/  Data Analysis Techniques Training

Data Analysis Techniques Training

BMC Training provides a training course in Data Analysis Techniques in Maintenance Engineering Training

Course Title
Venue
Start Date
End Date
  • Istanbul
    21 - 8 - 2022
    25 - 8 - 2022
  • Dubai
    21 - 8 - 2022
    25 - 8 - 2022
  • Paris
    21 - 8 - 2022
    25 - 8 - 2022
  • London
    28 - 8 - 2022
    1 - 9 - 2022
  • Dubai
    28 - 8 - 2022
    1 - 9 - 2022
  • Paris
    28 - 8 - 2022
    1 - 9 - 2022
  • Kuala Lumpur
    4 - 9 - 2022
    8 - 9 - 2022
  • Dubai
    11 - 9 - 2022
    15 - 9 - 2022
  • Paris
    11 - 9 - 2022
    15 - 9 - 2022
  • London
    18 - 9 - 2022
    22 - 9 - 2022
  • Istanbul
    25 - 9 - 2022
    29 - 9 - 2022
  • Dubai
    25 - 9 - 2022
    29 - 9 - 2022
  • Paris
    25 - 9 - 2022
    29 - 9 - 2022
  • Istanbul
    2 - 10 - 2022
    6 - 10 - 2022
  • Dubai
    2 - 10 - 2022
    6 - 10 - 2022
  • Paris
    2 - 10 - 2022
    6 - 10 - 2022
  • London
    9 - 10 - 2022
    13 - 10 - 2022
  • Dubai
    9 - 10 - 2022
    13 - 10 - 2022
  • Paris
    9 - 10 - 2022
    13 - 10 - 2022
  • Dubai
    16 - 10 - 2022
    20 - 10 - 2022
  • Paris
    16 - 10 - 2022
    20 - 10 - 2022
  • Kuala Lumpur
    23 - 10 - 2022
    27 - 10 - 2022
  • Dubai
    6 - 11 - 2022
    10 - 11 - 2022
  • Paris
    6 - 11 - 2022
    10 - 11 - 2022
  • London
    13 - 11 - 2022
    17 - 11 - 2022
  • Istanbul
    20 - 11 - 2022
    24 - 11 - 2022
  • Dubai
    20 - 11 - 2022
    24 - 11 - 2022
  • Paris
    20 - 11 - 2022
    24 - 11 - 2022
  • Kuala Lumpur
    27 - 11 - 2022
    1 - 12 - 2022
  • London
    4 - 12 - 2022
    8 - 12 - 2022
  • Dubai
    4 - 12 - 2022
    8 - 12 - 2022
  • Paris
    4 - 12 - 2022
    8 - 12 - 2022
  • Istanbul
    11 - 12 - 2022
    15 - 12 - 2022
  • Dubai
    11 - 12 - 2022
    15 - 12 - 2022
  • Paris
    11 - 12 - 2022
    15 - 12 - 2022
  • Dubai
    18 - 12 - 2022
    22 - 12 - 2022
  • Paris
    18 - 12 - 2022
    22 - 12 - 2022
  • Kuala Lumpur
    25 - 12 - 2022
    29 - 12 - 2022
  • London
    1 - 1 - 2023
    5 - 1 - 2023
  • Dubai
    8 - 1 - 2023
    12 - 1 - 2023
  • Paris
    8 - 1 - 2023
    12 - 1 - 2023
  • Kuala Lumpur
    15 - 1 - 2023
    19 - 1 - 2023
  • Istanbul
    22 - 1 - 2023
    26 - 1 - 2023
  • Dubai
    22 - 1 - 2023
    26 - 1 - 2023
  • Paris
    22 - 1 - 2023
    26 - 1 - 2023
  • London
    5 - 2 - 2023
    9 - 2 - 2023
  • Dubai
    5 - 2 - 2023
    9 - 2 - 2023
  • Paris
    5 - 2 - 2023
    9 - 2 - 2023
  • Istanbul
    12 - 2 - 2023
    16 - 2 - 2023
  • Dubai
    12 - 2 - 2023
    16 - 2 - 2023
  • Paris
    12 - 2 - 2023
    16 - 2 - 2023
  • Kuala Lumpur
    19 - 2 - 2023
    23 - 2 - 2023
  • Dubai
    26 - 2 - 2023
    2 - 3 - 2023
  • Paris
    26 - 2 - 2023
    2 - 3 - 2023
  • Dubai
    5 - 3 - 2023
    9 - 3 - 2023
  • Paris
    5 - 3 - 2023
    9 - 3 - 2023
  • Kuala Lumpur
    12 - 3 - 2023
    16 - 3 - 2023
