Events

EVENT CONTENT

Introduction

A Certified Data Analyst (CDA™) successfully applies process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making

Program dates are September 18, 19, 25, 26 & October 2, 3, 9, 10, 16, 17, 2021| 8:00 AM to 12:00 NN Philippine Standard Time (GMT+8).

Program Rationale

The role of a Certified Data Analyst is to apply the Data Management, Data Visualization, Data Statistics and Operational Analytics in their organization! Use of R-Studio Programming Language to handle big data and basic Tableau for Viz and create project in their organization using the tools that were acquired.

Target Participants

This program is suitable for professionals who want to further his knowledge, skills, and abilities on the utilization of Data Science methodologies, tools, and techniques covering problem definition, problem analysis, data modeling, insights generation, and data training.

Program Schedule

Phase 1 – Data Management
Live Class: June 19 and 20, 2021; 0800H-1200H via Zoom

Phase 2 – Data Visualization
Live Class: June 26 and 27, 2021; 0800H-1200H via Zoom

Phase 3 – Data Statistics
Live Class: July 3 and 10, 2021; 0800H-1200H via Zoom

Phase 4a/4b- Operational Analytics I and II
Live Class: July 17 and 18, 2021; 0800H-1200H via Zoom

Phase 4c/4d- Operational Analytics III and IV
Live Class: July 24 and 25, 2021; 0800H-1200H via Zoom

Phase 5 – Exam
Certification Exam Review: July 25, 2021; 1200H-0800H (Online)
Certification Exam: July 31, 2021; Morning
Recognition Rites: July 31, 2020; Evening

How to Get Certified

Each candidate will be certified by completing the training + e-learning, submitting the case study, successfully passing the exam and implementing an actual Data Analysis Capstone project. This certification automatically includes a FREE R – Studio Basic Level Certification upon completing its requirements.

Certified Data Analyst Program Outline

I. Phase 0: Introduction to Data Analysis

A. INTRODUCTION TO DATA SCIENCE
1. Explain Business Decision and Analytics
2. List the types of business analytics
3. Discuss the application of business analytics
4. Describe data science

B. ROLES OF DATA ANALYST IN INDUSTRY 4.0
1. CDA Roles and Opportunities

II. Phase 1: Data Management

A. Introduction to R programming
1. Importance of R and significance to data analytics
2. Data types and variables in R
3. Types of R operators
4. Different types and conditional Statements in R
5. Different types of loops in R
6. Methods to Run an R script
7. Commonly used R functions

B. Data Structure
1. How to Identify data structures in R
2. Assign Values to data Structures
3. How to manipulate data using dplyr package

III. Phase 2: Data Visualization

A. DATA VISUALIZATION USING R
1. Describe Data visualization
2. List the graphics used for data visualization in R Explain ggplot with examples
3. Discuss file formats of graphic outputs

B. DATA VISUALIZATION USING TABLEAU 10
1. Describe the components and terminologies of Tableau Describe Desktop application, view, and data pages
2. Explain the fields generated by Tableau automatically

IV. Phase 3: Data Statistics

A. DATA STATISTICS I
1. Define Hypothesis
2. Explain Data Sampling
3. Confidence and Significance Levels

B. DATA STATISTICS II
1. Hypothesis Testing
2. Parametric Tests and Types
3. Non – Parametric Tests and Types
4. Hypothesis Tests on Population Means
5. Hypothesis Tests on Population Variance
6. Hypothesis Tests on Population Proportions

V. Phase 4: Operational Analytics

A.REGRESSION ANALYSIS
1. Explain the meaning and uses of regression analysis
2. Different Types of Regression Analysis Models
3. Functions to convert non-linear models to linear models
4. R –sq and adj R-sq Models
5. Principal Component Analysis and Factor Analysis of Dimensionality Reduction

B. CLASSIFICATION
1. Classification and the types of classifications algorithms
2. Logistic Regression
3. Support Vector Machines
4. K-Nearest Neighbors (KNN)
5. Naïve Bayes Classifier
6. Decision Tree and Random Forest Classification
7. Evaluation of classifier models

C. CLUSTERING
1. Define Clustering
2. List of Clustering Methods
3. Control charts

D. ASSOCIATION
1. Explain Association Rule
2. Apriori algorithm and application steps

VI. Phase 5: Capstone Project

Program Feedback ⭐⭐⭐⭐⭐

“ lots of resources, both recorded videos and ebooks for self paced learning . Live classes were engaging and not boring and good course for starters, covered lots of topics and gained developing skill in short span of time” – Angela Nebres

“In this class, one of the most important lesson I’ve learned is the importance of accuracy of data and its structure to be able to manage it with good results.“ – Khristelle Urgelles

Join our program and get these perks!

✅ Lifetime Access to our eLMS
✅ 4 Live Classes + learning packages
✅ FREE R – Studio Programming Basic Course Certificate
✅ Per Module Certificate of Completion
✅ Certification Review & Examination
✅ Unlimited Project Coaching Hours
✅ Data Analyst Training Completion Certificate
✅ ALPHA Data Analyst Certification (TM)
✅ Almost 20 Million worth of Data to work during the course
✅ 30+hrs of Self Paced Lectures
✅ Limited Edition Reference Materials + Many more!

Learning Investment

✅ Regular Price: Php 30,000
✅ Modular: Php 9,000 (Total of 4 Modules)
✅ Discounted Rate: Php 15,000

Payment Terms

You will only need to pay at least P5,000 to reserve your seat and balance is payable until a week before the graduation. Send a copy of your proof of payment and 2 x 2 ID picture in white background to info@asklexph.com using the subject ALPHA_CDA – Surname

Register Here!