BACK

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Data Analytics

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GOOGLE ADVANCED DATA ANALYTICS PROFESSIONAL CERTIFICATE

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TABLE OF CONTENTS

COURSE 1: FOUNDATIONS OF DATA SCIENCE [데이터 과학의 기초]

⇒ The Impact of Data Today [오늘날 데이터의 영향력] ⇐

⇒ Career as a Data Professional [데이터 전문가로서의 커리어] ⇐

⇒ Data Applications and Workflow [데이터 활용 사례 및 작업흐름] ⇐

COURSE 2: GO BEYOND THE NUMBERS - TRANSLATE DATA INTO INSIGHTS [데이터를 통찰로 전환]

⇒ Find and Share Stories Using Data [데이터를 활용한 스토리텔링과 공유] ⇐

⇒ Explore Raw Data [원천 데이터 탐색] ⇐

⇒ Clean your Data [데이터 정제] ⇐

⇒ Data Visualizations and Presentations [데이터 시각화 및 프레젠테이션] ⇐

COURSE 3: THE POWER OF STATISTICS [통계의 힘]

⇒ Introduction to Statistics With Python [파이썬을 활용한 통계] ⇐

⇒ Probability [확률] ⇐

⇒ Sampling [표본 추출] ⇐

⇒ Confidence Intervals [신뢰 구간] ⇐

⇒ Introduction to Hypothesis Testing [가설 검정] ⇐

COURSE 4: REGRESSION ANALYSIS - SIMPLIFY COMPLEX DATA RELATIONSHIPS [회귀 분석]

⇒ Introduction to Complex Data Relationships [복잡한 데이터 관계 입문] ⇐

⇒ Simple Linear Regression [단순 선형 회귀] ⇐

⇒ Multiple Linear Regression [다중 선형 회귀] ⇐

⇒ Advanced Hypothesis Testing [심화 가설 검정] ⇐

⇒ Logistic Regression [로지스틱 회귀] ⇐

COURSE 5: THE NUTS AND BOLTS OF MACHINE LEARNING [머신러닝의 핵심 부품과 작동 원리]

⇒ The Different Types of Machine Learning [머신러닝의 다양한 유형] ⇐

⇒ Workflow for Building Complex Models [복잡한 모델 구축을 위한 작업흐름] ⇐

⇒ Unsupervised Learning Techniques [비지도 학습] ⇐

⇒ Tree-Based Modeling [트리 기반 모델링] ⇐

COURSE 1: FOUNDATIONS OF DATA SCIENCE [데이터 과학의 기초]

TABLE OF CONTENTS

THE IMPACT OF DATA TODAY

⇒ Data-Driven Careers: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Career Skills: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Work in the Field: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

CAREER AS A DATA PROFESSIONAL

⇒ Trajectory of the Field Pt. A: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Trajectory of the Field Pt. B: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

DATA APPLICATIONS AND WORKFLOW

⇒ Data Project Workflow: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Elements of Communication: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Professional Communication: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

THE IMPACT OF DATA TODAY [오늘날 데이터의 영향력]

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CAREER AS A DATA PROFESSIONAL [데이터 전문가로서의 커리어]

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DATA APPLICATIONS AND WORKFLOW [데이터 활용 사례 및 작업흐름]

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COURSE 2: GO BEYOND THE NUMBERS - TRANSLATE DATA INTO INSIGHTS [데이터를 통찰로 전환]

TABLE OF CONTENTS

FIND AND SHARE STORIES USING DATA

⇒ Use Pace to Inform EDA and Data Visualization Pt. A: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Use Pace to Inform EDA and Data Visualization Pt. B: _________________ | _________________ | _________________ | _________________ ⇐

EXPLORE RAW DATA

⇒ Discovering is the Beginning of an Investigation: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Understand Data Format: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Create Structure From Raw Data: _________________ | _________________ | _________________ | _______________ | _______________ ⇐

CLEAN YOUR DATA

⇒ The Challenge of Missing or Duplicate Data: _________________ | _________________ | _________________ | _________________ ⇐

