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CERTIFIED DATA ANALYSIS PROFESSIONAL

- 1,499 USD -
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ONLINE

CERTIFIED DATA ANALYSIS PROFESSIONAL

This course provides perspective into this complex, fast-moving field by equipping you with the necessary concepts and tools needed to perform statistical and analytics activities, to generate value out of the existing data.
 
key features KEY FEATURES
checked Modules: 12
checked Video presentation: 3.45 hours
checked Practical assignments: Approx. 14 Hours
checked Evaluation: Approx. 2 Hours
checked Language: English
checked Instruction method: self-paced
checked Availability: anytime
checked Prerequisite: not needed
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GET CERTIFIED WITH US GET CERTIFIED WITH US
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The Data Analysis Certification is an accreditation that endorses you both for the knowledge and practical application of best practices used in analyzing statistical data.

The certification is the result of a complex, experiential learning program that has 3 sections: pre-course activities, core-course exercises and post-course assignments.

You will acquire the tools and skills needed to develop complex data analysis, useful for the processing and interpretation of data and relevant for your company's profile.

Validate your expertise!

BENEFITS BENEFITS
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Achieve processes clarity and strategy optimization by implementing data analysis frameworks;        
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Optimize the performance reporting processes by closing the gaps found in the data analysis tools used within your own business;
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Attain superior results by implementing data analysis procedures, which improve the achievement of your company's objectives.
TARGET AUDIENCE TARGET AUDIENCE
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Professionals interested in Data Analysis

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.

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Management Representatives

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.

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Data Analysis Experts

The course is ideal 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.)

WHAT OUR CLIENTS SAY WHAT OUR CLIENTS SAY
FACILITATOR FACILITATOR
man Adrian Oțoiu
Subject Matter Expert
The KPI Institute

Adrian Otoiu gained more than 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, 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 his 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 non-specialized audiences and fact-finding for high-level analyses.

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 program, and through teaching statistics and quantitative methods.

AGENDA AGENDA
Module 1
Basics of data analysis
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  • Definitions and usefulness of data analysis;
  • Data analysis process;
  • Realignment and governance of data analysis;
  • Module 1 Review.
Module 2
Data quality
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  • 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
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  • Data aggregation;
  • Data preparation;
  • Expert judgement;
  • Meta-analysis and evaluation synthesis;
  • Normalization of data;
  • Module 3 Review
Module 4
Statistical Analysis Tools
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • Module 10 Introduction;
  • Probability and Modelling;
  • Confidence limits;
  • Limits;
  • Module 10 Review.
Module 11
Data Dimensionality
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  • 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
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  • Module 12 Introduction;
  • Activate Analysis Toolpak in Excel;
  • How to Use Data Analysis Tools;
  • Module 12 Review.
LEARNING EXPERIENCE LEARNING EXPERIENCE
empty circle Pre course
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:
arrow Needs assessment - complete a questionnaire to determine a tailored and relevant learning experience;
arrow Pre-course evaluation quiz - take a short quiz to establish your current level of knowledge;
arrow Guidance and schedule - analyze a document presenting guidelines on how to maximize your learning experience;
arrow Forum introduction - share an introduction message to present yourself to the other course participants;
arrow Expectations - share your expectations regarding the training course;
arrow Pre-requisite reading - go through a series of documents to better understand the core course content.
empty circle Core course
The Certified Data Analysis Professional training course provides an interactive practice-based learning environment in which participants focus on:
arrow Establishing customized models for data analysis based on your organization's requirements;
arrow Gaining knowledge on basic (and advanced) data analysis concepts and statistical instruments;
arrow Applying the knowledge gained in practical exercises, aimed at strengthening the learning process.
empty circle After course
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:
arrow Forum discussions - initiate a discussion on the forum and contribute in a discussion opened by another participant;
arrow Action plan - create a plan for the actions and initiatives you intend to implement after the training course;
arrow In-house presentation - create and submit a short PowerPoint presentation to share the knowledge acquired within the training course with your colleagues;
arrow Additional reading - go through a series of resources to expand your content related knowledge;
arrow Learning journal: reflect upon your 3 stages learning experience and complete a journal.
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The certification process is finalized only when you complete all the 3 stages of the learning experience. You will receive the Certified Data Analysis Professional diploma after you have successfully completed all the 3 stages of the learning experience. This certifies the skills and knowledge related to performance measurement field.
FAQ FAQ
FAQ
What is the difference between the face-to-face course and the online course?
The course structure is the same, regardless of the delivery method. However, the autonomy given by the self-paced instruction is balanced by the lack of live interaction with the facilitator and other participants while attending.
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FAQ
What are the prerequisites for taking a course?
There are no prerequisites required.
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FAQ
Who should I contact if I have questions throughout the course?

You can communicate with your instructor through the eLearning forum or by email. You are highly encouraged to use these ways of communications.

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FAQ
How do I register for an online course?
You can register for an online course the same way you would register for any training course organized by The KPI Institute.
If you enroll through The KPI Institute Marketplace, after paying the course fee, you will receive a confirmation email containing the instructions and the credentials to access your online course.
If you enroll by fax or telephone, you will also receive your confirmation and instructions by email.
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FAQ
Where and how can I access the e-learning content? Are there any limitations?
Once you register with us for a course by paying the course fee, you can have 24/7 access to the e-learning content on our website. An automated course purchase confirmation email from our side will guide you through the process.
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FAQ
How can I download course materials?
Downloadable course materials are available on our eLearning Platform. These include course slides, assignment templates and supplemental documents that will help you brush up or dive deeper on concepts within that course. However, video presentations are not available for download.
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FAQ
What kind of computer equipment do I need in order to take an online course?

Operating Systems: Windows 7 and newer, Mac OSX 10.6 and newer, Linux - chromeOS.

Browsers: You must update to the newest version of whatever browser you are using. We recommend using Chrome, Firefox or Safari, beta versions of browsers are not supported and Internet Explorer is problematic. Please make sure that your web browser has JavaScript and cookies enabled.

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FAQ
Who do I contact for technical support?
If you are experiencing technical difficulties, contact our Customer Service team: office@kpiinstitute.org or +61 3 9028 2223
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ABOUT THE KPI INSTITUTE ABOUT THE KPI INSTITUTE
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Educational programs
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Research reports
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Years of experience
CLIENTS CLIENTS
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