Data Analyst Programme

Pre-Requisites

The expected learners for the course are professionals wanting to acquire a strong foundation in planning, executing, and extracting insights from data analytics; focused on solving/proving business problems and hypotheses.

They could be current and aspiring Data Analysts, Brand Supervisors, Brand Managers,
Customer Loyalty Managers, E-Commerce Managers, or Marketing Managers. They would essentially be in or aspiring to roles where data analytics is used to understand better, analyze and even predict customer behaviors and actions to use these insights to improve different aspects of business conduct.

Learners are expected to:

• Be at least 21 years old
• Have a Polytechnic Diploma or two (2) years of IT-related working experience
• Be familiar with computer use, basic statistics, Microsoft Excel, and fundamentals of coding and development
• Be able to read, write and speak English at WSQ Workplace Literacy (WPL) Level 5 or equivalent
• Be able to manipulate numbers at WSQ Workplace Numeracy (WPN) Level 5 or equivalent

Target Audience

• Digital Marketing
• Social Media – Executive, Specialist,
• Assistant Manager,
• Manager, Sales & Business Development for Digital Media,
• Creative personnel looking forward to upskilling, reskilling, and developing a new skill set.
• Content Creator, Specialist, Manager,
• Freelancer
• Marketing Specialist etc.

Course Content

Module Unit 1
LO1: Process and prepare collected data for analysis
LO2 – Utilise data visualization tools to represent initial insights obtained from data

• General Analytics Workflow
• Understanding Data Cleaning and Preparation Techniques
• Preparing Data for Analysis Using Spreadsheets and Python
• Collecting Data to Extract Insights
• Loading and Processing Data Using Relational Databases
• Representing Insights Obtained from Data

Module Unit 2
LO3 – Combine and shape data across multiple data sets based on identified patterns to
facilitate analysis

• Exploring Techniques to Combine and Shape Data
• Combining and Shaping Data Using Spreadsheets
• Combining and Shaping Data Using SQL Combining and
• Shaping Data Using Python
• Integrating Data from Disparate Sources into a Data Warehouse
• Working with Streaming Data
• Using a Data Warehouse

Module Unit 3
LO4 – Establish data relationships and calculate probabilities to develop relevant
insights

• Understanding Descriptive Statistics for Data Analysis
• Performing Exploratory Data Analysis in Spreadsheets
• Summarizing Data and Deducing Probabilities Using Python
• Understanding and Applying Bayes’ Rule
• Visualizing Probabilistic and Statistical Data Using Seaborn

Module Unit 4
LO5 – Build and train a machine-learning model to facilitate further analysis
LO6 – Build and refine data models for hypothesis testing

• Designing an Experiment for Data Analysis
• Building and Training a Machine Learning Model
• Understanding and Overcoming Common Problems in Data Modeling
• Leveraging Different Validation Strategies in Data Modeling
• Tuning Hyperparameters Using Cross-Validation Scores

Module Unit 5
LO7 – Conduct multiple comparisons and detect functional relationships among variables using statistical models

• Thinking Like a Statistician
• Testing a Hypothesis
• Comparing Categorical Values with Frequency Analysis
• Analyzing Experiments with Analysis of Variance (ANOVA)
• Comparing Groups and Effects with ANOVA
• Predicting Linear Relationships with Regression
• Predicting Non-linear Relationships with Regression

Module Unit 6
LO8 – Communicate insights from data to stakeholders using various visualization techniques
LO9 – Manage the integration of data and visualized insights in a Multi-cloud or Hybrid Environment

• Communicating Insights from Statistical Data
• Communicating Insights from Business Data
• Visualizing Distributions and Relationships in Data
• Integrating Data in a Multi-cloud Environment
• Integrating Data in a Hybrid Environment

Mode of Assessment

• Practical Performance (PP)
  • Open Book
  • Summative Assessment
• Project Assessment (PA)
  • Open Book
  • Formative and Summative Assessment

Certification

Participants who fulfill all requirements will receive a Statement of Attainment (SOA) issued by SkillsFuture Singapore (SSG).

For other Data Analytics Course, please see Power BI : Data Analytics.