⇒ The Ins and Outs of Data Outliers: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Change Categorical Data to Numerical Data: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Input Validation: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

DATA VISUALIZATIONS AND PRESENTATIONS

⇒ Present a Story: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Advanced Tableau A: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Advanced Tableau Pt. B: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

FIND AND SHARE STORIES USING DATA [데이터를 활용한 스토리텔링과 공유]

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EXPLORE RAW DATA [원천 데이터 탐색]

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CLEAN YOUR DATA [데이터 정제]

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DATA VISUALIZATIONS AND PRESENTATIONS [데이터 시각화 및 프레젠테이션]

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COURSE 3: THE POWER OF STATISTICS [통계의 힘]

TABLE OF CONTENTS

INTRODUCTION TO STATISTICS WITH PYTHON

⇒ Introduction to Statistics: THE ROLE OF STATISTICS IN DATA SCIENCE | Descriptive Statistics vs. Inferential Statistics ⇐

⇒ Descriptive Statistics: MEASURES OF CENTRAL TENDENCY | MEASURES OF DISPERSION | MEASURES OF POSITION ⇐

⇒ Calculate Statistics With Python: COMPUTE DISCRIPTIVE STATISTICS IN PYTHON WITH NUMPY, PANDAS, MATPLOTLIB ⇐

PROBABILITY

⇒ Basic Concepts of Probability: Objective vs. Subjective Probability | PRINCIPLES OF PROBABILITY | BASIC RULES OF PROBABILITY AND EVENTS ⇐

⇒ Conditional Probability: CONDITIONAL PROBABILITY FOR DEPENDENT EVENTS | BAYES' THEOREM FOR CONDITIONAL PROBABILITY ⇐

⇒ Discrete Probability Distributions: Introduction to Probability Distributions | THE BINOMIAL DISTRIBUTION | THE POISSON DISTRIBUTION ⇐

⇒ Continuous Probability Distributions: THE NORMAL DISTRIBUTION AND THE EMPIRICAL RULE | STANDARD DATA USING Z-SCORES ⇐

⇒ Probability Distributions With Python: WORK WITH PROBABILITY DISTRIBUTIONS IN PYTHON WITH SCIPY AND STATSMODEL ⇐

SAMPLING

⇒ Introduction to Sampling: Population and Sampling | Sampling Process | PROBABILITY SAMPLING METHODS | NON-PROBABILITY SAMPLING METHODS ⇐

⇒ Sampling Distributions: How Sampling Affects Data | THE CENTRAL LIMIT THEOREM | SAMPLING DISTRIBUTION AND STANDARD ERROR ⇐

⇒ Work With Sampling Distributions in Python: WORK WITH SAMPLING DISTRIBUTIONS IN PYTHON WITH SCIPY AND STATSMODEL ⇐

CONFIDENCE INTERVALS

⇒ Introduction to Confidence Intervals: CONFIDENCE INTERVALS FOR FREQUENTIST STATISTICS | INTERPRETATIONS OF CONFIDENCE INTERVALS ⇐

⇒ Construct Confidence Intervals: CONSTRUCT CONFIDENCE INTERVAL USING Z-SCORES | CONSTRUCT CONFIDENCE INTERVAL USING T-SCORES ⇐

⇒ Work With Confidence Intervals in Python: CONSTUCT SAMPLING DISTRIBUTIONS IN PYTHON WITH NUMPY, PANDAS, SCIPY ⇐

INTRODUCTION TO HYPOTHESIS TESTING

⇒ Hypothesis Testing: NULL HYPOTHESIS AND ALTERNATIVE HYPOTHESIS | CONFIDENCE LEVEL AND P-VALUE | TYPE I AND TYPE II ERRORS ⇐

⇒ One-Sample Tests: ONE-SAMPLE TEST AND STATISTICAL SIGNIFICANCE | Example of One-Sample Test ⇐

