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Certified Data Analysis Professional
Certified Data Analysis Professional
The Data Analysis Professional is a first level, hands-on training course aimed at equipping you with the necessary concepts and tools needed to perform primary statistical and analytics reporting activities, to generate value out of the existing data.
By attending this course, participants will gain both theoretical knowledge and practical skills in working with data. The information will enable them to understand better the meaning of data and the insights that it reveals.
- COURSE TYPE INTERMEDIATE
- COURSE NUMBER
- DURATION 3 days
- COURSE ACCREDITED BY Microsoft
You will learn how to
The course will provide you with the knowledge required for understanding distinct methods used in the interpretation of statistical data. Also, by attending this certificate on the program, you will be able to understand the underlying methodology used in the statistical analysis of quantitative data and become proficient in using key Microsoft Excel features, histograms, and Pareto Charts.
This course aims to improve the decision-making process through a rigorous data analysis within the company, as well as to enable managers and analysts to draw insights from both quantitative and qualitative data. Participants will understand, through practical learning, how to efficiently collect, analyze and interpret data for a better decision-making process, based on historical data and trend analysis.
IMPORTANT COURSE INFORMATION
Upon completing the course, participants will be awarded Microsoft Certificate in addition to the certificate they receive from Strategic Axis.
Module 1: Data Analysis – The Basics
• Definitions and utility of data analysis;
• Data analysis process;
• Realignment based on analysis;
• Governance of data analysis.
Module 2: Data quality
• Data accuracy;
• Logical inconsistencies;
• Data sampling errors;
• Data comparability;
• Data completeness;
• Economic/business interpretation of qualitative data.
Module 3: Organizing, synthesizing and aggregating data
• Data structure;
• Challenges in aggregating data;
• Data preparation;
• Expert judgement;
• Meta-analysis and evaluation synthesis;
• Normalization of data.
Module 4: Statistical analysis tools
• Statistical tools: mean, median & mode;
• Trend analysis: variance and standard deviation;
• Hypothesis testing;
• Statistical process control.
Module 5: Data visualization and pattern detection
• Single, two and multi-dimensional data visualization;
• Level, trend, seasonality and noise in time series data;
Module 6: Data comparison
• Analysis using histograms and Pareto Charts;
• Cumulative percentage analysis;
• Rules for interpreting data and formulating conclusions.
Module 7: Univariate and multivariate analysis
• Differences and complementarities in single and multivariate analyses;
• Techniques used in analyzing single variables;
• Techniques for analyzing relationships between variables (correlation analysis);
• Parametric vs. non-parametric techniques used for analysis.
Module 8: Regression analysis
• Linear and logistic regression;
• Assumptions and basic models;
• Diagnostic measures and uses;
• Nonlinear models using categorical data and other topics of interest.
Module 9: Probability and confidence
• Expected values and hypothesis testing;
• Contingency tables – ANOVA.
Module 10: From exploratory to predictive modelling
• Expected values;
• Confidence limits;
• Risk and uncertainty;
• Type 1 and type 2 errors;
• Tentative sensitivity analysis.
Module 11: Data dimensionality
• Compensation for small sample sizes;
• Big Data.
In The Classroom
Private Team Training
Indiviual Private Session
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