Data Analysis Methods and Techniques
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Data Analysis Methods and Techniques Course
Introduction:
Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis.
Data analysis, therefore, is a necessity for making well-informed and efficient decisions. Data analysis is what helps organizations determine their positions in the market relative to competitors.
The data Analysis Techniques training course will give you broad exposure to key technologies and skills currently used in data analytics and data science, it aims to provide those involved in analyzing numerical data with the understanding and practical capabilities needed to convert data into information via appropriate analysis, and then represent these results in ways that can be readily communicated to others in the 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 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, and 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, and 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 fit, 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 spreadsheets, sensitivity analysis.