Data Analytics is a skill that will future proof your career even with the rise of artificial intelligence (AI) as human intuition will still come into play in every day decision-making or tasks, which is a limitation of the machine.

With the inability of AI to determine or fact check a myriad of information readily available from valid and invalid sources, it takes a human being to countercheck and validate the data and recommendations of the machine. As such, critical thinking and analysis to understand data and context, with the use of Artificial Intelligence will be needed, especially in the coming years.

This 1-day certification program in Data Analytics introduces you to statistical analysis, data analytics, with practical applications and case studies.

Candidates for this exam are seeking to prove introductory knowledge of how to responsibly manipulate, analyze, and communicate findings of data analysis. Candidates should have instruction or hands-on experience with data manipulation, analysis, visualization, and communication. They should be familiar with general data concepts, data-related laws, and responsible analytics practices.

About the IT Specialist Program

The Information Technology Specialist program is a way for candidates to validate foundational IT skills sought after by employers. The IT Specialist program is aimed at individuals who are considering or just beginning a path to a career in information technology. The certification exam is internationally recognized and issued by Certiport, a Pearson VUE business.

Course Inclusions

1. Instructor-led virtual training.

2. Exam simulator.

3. Exam voucher.

4. Internationally recognized Data Analytics Certification from IT Specialist.

   
   

Course Outline

 Introduction to Statistical Analysis

• Counting and Probability o Basic counting principles, permutations, and combinations o Introduction to probability and key concepts.

• Probability Distributions and Sampling o Probability Distributions: Binomial, Poisson, and Normal distributions.

• Sampling Distributions: Concept of sampling distribution, Central Limit Theorem and its significance.

• Break Estimation and Hypothesis Testing.

• Estimation o Point and interval estimation.

• Hypothesis Testing o Null and alternative hypotheses.

• Type I and Type II errors.

• P-values and significance levels Correlation and Regression.

• Correlation o Understanding correlation and its interpretation.

• Regression o Simple linear regression and its applications.

Introduction to Data Analytics

• Data Analytics Overview.

• Types of Data Analytics.

• Data Visualization.

• Descriptive Statistics.

Practical Applications and Case Studies

• Graphical Techniques (Histograms, bar charts, line graphs).

• Skewness & Kurtosis, Box Plot (Understanding data distribution and outliers through graphical techniques Hands-On Session).

• ANOVA and Chi-Square Tests.

• Imputation Techniques and Data Cleaning.

• Correlation and Regression Analysis.

Q&A and Wrap-Up

• Review of key concepts.

• Q&A session.

• Quick Tour of Gmetrix Tool for exam preparation.