Data Analytics
Self-Learning Course Info
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Course Details
Introduction
Objective
Who should attend
Have you ever wondered how to turn data into strategic decisions that propel your organization’s success? Data analysis is the key to gathering, modelling, and interpreting information to uncover insights that drive effective decision-making. It empowers organizations to position themselves in the market and outpace the competition.
The Data Analysis course provides you with the tools and practical expertise to analyze data and transform it into actionable insights. You’ll also learn how to effectively present your findings to support clear communication and strategic decision-making in your organization.
Are you ready to revolutionize the way you use data?
Self-learning Course Outline
Self-learning Course Content
Part 1
The Basics
- Sources of data, data sampling, data accuracy, data completeness, simple representations, and handling practical challenges.
Fundamental Statistics
- Mean, average, median, mode, rank, variance, covariance, standard deviation, "lies, more lies and statistics," adjustments for small sample sizes, descriptive statistics, and insensitive measures.
Part 2
Basics of Data Mining and Representation
- Single, two, and multi-dimensional data visualization, trend analysis, determining what to visualize, box and whisker charts, common errors and issues.
Data Comparison
- Correlation analysis, autocorrelation function, practical considerations of data set dimensionality, multivariate and non-linear correlation.
Part 3
Histograms and Frequency of Occurrence
- Histograms, Pareto analysis (sorted histogram), cumulative percentage analysis, law of diminishing returns, percentile analysis.
Part 4
Frequency Analysis
- Fourier transform periodic and aperiodic 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.
Part 5
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, online-based analysis tools, macros, dynamic spreadsheets, sand ensitivity analysis.