  • Istanbul
    19 - 3 - 2023
    23 - 3 - 2023
  • Dubai
    19 - 3 - 2023
    23 - 3 - 2023
  • Paris
    19 - 3 - 2023
    23 - 3 - 2023
  • London
    26 - 3 - 2023
    30 - 3 - 2023
  • Kuala Lumpur
    2 - 4 - 2023
    6 - 4 - 2023
  • Istanbul
    9 - 4 - 2023
    13 - 4 - 2023
  • Dubai
    9 - 4 - 2023
    13 - 4 - 2023
  • Paris
    9 - 4 - 2023
    13 - 4 - 2023
  • London
    16 - 4 - 2023
    20 - 4 - 2023
  • Dubai
    16 - 4 - 2023
    20 - 4 - 2023
  • Paris
    16 - 4 - 2023
    20 - 4 - 2023
  • Dubai
    23 - 4 - 2023
    27 - 4 - 2023
  • Paris
    23 - 4 - 2023
    27 - 4 - 2023
  • Kuala Lumpur
    7 - 5 - 2023
    11 - 5 - 2023
  • Istanbul
    14 - 5 - 2023
    18 - 5 - 2023
  • Dubai
    14 - 5 - 2023
    18 - 5 - 2023
  • Paris
    14 - 5 - 2023
    18 - 5 - 2023
  • Dubai
    21 - 5 - 2023
    25 - 5 - 2023
  • Paris
    21 - 5 - 2023
    25 - 5 - 2023
  • London
    28 - 5 - 2023
    1 - 6 - 2023
  • London
    4 - 6 - 2023
    8 - 6 - 2023
  • Dubai
    4 - 6 - 2023
    8 - 6 - 2023
  • Paris
    4 - 6 - 2023
    8 - 6 - 2023
  • Dubai
    11 - 6 - 2023
    15 - 6 - 2023
  • Paris
    11 - 6 - 2023
    15 - 6 - 2023
  • Kuala Lumpur
    18 - 6 - 2023
    22 - 6 - 2023
  • Istanbul
    25 - 6 - 2023
    29 - 6 - 2023
  • Dubai
    25 - 6 - 2023
    29 - 6 - 2023
  • Paris
    25 - 6 - 2023
    29 - 6 - 2023
  • Istanbul
    2 - 7 - 2023
    6 - 7 - 2023
  • Dubai
    2 - 7 - 2023
    6 - 7 - 2023
  • Paris
    2 - 7 - 2023
    6 - 7 - 2023
  • Kuala Lumpur
    9 - 7 - 2023
    13 - 7 - 2023
  • Kuala Lumpur
    16 - 7 - 2023
    20 - 7 - 2023
  • London
    23 - 7 - 2023
    27 - 7 - 2023
  • Dubai
    6 - 8 - 2023
    10 - 8 - 2023
  • Paris
    6 - 8 - 2023
    10 - 8 - 2023
  • Kuala Lumpur
    13 - 8 - 2023
    17 - 8 - 2023

Introduction

Corporate ethos which demands continual improvement in work place efficiencies and reduced operating, maintenance, support service and administration costs means that managers, analysts and their advisors are faced with ever-challenging analytical problems and performance targets. To make decisions which result in improved business performance it is vital to base decision making on appropriate analysis and interpretation of numerical data.

Objectives                                                   

This course aims to provide those involved in analysing numerical data with the understanding and practical capabilities needed to convert data into information via appropriate analysis, and then to represent these results in ways that can be readily communicated to others in the organisation.

Objectives include:

  • 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 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack.

Content

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 visualisation, 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.

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