⇒ Two-Sample Tests: TWO-SAMPLE TESTS | ONE-TAILED AND TWO-TAILED TESTS | AB Testing | Experimental Design ⇐

⇒ Hypothesis Testing With Python: CONDUCT A HYPOTHESIS TEST IN PYTHON WITH PANDAS AND SCIPY ⇐

INTRODUCTION TO STATISTICS WITH PYTHON [파이썬을 활용한 통계]

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PROBABILITY [확률]

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SAMPLING [표본 추출]

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CONFIDENCE INTERVALS [신뢰 구간]

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INTRODUCTION TO HYPOTHESIS TESTING [가설 검정]

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COURSE 4: REGRESSION ANALYSIS - SIMPLIFY COMPLEX DATA RELATIONSHIPS [회귀 분석]

TABLE OF CONTENTS

INTRODUCTION TO COMPLEX DATA RELATIONSHIPS

⇒ Introduction to Linear Regression: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Introduction to Logistic Regression: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

SIMPLE LINEAR REGRESSION

⇒ Foundations of Linear Regression: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Assumptions and Construction in Python: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Evaluate a Linear Regression Model: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Interpret Linear Regression Results: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

MULTIPLE LINEAR REGRESSION

⇒ Understand Multiplier Linear Regression: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Model Assumptions Revisited: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Model Interpretation: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Variable Selection and Model Evaluation: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

ADVANCED HYPOTHESIS TESTING

⇒ The Chi-Squared Test: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Analysis of Variance: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ ANCOVA, MANOVA, MANCOVA: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

LOGISTIC REGRESSION

⇒ Foundations of Logistic Regression: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Logistic Regression With Python: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Interpret Logistic Regression Results: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Compare Regression Models: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

INTRODUCTION TO COMPLEX DATA RELATIONSHIPS [복잡한 데이터 관계 입문]

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SIMPLE LINEAR REGRESSION [단순 선형 회귀]

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MULTIPLE LINEAR REGRESSION [다중 선형 회귀]

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ADVANCED HYPOTHESIS TESTING [심화 가설 검정]

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LOGISTIC REGRESSION [로지스틱 회귀]

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COURSE 5: THE NUTS AND BOLTS OF MACHINE LEARNING [머신러닝의 핵심 부품과 작동 원리]

TABLE OF CONTENTS

THE DIFFERENT TYPES OF MACHINE LEARNING

⇒ Main Types of Machine Learning: SUPERVISED LEARNING | UNSUPERVISED LEARNING | _________________ | _________________ ⇐

⇒ Categorical vs. Continuous Data Types and Models: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Machine Learning in Everyday Life: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Ethics in Machine Learning: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Utilizing Python Toolbelt for Machine Learning: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Machine Learning Resources for Data Professionals: _________________ | _________________ | _________________ | _________________ ⇐

WORKFLOW FOR BUILDING COMPLEX MODELS

⇒ PACE in Machine Learning - Plan and Analyze: _________________ | _________________ | _________________ | _________________ ⇐

⇒ PACE in Machine Learning - Construct and Execute: _________________ | _________________ | _________________ | _________________ ⇐

UNSUPERVISED LEARNING TECHNIQUES

⇒ Explore Unsupervised Learning and K-Means: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Evaluate K-Means Model: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

TREE-BASED MODELING

⇒ Additional Supervised Learning Techniques: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Tune Tree-Based Models: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Bagging: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Boosting: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

THE DIFFERENT TYPES OF MACHINE LEARNING [머신러닝의 다양한 유형]

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WORKFLOW FOR BUILDING COMPLEX MODELS [복잡한 모델 구축을 위한 작업흐름]

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UNSUPERVISED LEARNING TECHNIQUES [비지도 학습]

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TREE-BASED MODELING [트리 기반 모델링]

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GOOGLE DATA ANALYTICS PROFESSIONAL CERTIFICATE

Coursera L2FWT4V7UQFW.pdf

TABLE OF CONTENTS

COURSE 1: FOUNDATIONS - DATA, DATA, EVERYWHERE [데이터에 관한 기초]

