Data Analysis Methods and Techniques

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Data Analysis Methods and Techniques Course
Introduction:
Data analysis is a crucial process that involves the collection, modeling, and examination of data to extract valuable insights that support decision-making. The specific methods and techniques used for analysis vary depending on the industry and the objectives of the analysis.
In today's data-driven world, data analysis has become an essential requirement for making well-informed and effective decisions. It is through data analysis that organizations can gain a clear understanding of their market position in relation to competitors, enabling them to strategize and adapt accordingly.
The Data Analysis Techniques training course offers participants a comprehensive understanding of key technologies and skills utilized in data analytics and data science. The primary objective of this course is to equip individuals involved in analyzing numerical data with practical capabilities and a deep understanding of how to convert data into valuable information through appropriate analysis. Furthermore, the course emphasizes the importance of effectively communicating these results to others within the organization.
By participating in this training course, you will gain broad exposure to various data analysis techniques and develop the necessary skills to transform raw data into actionable insights. Whether you are a data analyst, a business professional, or someone interested in leveraging data for decision-making, this course will empower you to effectively analyze and interpret data, enabling you to communicate your findings in a compelling and meaningful manner within your organization.
Course Objectives:
At the end of this Data Analysis Techniques Training Course, learners will be able to do:
- To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and representation methods for numerical data
- To give delegates the ability to recognize which types of analysis are best suited to particular types of problems
- To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions
- To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics, and probability, and to be able to read and comprehend common textbooks and journal articles in this field
- To introduce some basic statistical methods and concepts
- To explore the use of Excel for data analysis and the capabilities of the Data Analysis Tool Pack
Who Should Attend?
Data Analysis Techniques training course is ideal for
- Professionals whose jobs involve in the manipulation, representation, interpretation and/or analysis of data. The course involves extensive computer-based data analysis using Excel and therefore delegates will be expected to be numerate and to enjoy working with numerical data on a computer.
Course Outlines:
The Basics
- Sources of data, data sampling, data accuracy, data completeness, simple representations, dealing with practical issues.
Fundamental Statistics
- Mean, average, median, mode, rank, variance, covariance, standard deviation, “lies, more lies and statistics”, compensations for small sample sizes, descriptive statistics, insensitive measures.
Basics of Data Mining and Representation
- Single, two and multi-dimensional data visualization, trend analysis, how to decide what it is that you want to see, box and whisker charts, common pitfalls and problems.
Data Comparison
- Correlation analysis, the autocorrelation function, practical considerations of data set dimensionality, multivariate and non-linear correlation.
Histograms and Frequency of Occurrence
- Histograms, Pareto analysis (sorted histogram), cumulative percentage analysis, the law of diminishing return, percentile analysis.
Frequency Analysis
- The Fourier transform, periodic and a-periodic data, inverse transformation, practical implications of sample rate, dynamic range and amplitude resolution.
Regression Analysis and Curve Fitting
- Linear and non-linear regression, order; best fit; minimum variance, maximum likelihood, least squares fits, curve fitting theory, linear, exponential and polynomial curve fits, predictive methods.
Probability and Confidence
- Probability theory, properties of distributions, expected values, setting confidence limits, risk and uncertainty, ANOVA (analysis of variance).
Some more advanced ideas
- Pivot tables, the Data Analysis Tool Pack, internet-based analysis tools, macros, dynamic spread sheets, sensitivity analysis.