Data Analysis Techniques

courses Introduction

In today's corporate landscape, optimizing workplace efficiencies and reducing operational costs are paramount. Managers, analysts, and advisors face increasingly complex analytical challenges and performance targets. Making informed decisions relies on accurate analysis and interpretation of numerical data.

Objectives

This courses aims to equip individuals involved in data analysis with the practical skills and understanding needed to transform data into actionable insights. Key objectives include:

• Providing a comprehensive understanding and practical experience of common analytical techniques.
• Recognizing the most suitable analysis methods for different types of problems.
• Developing the ability to evaluate applied techniques critically.
• Building a foundational knowledge of statistical methods and terminology.
• Introducing basic statistical concepts and methods.
• Exploring the use of Excel 2010 or 2013 and its Data Analysis Tool Pack.

courses Syllabus

Day 1 - The Basics

• Understanding data sources, sampling, accuracy, and completeness.
• Introduction to simple data representations and addressing practical issues.

Day 2 - Fundamental Statistics

• Exploring mean, median, mode, variance, covariance, and standard deviation.
• Descriptive statistics and handling small sample sizes.

Day 3 - Basics of Data Mining and Representation

• Visualizing single, two, and multi-dimensional data.
• Trend analysis and common visualization pitfalls.

Day 4 - Data Comparison & Frequency Analysis

• Correlation analysis and autocorrelation function.
• Multivariate and non-linear correlation techniques.
• Histograms, Pareto analysis, and percentile analysis.

Day 5 - Regression Analysis, Probability, and Confidence

• Linear and non-linear regression techniques.
• Curve fitting theory and predictive methods.
• Probability theory, setting confidence limits, risk, uncertainty, and ANOVA (analysis of variance).

Skilllinx's Data Analysis Techniques courses empowers participants with practical skills and knowledge to derive meaningful insights from numerical data, enabling informed decision-making within their organizations.