⇒ Introducing Data Analytics and Analytical Thinking [데이터 분석 및 분석적 사고] ⇐

⇒ The Wonderful World of Data [방대한 데이터의 세계] ⇐

⇒ Set-up Your Data Analytics Toolbox [데이터 분석 도구함 설정] ⇐

⇒ Become a Fair and Impactful Data Professional [공정하고 영향력 있는 데이터 전문가] ⇐

COURSE 2: ASKING QUESTIONS TO MAKE DATA-DRIVEN DECISIONS [데이터 기반 의사 결정을 위한 질문]

⇒ Ask Effective Questions [효과적인 질문] ⇐

⇒ Make Data-Driven Decisions [데이터 기반 의사결정] ⇐

⇒ Spreadsheet Magic [스프레드시트의 마법] ⇐

⇒ Always Remember the Stakeholder [이해관계자 고려] ⇐

COURSE 3: PREPARE DATA FOR EXPLORATION [데이터 탐색을 위한 준비]

⇒ Data Types and Structures [데이터 타입 및 구조] ⇐

⇒ Data Responsibility [데이터 책임 윤리] ⇐

⇒ Database Essentials [데이터베이스 필수 지식] ⇐

⇒ Organize and Protect Data [데이터 조직화 및 보호] ⇐

COURSE 4: PROCESS DATA FROM DIRTY TO CLEAN [불완전한 데이터에서 정제된 데이터로]

⇒ The Importance of Integrity [데이터 무결성의 중요성] ⇐

⇒ Clean Data for More Accurate Insights [정확한 통찰을 위한 데이터 정제] ⇐

⇒ Data Cleaning With SQL [SQL을 활용한 데이터 정제] ⇐

⇒ Verify and Report on Cleaning Results [정제 결과 검증 및 보고] ⇐

COURSE 5: ANALYZE DATA TO ANSWER QUESTIONS [질문에 답하기 위한 데이터 분석]

⇒ Organize Data for More Effective Analysis [분석을 위한 데이터 조직화] ⇐

⇒ Format and Adjust Data [데이터 포맷 설정 및 조정] ⇐

⇒ Aggregate Data for Analysis [분석을 위한 데이터 요약 및 집계] ⇐

⇒ Perform Data Calculations [데이터 계산 프로세스 수행] ⇐

COURSE 6: SHARE DATA THROUGH THE ART OF VISUALIZATION [시각화 기술을 통한 데이터 공유]

⇒ Visualize Data [데이터 시각화] ⇐

⇒ Create Data Visualizations With Tableau [태블로를 활용한 데이터 시각화] ⇐

⇒ Craft Data Stories [데이터 스토리 설계] ⇐

⇒ Develop Presentations and Slideshows [프레젠테이션 및 슬라이드 제작] ⇐

COURSE 7: INTRODUCTION TO DATA ANALYSIS USING PYTHON [파이썬 프로그래밍을 활용한 데이터 분석]

⇒ Introduction to Python [파이썬 프로그래밍 입문] ⇐

⇒ Functions and Conditional Statements [함수와 조건문] ⇐

⇒ Loops and Strings [반복문과 문자열] ⇐

⇒ Data Structures in Python [파이썬의 데이터 구조] ⇐

COURSE 1: FOUNDATIONS - DATA, DATA, EVERYWHERE [데이터에 관한 기초]

TABLE OF CONTENTS

INTRODUCING DATA ANALYTICS AND ANALYTICAL THINKING

⇒ Transform Data Into Insights: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Understand Data Ecosystem: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Analyst Skills and Analytical Thinking: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

THE WONDERFUL WORLD OF DATA

⇒ Follow Data Life Cycle: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Outline Data Analysis Process: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Analysis Toolbox: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

SET-UP YOUR DATA ANALYTICS TOOLBOX

⇒ Master Spreadsheet Basics: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Introduction to SQL: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Introduction to Data Visualization: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

