SCTP – Data Science Professional

With the rapid growth of the data industry in Singapore and the need for data to be managed and interpreted, more and more companies are in need of skilled professionals who know how to translate raw data and extract valuable information.

Consequently, the role of the Data Scientist in Singapore is growing in importance. Data scientists are now one of the top five in-demand hiring roles in Singapore. Companies are under mounting pressures to offer an attractive Data Scientist salary in Singapore to draw in-demand talent. Based on Robert Half Salary Guide for 2023, annual salary for data scientist could reach up to S$200,000.

Data expertise is also highly sought-after with companies re-imagining their business processes. They are increasingly turning to data to identify ways for their organisation to eliminate inefficiencies while using data visualization tools for reporting and planning purposes.

This is where the Data Scientist comes in. Singaporean businesses need people with knowledge of statistics and modelling to unlock the value of complex, unprocessed data from an array of sources. Those sources can include everything from machine log data, digital media and documents, databases, the web, and social media channels.

* Salary range depending on specific roles, qualifications, and working experience.

* Source are taken from reliable sources and not determined by BELLS Institute of Higher Learning.

Data scientists are professionals who extract insights, analyze complex data sets, and develop models and algorithms to solve business problems or gain valuable insights. Their work involves combining statistical analysis, programming skills, and domain expertise to understand and interpret data.

Here are some key tasks and responsibilities of Data Scientists:

• Data Collection and Cleaning: Data scientists gather data from various sources, such as databases, APIs, or web scraping, and clean and preprocess the data to ensure it is suitable for analysis.
• Exploratory Data Analysis (EDA): They perform EDA to understand the structure and patterns within the data, identify trends, outliers, and correlations, and gain initial insights.
• Statistical Analysis: Data scientists apply statistical techniques to uncover relationships, distributions, and dependencies in the data. They may use techniques such as hypothesis testing, regression analysis, or clustering.
• Machine Learning and Modeling: Data scientists build predictive models using machine learning algorithms to make accurate predictions or classifications based on the available data. They select appropriate models, train them on historical data, tune hyperparameters, and evaluate model performance.
• Data Visualization: They create visual representations, such as charts, graphs, or dashboards, to communicate insights and findings to stakeholders effectively.
• Communication and Reporting: Data scientists present their findings, methodologies, and recommendations to non-technical stakeholders in a clear and concise manner. They collaborate with cross-functional teams to help make data-driven decisions.
• Ongoing Model Monitoring and Maintenance: Data scientists continuously monitor and evaluate the performance of deployed models, ensuring they remain accurate and effective. They may update models as new data becomes available or business requirements change.

It’s worth noting that the specific tasks and responsibilities of data scientists can vary depending on the industry, company, and project requirements. However, the core focus remains on extracting insights and value from data using various techniques and tools.

• Mid-career individuals seeking industry-relevant skills to improve employability and pivot to new sectors or job roles.
• Jobseekers seeking employment in Data Science related-roles
• Career starter aspiring to venture into the field of data science
• Entrepreneurs starting their businesses in the related field of data science

 The SCTP is open to all Singaporeans and Singapore Permanent Residents who are looking for training opportunities to transit to a new sector or job role related to the field of training, provided the below requisites are fulfilled.

 Pre-requisites

• Singapore Citizens, Singapore permanent residents, and holders of Long-term visit pass plus (“LTVP+ Holders”) who are aged 21 years old and above
• Possess right attitude to complete the programme and seek employment in the related field of training.

