Certified Data Analysis Professional Online Course

  • ID: 4328443
  • Training
  • KPI Institute Pty. Ltd.
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Achieve Processes Clarity and Strategy Optimization by Implementing Data Analysis Frameworks

The Certified Data Analysis Professional is a first level, hands-on training course aimed at equipping you with the necessary concepts and tools needed to perform basic statistical and analytics reporting activities, in order to generate value out of the existing data.

The course will provide you with the knowledge required for understanding distinct methods used in the interpretation of statistical data. Also, by attending this certification program, you will be able to understand the basic methodology used in statistical interpretation of quantitative data and become proficient in using key Microsoft Excel features, histograms and Pareto Charts.

The course presents an overview of the basic data analysis concepts, such as data accuracy, preparation and comparability. Furthermore, the course provides both a general understanding upon basic analysis tools, such as mean, median and mode, as well as advanced operations, such as hypothesis testing or tentative sensitivity analysis.

3 Key business benefits

  • Achieve processes clarity and strategy optimization by implementing data analysis frameworks
  • Optimize the performance reporting processes by closing the gaps found in the data analysis tools used within your own business
  • Attain superior results by implementing data analysis procedures, which improve the achievement of your company’s objectives

5 Benefits

  • Improve the organization’s decision making process by gaining knowledge on data analysis and interpretation
  • Obtain the most relevant data you need by setting up a customized data analysis process
  • Achieve the management’s buy-in, by understanding the utility of implementing customized data analysis methodology in daily business activities
  • Provide a logical framework for understanding data analysis instruments
  • Obtain premium recognition as a Certified Data Analysis Professional, by completing a unique learning program

Learning objectives

  • Develop a hands-on, practical overview of data analysis and connected topics
  • Integrate statistical concepts and analysis tools that are widely used in corporate analytics environments
  • Analyze examples of practical applications for statistical methods used in solving real-life business issues
  • Acquire mastery of basic MS Excel and statistical techniques though practical examples
  • Solve complex problems, using intermediate and advanced Excel techniques

Evaluation

The certification process is finalized only when you complete all of the 3 stages of the learning experience. You will receive the Certified Data Analysis Professional diploma after you have successfully completed all of the 3 stages of the learning experience. This certifies the skills and knowledge related to performance measurement field.

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Module 1 - Basics of data analysis

  • Definitions and usefulness of data analysis
  • Data analysis process
  • Realignment and governance of data analysis
  • Module 1 Review

Module 2 - Data quality

  • Module 2 Introduction
  • Data completeness
  • Data accuracy
  • Logical inconsistencies
  • Data sampling errors
  • Data comparability
  • Economic/ business interpretation of qualitative data
  • Module 2 Review

Module 3 - Organizing, synthesizing and aggregating data

  • Data aggregation
  • Data preparation
  • Expert judgement
  • Meta-analysis and evaluation synthesis
  • Normalization of data
  • Module 3 Review

Module 4 - Statistical Analysis Tools

  • Statistical analysis tools: average, mean and median
  • Identifying and analyzing trends: variance and standards deviation
  • Hypothesis testing
  • Module 4 review

Module 5 - Data Visualization and Pattern Detection

  • Data visualization definition
  • Data visualization representations
  • Time series
  • Level, trend, seasonality and noise in time series data
  • Autocorrelation
  • Module 5 Review

Module 6 - Data Comparison

  • Module 6 Introduction
  • Analysis using histograms
  • Histograms using broadband data
  • Rules for interpreting data and formulating conclusions
  • Module 6 Review

Module 7 - Univariate and Multivariate Analysis

  • Module 7 Introduction
  • Univariate and Multivariate Analysis: Introduction
  • Descriptive measures of univariate data: Numeric variables
  • Multivariate analysis: introduction
  • Correlation analysis
  • Type of data and analysis options
  • Module 7 Review

Module 8 - Regression Analysis

  • Module 8 Introduction
  • Regression Analysis: introduction
  • Linear Regression Analysis: Assessing the Estimation
  • Autocorrelation, Heteroskedasticity, Multicollinearity
  • Linear Regression and Other Types of Regression Models
  • Types of Variables Used
  • Regression Analysis using Excel: ANALYSIS TOOLPACK
  • Regression Analysis: Initial Results and Final Results
  • Presenting and Interpreting Results
  • Regression Analysis: A Good Model
  • Module 8 Review

Module 9 - Probability and Confidence

  • Module 9 Introduction
  • Statistics and Probability: Overview
  • Probability and Confidence Intervals
  • Hypothesis Testing
  • Probability and Significance. P-values, Calculation and Meaning
  • Contingency Tables and The Chi Square
  • Anova
  • Anova single-factor analysis
  • Module 9 Review

Module 10 - From Exploratory to Predictive Modelling

  • Module 10 Introduction
  • Probability and Modelling
  • Confidence limits
  • Limits
  • Module 10 Review

Module 11 - Data Dimensionality

  • Module 11 Introduction
  • Data Dimensionality and Small Sample Sizes
  • The Student’s T test and the paired-sample test
  • Results for Paired T -Test
  • Big Data
  • Sampling Big Data with Excel
  • Sources of Error and Data Cleaning
  • Module 11 Review

Module 12 - Software Enablers for Data Analysis

  • Module 12 Introduction
  • Activate Analysis Toolpak in Excel
  • How to Use Data Analysis Tools
  • Module 12 Review
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Adrian Otoiu has a PhD in Statistics and is a Statistics Expert with over 20 years of experience, of which over 8 years are within the private sector, working for multinationals and governments using SAS, Excel, R, Eviews and SPSS. His key areas of expertise are business data analysis, decision model building, advanced statistics and research analysis. Adrian is an experienced trainer and facilitator, having conducted numerous face to face and online courses. Some of the organizations that benefitted from his expertise are: Ericsson, T-Mobile, Vodafone, ACNielsen, Ipsos Interactive Services, KPMG, The Fraser Institute or the Ontario Ministry of Finance. Adrian is the author of over 25 research publications on data analysis and business modelling.

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