BECOME A FAIR AND IMPACTFUL DATA PROFESSIONAL

⇒ Data Analyst Job Opportunities: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Importance of Fair Business Decisions: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

INTRODUCING DATA ANALYTICS AND ANALYTICAL THINKING [데이터 분석 및 분석적 사고]

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THE WONDERFUL WORLD OF DATA [방대한 데이터의 세계]

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SET-UP YOUR DATA ANALYTICS TOOLBOX [데이터 분석 도구함 설정]

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BECOME A FAIR AND IMPACTFUL DATA PROFESSIONAL [공정하고 영향력 있는 데이터 전문가]

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COURSE 2: ASKING QUESTIONS TO MAKE DATA-DRIVEN DECISIONS [데이터 기반 의사 결정을 위한 질문]

TABLE OF CONTENTS

ASK EFFECTIVE QUESTIONS

⇒ Problem-Solving and Effective Questioning: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Take Action With Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Solve Problems With Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Craft Effective Questions: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

MAKE DATA-DRIVEN DECISIONS

⇒ Understand the Power of Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Follow the Evidence: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Connect the Data Dots: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

SPREADSHEET MAGIC

⇒ Work With Spreadsheets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Formulas in Spreadsheets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Functions in Spreadsheets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Save Time With Structured Thinking: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

ALWAYS REMEMBER THE STAKEHOLDER

⇒ Balance Team and Stakeholder needs: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Clear Communication Is Key: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Teamwork Best Practices: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

ASK EFFECTIVE QUESTIONS [효과적인 질문]

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MAKE DATA-DRIVEN DECISIONS [데이터 기반 의사결정]

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SPREADSHEET MAGIC [스프레드시트의 마법]

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ALWAYS REMEMBER THE STAKEHOLDER [이해관계자 고려]

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COURSE 3: PREPARE DATA FOR EXPLORATION [데이터 탐색을 위한 준비]

TABLE OF CONTENTS

DATA TYPES AND STRUCTURES

⇒ Data Exploration: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Collection: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Differentiate Data Formats and Structures: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Explore Data Types, Fields, Values: _________________ | _________________ | _________________ | _________________ ⇐

DATA RESPONSIBILITY

⇒ Unbiased and Objective Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Achieve Data Credibility: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Ethics and Privacy: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Understand Open Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

DATABASE ESSENTIALS

⇒ Work With Databases: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Manage Data With Metadata: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Access Different Data Sources: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Sort and Filter Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Large Datasets in SQL: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

ORGANIZE AND PROTECT DATA

⇒ Bring Data to Order: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Secure Data: _________________ | ___________________ | ___________________ | _________________ | _________________ ⇐

DATA TYPES AND STRUCTURES [데이터 타입 및 구조]

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DATA RESPONSIBILITY [데이터 책임 윤리]

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DATABASE ESSENTIALS [데이터베이스 필수 지식]

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ORGANIZE AND PROTECT DATA [데이터 조직화 및 보호]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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COURSE 4: PROCESS DATA FROM DIRTY TO CLEAN [불완전한 데이터에서 정제된 데이터로]

TABLE OF CONTENTS

THE IMPORTANCE OF INTEGRITY

⇒ Focus on Integrity: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Integrity and Analytics Objectives: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Overcome the Challenges of Insufficient Data: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Test Your Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Consider the Margin of Error: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

CLEAN DATA FOR MORE ACCURATE INSIGHTS

⇒ Data Cleaning Is a Must: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ First Steps Toward Clean Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Continue Cleaning Data in Spreadsheets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

DATA CLEANING WITH SQL

⇒ SQL for Sparking Clean Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Learn Basic SQL Queries: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Transforming Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

VERIFY AND REPORT ON CLEANING RESULTS

⇒ Manually Cleaning Data: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Document Cleaning Process: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

THE IMPORTANCE OF INTEGRITY [데이터 무결성의 중요성]

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CLEAN DATA FOR MORE ACCURATE INSIGHTS [정확한 통찰을 위한 데이터 정제]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

