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Statistics Fundamental Training - Statistics Course, Probability, Confidence Interval, Sampling

Course Information

  • Sessions 2 days
  • Duration 15 hrs
  • Level Beginner
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

Additional Information

Duration

2 months (Full Time)

Assessment

3 hours online assessment after each module

Class (No of teacher : student): 1:20

Intake

  • 3 Nov 2025 to 29 Sep 2026
  • 4 May 2026 to 26 June 2026
  • 2 Jan 2026 to 2 Mar 2026
  • 2 Mar 2026 to 27 Apr 2026

Enrolment Requirement

  • Age: 21 years old and above
  • Language Proficiency: At least C6 for GCE "O" Level English
  • Academic: At least C6 for GCE "O" Level in any 3 subjects

Graduation Requirement

  • Attendance: 75%
  • Assessment: Passed

Statistics Fundamental Training

Course Code: C440

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What's This Course About

Dive into the riveting world of statistics with our Statistics Fundamental Training, designed to equip you with the tools and techniques pivotal to insightful data analysis. Initiate your journey with the foundational concepts of descriptive statistics and probability theory, enabling you to aptly describe and interpret data. Embrace the power of sampling theory, gaining expertise in estimating population statistics and confidence intervals, ensuring your conclusions are both precise and actionable.

Transitioning from basic theories to advanced applications, the course dives into hypothesis testing for means and proportions, guiding you in validating data-driven assumptions. Understand the nuances of goodness of fit testing, analysis of variance, and regression statistical modeling, all pivotal in unraveling data's hidden intricacies. Furthermore, delve into the relationships between data points, mastering the art of determining correlation and covariance among multiple factors. By the course's conclusion, you'll be well-versed in the myriad facets of statistics, empowering you to make informed decisions and craft compelling data stories.

WSQ Funding

Full Fee $498.00 Before GST
GST $44.82 9% of fee
Baseline Nett $293.82 SG/PR age 21+ · 50% funded
MCES / SME Nett $194.22 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course.

For WSQ funding, please checkout the details at WSQ - Statistics Fundamental Training for Beginners

Course Fee

$498.00 (GST-exclusive)
$542.82 (GST-inclusive)

Course Date

Course Time

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Introduction to Statistics

Why Statistics Matter

Categorical and Quantitative Data

Descriptive Statistics: Mean and Standard Deviation

Probability and Conditional Probability

Bayes Theorem

Discrete Probability Distributions

Continuous Probability Distributions

Software for Statistical Analysis

Topic 2 Sampling

Sampling Consideration

Central Limit Theorem

Sampling Distribution of the Mean

Standard Errors for Proportion and Mean

Confidence Interval

T-Statistics vs Z-Statistics

T-Score Table and Degree of Freedom

Calculating Confidence Interval of T-Score

Topic 3 Hypothesis Testing

Overview of Hypothesis Testing

Steps for Performing a Hypothesis Testing

P-Value and Significance Level

Types of Hypothesis Testings

One Tailed vs Two Tailed Hypothesis Testing

Type 1 and Type 2 Errors

Topic 4 Chi-Square Testing

Overview of Chi-Square Hypothesis Testing

Chi-Square Statistic and Distribution

Goodness of Fit Test

Topic 5 ANOVA: Analysis of Variance

What is Analysis of Variance

F Statistics and Distribution

One Way ANOVA

Two Way ANOVA

Topic 6 Regression

• What is Regression?

• Residues and Mean Square Error

Topic 7 Correlation Analysis

Covariance and Covariance Matrix

Correlation Coefficient and Correlation Matrix

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

Hardware: Windows and Mac Laptops

Job Roles

Job Roles

  • Data Analyst
  • Market Researcher
  • Business Analyst
  • Quality Assurance Specialist
  • Social Science Researcher
  • Graduate Student
  • Economic Analyst
  • Product Manager
  • Human Resources Analyst
  • Healthcare Data Specialist
  • Educational Researcher
  • Sports Statistician
  • Financial Analyst
  • Behavioral Scientist
  • Environmental Data Specialist.

Trainers

Trainers

Dwight Nuwan Fonseka

Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning, and AI applications. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.

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