivalent to WSQ Workplace Literacy (WPLN) Level 4.
Minimum Age Required:
21