DATA CLEANING WITH SQL [SQL을 활용한 데이터 정제]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

VERIFY AND REPORT ON CLEANING RESULTS [정제 결과 검증 및 보고]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

COURSE 5: ANALYZE DATA TO ANSWER QUESTIONS [질문에 답하기 위한 데이터 분석]

TABLE OF CONTENTS

ORGANIZE DATA FOR MORE EFFECTIVE ANALYSIS

⇒ Organize Data for Analysis: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Sort Data in Spreadsheets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Sort Data Using SQL: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

FORMAT AND ADJUST DATA

⇒ Formatting for Better Analysis: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Combine Multiple Datasets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Get Support During Analysis: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

AGGREGATE DATA FOR ANALYSIS

⇒ VLOOKUP and Data Aggregation: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Use JOINS to Aggregate Data in SQL: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Work With Subqueries: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

PERFORM DATA CALCULATIONS

⇒ Introduction to Data Calculations: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Pivot Tables: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Learn More SQL Practices: _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Validation Process: _________________ | _________________ | _________________ | _________________ ⇐

⇒ SQL and Temporary Tables: _________________ | _________________ | _________________ | _________________ ⇐

ORGANIZE DATA FOR MORE EFFECTIVE ANALYSIS [분석을 위한 데이터 조직화]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

FORMAT AND ADJUST DATA [데이터 포맷 설정 및 조정]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

AGGREGATE DATA FOR ANALYSIS [분석을 위한 데이터 요약 및 집계]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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PERFORM DATA CALCULATIONS [데이터 계산 프로세스 수행]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

COURSE 6: SHARE DATA THROUGH THE ART OF VISUALIZATION [시각화 기술을 통한 데이터 공유]

TABLE OF CONTENTS

VISUALIZE DATA

⇒ Communicate Data Insights: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Understand Data Visualization: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Design Data Visualizations: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Visualization Considerations: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

CREATE DATA VISUALIZATIONS WITH TABLEAU

⇒ Introduction to Tableau: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Design Visualizations in Tableau: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

CRAFT DATA STORIES

⇒ Data-Driven Storytelling : _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Tableau Dashboards: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Share Data Stories: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

DEVELOP PRESENTATIONS AND SLIDESHOWS

⇒ The Art and Science of Presentations: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Presentation Skills and Practices: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Data Caveats and Limitations: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Listen, Respond, Include: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

VISUALIZE DATA [데이터 시각화]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

CREATE DATA VISUALIZATIONS WITH TABLEAU [태블로를 활용한 데이터 시각화]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

CRAFT DATA STORIES [데이터 스토리 설계]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

DEVELOP PRESENTATIONS AND SLIDESHOWS [프레젠테이션 및 슬라이드 제작]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

COURSE 7: INTRODUCTION TO DATA ANALYSIS USING PYTHON [파이썬 프로그래밍을 활용한 데이터 분석]

TABLE OF CONTENTS

INTRODUCTION TO PYTHON

⇒ The Power of Python: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Use Python Syntax: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

FUNCTIONS AND CONDITIONAL STATEMENTS

⇒ Functions: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Conditional Statements: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

LOOPS AND STRINGS

⇒ While Loops: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ For Loops: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Strings: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

DATA STRUCTURES IN PYTHON

⇒ Lists and Tuples: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Dictionaries and Sets: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Arrays and Vectors With NumPy: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

⇒ Dataframes With Pandas: _________________ | _________________ | _________________ | _________________ | _________________ ⇐

INTRODUCTION TO PYTHON [파이썬 프로그래밍 입문]

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FUNCTIONS AND CONDITIONAL STATEMENTS [함수와 조건문]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

LOOPS AND STRINGS [반복문과 문자열]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

DATA STRUCTURES IN PYTHON [파이썬의 데이터 구조]

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⇒ ________________________________________________________________________________________________________________________ ⇐

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⇒ ________________________________________________________________________________________________________________________ ⇐

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