Certified Data Analysis

Program Overview

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 certificati on 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.

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 customised 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.

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.

Participants’ profile

The course is designed for anyone who has basic mathematical training and basic competences in using Microsoft Excel. Statistical knowledge,intermediate or advanced knowledge of Excel, practical experience with data analysis and related duties are not necessary, but may contribute to a better understanding and more in-depth coverage of the course content. Diversity of participants’ background may help in a thorough coverage of the entire syllabus.
The course is addressed to managers, HR Representatives, analysts, auditors or logistics and acquisitions experts, as well as to professionals from other business areas, who deal with data analysis.
The course may also be a starting point for those interested in pursuing career opportunities in data analysis, data modelling and related activities (e.g.campaign management, data mining, statistics, risk management, reporting, data processing for survey analysis etc.).

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;
Attain superior results by implementing data analysis procedures.

Program Agenda

Day One: Understanding Data Analysis

Course context

Introduction of the participants;
Expectations setting;
Learning objectives formulation;
Course agenda presentation.

Data Analysis – The Basics

Definitions and utility of data analysis;
Data analysis process;
Realignment based on analysis;
Governance of data analysis.

Data quality

Data accuracy;
Logical inconsistencies;
Data sampling errors;
Data comparability;
Data completeness;
Economic/business interpretation of qualitative data.

Organizing, synthesizing and aggregating data

Data structure;
Challenges in aggregating data;
Data preparation;
Expert judgement;
Meta-analysis and evaluation synthesis;
Normalization of data.

Day 2 - Data Analysis

Day 3 - Advanced data analysis

Facilitator

Adrian Oţoiu
Subject Matter Expert
The KPI Institute

Adrian Otoiu is a subject matter expert for The KPI Institute and gained 15 years of experience in statistical, economic and business analysis in various roles within the government, academic organizations and multinational corporations.

Throughout his experience, he has garnered work expertise and had undergone training in the following fields: labor market, health economics, migration, quantitative marketing - including online surveys, composite indicators, default and risk models, business analytics and business intelligence, data preparation and processing, and teaching and coaching.

The work he has been performing includes doing statistical analysis by using the following main methods: regression analysis, including logistic analysis,panel data/hierarchical models, factor and PCA analysis, Bayesian regression analysis, cluster analysis, market basket analysis, decision trees, and natural language processing. Adrian has supplemented this process portfolio by adding data analysis and reports targeted at specific needs, including industry and competition analyses, SWOT and SBP studies, government briefings and notes, and academic paper and conference proceedings, retrieval of specialized data from various sources, presentation of results to nonspecialized audiences and fact-finding for high-level analyses.

In addition to statistical tasks, other significant work experiences includes analytics support in the form of back-to-back administration and processing of online surveys, extensive data processing for large data sets, processing of micro-data, retrieving, using and advising on the use of official statistics,setting-up custom statistical products and customizing reports and, last but not least, developing occasional scripts and tools for automating complex tasks.

These skills and abilities are supported by extensive experience and training in SAS and R, machine learning and modelling methods, backed by constant contact with the academia either in the form of hands-on research, continuous training offered by SAS and Johns Hopkins University Data Science programme and through teaching statistics and quantitative methods.

Learning experience

This part of the learning experience is meant to ensure a smooth transition to the face to face training. Participants are required to take the following steps:

Needs assessment – complete a questionnaire to determine a tailored and relevant learning experience;
Pre-course evaluation quiz - take a short quiz to establish your current level of knowledge;
Guidance and schedule - analyze a document presenting guidelines on how to maximize your learning experience;
Forum introduction – share an introduction message to present yourself to the other course participants;
Expectations – share your expectations regarding the training course;
Pre-requisite reading – go through a series of documents to better understand the core course content.

The Certified Data Analysis Professional training course provides an interactive practice-based learning environment in which participants focus on:

Establishing customized models for data analysis based on your organization’s requirements;
Gaining knowledge on basic (and advanced) data analysis concepts and statistical instruments;
Applying the knowledge gained in practical exercises, aimed at strengthening the learning process.

The learning process is not finalized when the core course ends. In order to benefit from a complete learning experience, participants are also required to take the following steps:

Forum discussions – initiate a discussion on the forum and contribute in a discussion opened by another participant;
Action plan – create a plan for the actions and initiatives you intend to implement after the training course;
In-house presentation – create and submit a short PowerPoint presentation to share the knowledge acquired within the training course with your colleagues;
Additional reading - go through a series of resources to expand your content related knowledge;
Learning journal: reflect upon your 3 stages learning experience and complete a journal.

The certification process is finalized only when you complete all 3 stages of the learning experience .Nonetheless,you will receive a:

Certificate of Completion (soft copy): after completing pre-course activities and passing the Certificati on Exam;
Certificate of Attendance (hard copy): after participating in the 3 days on-site training course;
Certified Data Analysis Professional Diploma (hard copy): after you have successfully completed all 3 stages of the learning experience.