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CERTIFIED DATA ANALYSIS PROFESSIONAL

- 1,250 USD -
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ONLINE

CERTIFIED DATA ANALYSIS PROFESSIONAL

The course provides you the knowledge required for understanding distinct methods used in analyzing data, statistical interpretation of quantitative and qualitative data, and becoming proficient in using key Microsoft Excel features, by building frequency and conditional tables, creating different types of charts, finding correlations and relationships between variables, hypothesis testing and statistical modeling.
 
key features KEY FEATURES
checked Modules: 15
checked Learning process estimated duration: 40 hours
checked Pre-course stage: 4 Hours
checked Course course stage: 24 hours, consisting of:
  • Video presentation: 3 hours
  • Practical assignments and exam preparation: 20 hours
  • Evaluation: 1 hour
checked After-course stage: 12 hours
checked Language: English
checked Instruction method: self-paced
checked Availability: anytime
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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!

Bonus: Premium subscription on smartKPIs.com - Available for 6 months, providing access to 500 fully documented KPIs and over 20.000 KPIs enlisted and one research report from the Top 25 KPIs series.

BENEFITS BENEFITS
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Obtaining the most relevant data, by setting up a customized data analysis process;
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Understanding the data analysis process, its methodology, and logical framework;
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Obtaining the necessary knowledge to analyze complex data and to interpret results;
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Improving the organization’s decision-making process, by gaining knowledge on data analysis and interpretation;
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• Receiving the management team’s buy-in, by sharing with them the utility of implementing a customized data analysis methodology in daily business activities.
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 Ágnes Ilyés
Subject Matter Expert
The KPI Institute

Ágnes holds valuable experience in data analysis, as during both her university and working years she had participated in numerous marketing related research projects where survey based primary researches were conducted and a lot of data were evaluated.

She mainly uses SPSS statistical program to analyze data. She has experience with the following analyses: Chi2 analysis, Variance analysis, Correlation analysis, t-test, factor- and cluster analysis.

Ágnes also deepened her knowledge by teaching interferential statistics as an external lecturer on the university, on economics and business administration faculty, marketing specialization.

As a Business Research Analyst at the KPI Institute she also has numerous possibilities to capitalize her experience in this field.

AGENDA AGENDA
Module 1
Business Understanding
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  • What Is Data Analysis?
  • Types of Data Analysis
  • Data Analysis Process
  • Data Governance
  • Data Analysis and its Benefits in Business.
  • Module 1 Review.
Module 2
Data Collection
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  • Types of Data
  • How to Collect Data?
  • Primary Data Collection Methods
  • Secondary Data Collection Methods
  • Module 2 Review.
Module 3
Data Preparation
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  • Types of Data Sets
  • Data Quality
  • Data Cleaning
  • Data Aggregation
  • Module 3 Review
Module 4
Data Exploration
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  • Frequency Tables
  • Quantitative Charts
  • Qualitative Charts
  • Structure Charts
  • Module 4 review.
Module 5
Descriptive Statistics
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  • Univariate Analysis
  • Bivariate Analysis
  • Module 5 Review.
Module 6
Sampling
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  • Population and sample
  • Why to Sample?
  • Sampling techniques
  • Sample Size Determination
  • Module 6 Review.
Module 7
Estimation of Population
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  • Sampling Distribution
  • Central Limit Theorem
  • Normal Distribution and T-Distributions
  • One-Tailed vs Two Tailed
  • Interval Estimation
  • Module 7 Review.
Module 8
Hypothesis Testing
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  • Hypothesis Testing Procedure
  • Types of Errors
  • Level of Significance
  • Test Statistic
  • Types of Hypothesis Testing
  • Module 8 Review.
Module 9
Z-Test and T-test
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  • Z-Test Statistics and T-Test Statistic
  • One Sample Hypothesis Testing
  • Two Independent Samples Hypothesis Testing
  • Paired Samples Hypothesis Testing
  • Module 9 Review.
Module 10
ANOVA Test
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  • When to perform ANOVA Test
  • F-Distribution
  • Three or more Independent Samples Hypothesis Testing
  • F-Statistic
  • Module 10 Review.
Module 11
Chi-Square Tests
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  • Chi-Square Test
  • Chi-Square Distribution
  • Goodness of Fit Test
  • Test of Independence
  • Module 11 Review.
Module 12
Regression Analysis
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  • Least Squares Method
  • Simple Linear Regression Model
  • Coefficient of determination and Correlation
  • Standardization
  • Homogeneity
  • Outliers
  • Module 12 Review.
Module 13
Multiple Regression
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  • Multiple Regression
  • Multiple Coefficient of Determination
  • Testing for Significance
  • Multicollinearity
  • Variance Inflation Factor
  • Module 13 Review.
Module 14
Time Series
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  • Trend Analysis
  • Cyclical Component
  • Seasonal Component
  • Irregular Component
  • Moving Average
  • Module 14 Review.
Module 15
Revision
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  • Business Understanding
  • Data Collection and Preparation
  • Data Exploration
  • Statistical Analysis
  • Regression Analysis
  • Time Series
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 15 modules represent a mix of videos and assignments that need to be realized either by matching, filling the blanks, or completing and submitting some templates. The slides corresponding to each video are available in PDF format. Also, each module ends with a quiz, meant at assessing the knowledge gained so far. The course ends by taking the online Certification Exam. It contains 75 questions and the necessary score to pass is minimum 50.
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.
arrow Achieve processes clarity and strategy optimization by implementing data analysis frameworks;
arrow Optimize the performance reporting processes by closing the gaps found in the data analysis tools;
arrow Attain superior results by implementing data analysis procedures.
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.
empty circle Evaluation
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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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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85,000
Community members
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Training hours
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Professionals trained
1,000
Educational programs
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Research reports
40
Countries
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Years of experience
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