 Minimum Entry Requirements

• 3 GCE ‘O’ Level Passes including English Language with C6 pass or Higher Nitec or Equivalent
• Be able to listen, speak, read and write English at a proficiency level equivalent to WSQ Workplace Literacy (WPLN) Level 4

1. Introduction to Data Science, Probabilities and Statistics: (Duration: 38 hrs) This module covers the fundamentals of Data Science, starting with an overview of the field and the different types of data. Students will also learn about the various applications of Data Science and the tools and technologies used in the industry. The Probability and Statistics module covers basic probability concepts, descriptive and inferential statistics, hypothesis testing, regression analysis, and time series analysis. By the end of the course, students will have a solid foundation in both Data Science and Probability/Statistics.
2. Data Exploration, Cleaning & Programming for Data Science: (Duration: 38 hrs) This module focuses on Data Preparation and Programming for Data Science. Module I covers the basics of understanding data structure, exploratory data analysis, data cleaning, handling missing values, and outlier detection and treatment. Module II introduces students to Python and its libraries for data science (NumPy, Pandas, Matplotlib, Seaborn), data manipulation with Pandas, data visualization with Matplotlib and Seaborn. By the end of the course, students will be proficient in data preparation and programming for data science in Python.
3. Working with Big Data, Data Sources & Databases: (Duration: 24 hrs) This module will equip students with the necessary skills to work with big data, different data sources and databases. Module I introduces Big Data, storage and processing technologies like 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. Module II covers an overview and 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. Machine Learning: (Duration: 32 hrs) This module covers the basics of Machine Learning, starting with an introduction to the field. The module discusses Supervised Learning techniques such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, Naive Bayes, and K-Nearest Neighbors. It also covers Unsupervised Learning techniques such as Clustering and Dimensionality Reduction. In addition, it teaches Ensemble Learning, Deep Learning, and Model Evaluation and Selection. By the end of the module, students will have a solid foundation in the different Machine Learning techniques and be able to apply them in real-world scenarios.
5. Data Visualisation & Data Story Telling: (Duration: 37.5 hrs) This module on Data Visualization covers the principles of data visualisation, starting with an overview of the subject. The module then covers Interactive data visualisation with JavaScript using D3.js, Visualisation of geospatial data, Dashboarding and reporting, and Creating interactive dashboards with Tableau. By the end of the module, students will have 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. Problem Solving for IT Professionals: (Duration: 24 hrs) The module equips learners to implement guidelines and procedures to identify problems and conceptualise solutions using technology as an enabler.
7. Communication Skills for IT Professionals: (Duration: 24 hrs) This module teaches IT professionals to communicate effectively with different stakeholders to deliver IT solutions.

Training Methodology

The course will be conducted in classroom and facilitated by experienced trainers.

Participants will be assessed via mix of questionnaires, projects and portfolio building

Duration & Class Frequency:

Total in-class training of 217.5 hours

Full Time: 3 months – 3 days per week @ 8 hours per day

Part Time: 6 months – 3 days per week @ 3 hours per day

Facilities and Locations

Lessons will be conducted at BELLS training centre at International Plaza

Address: 10 Anson Road, International Plaza #06-18 Singapore 079903.

1. SkillsFuture Funding (Baseline) Subsidy – Eligible Singapore Citizens aged 21-39 years old and PRs can enjoy up to 70% course fee subsidies.
2. SkillsFuture Mid-Career Enhanced Subsidy (MCES) – Eligible Singapore Citizens aged 40 years old and above can enjoy up to 90% course fee subsidies.
3. Additional Funding Support (AFS) – Eligible Singapore Citizens that meet at least one of the following eligibility criteria can enjoy up to 95% course fee subsides.

• Long-term unemployed individuals (unemployed for six months or more); or
• Individuals in need of financial assistance – ComCare Short-to-Medium Term Assistance (SMTA) recipients or workfare Income Supplement (WIS) recipients; or
• Persons with Disabilities

4. As per SSG’s policy, the GST payable for all funding-eligible applicants is calculated based on prevailing GST rate after the baseline funding subsidy of 70%.

Funding Eligibility Criteria

• Singapore Citizens or Singapore Permanent Residents or LTVP+ holder
• Trainee must achieve at least 75% attendance.
• Trainee must pass all prescribed tests / assessments and attain 100% competency.
• BELLS Institute of Higher Learning reserves the right to claw back the funded amount from Trainee if he/she did not meet the eligibility criteria.

Remarks

• Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.
• Eligible Singapore Citizens can use their PSEA (Post Secondary Education Account) funds to offset course fee payable after funding.