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Mastering Data-Driven Decision Making; A Step-by-Step Guide to Becoming a Certified Analytics Professional

Mastering Data-Driven Decision Making; A Step-by-Step Guide to Becoming a Certified Analytics Professional

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Mastering Data-Driven Decision Making: A Step-by-Step Guide to Becoming a Certified Analytics Professional

Mastering Data-Driven Decision Making: A Step-by-Step Guide to Becoming a Certified Analytics Professional

This comprehensive course is designed to equip you with the skills and knowledge needed to make data-driven decisions and become a certified analytics professional. Upon completion, you will receive a certificate issued by The Art of Service.



Course Features

  • Interactive: Engage with interactive lessons and activities to enhance your learning experience.
  • Engaging: Enjoy a user-friendly interface and bite-sized lessons that make learning fun and easy.
  • Comprehensive: Cover all aspects of data-driven decision making, from data analysis to visualization and interpretation.
  • Personalized: Get personalized feedback and support from expert instructors.
  • Up-to-date: Stay current with the latest tools, technologies, and methodologies in data analysis.
  • Practical: Apply your knowledge to real-world projects and case studies.
  • Real-world applications: Learn from real-world examples and case studies.
  • High-quality content: Access high-quality video lessons, readings, and resources.
  • Expert instructors: Learn from experienced instructors with industry expertise.
  • Certification: Receive a certificate upon completion, issued by The Art of Service.
  • Flexible learning: Learn at your own pace, anytime, anywhere.
  • User-friendly: Navigate our user-friendly interface with ease.
  • Mobile-accessible: Access the course on your mobile device or tablet.
  • Community-driven: Join a community of learners and professionals in the field.
  • Actionable insights: Gain actionable insights and practical skills to apply in your work.
  • Hands-on projects: Work on hands-on projects to apply your knowledge and skills.
  • Bite-sized lessons: Learn in bite-sized chunks, with each lesson lasting around 10-15 minutes.
  • Lifetime access: Enjoy lifetime access to the course materials and resources.
  • Gamification: Engage with gamification elements, such as quizzes and challenges, to make learning fun.
  • Progress tracking: Track your progress and stay motivated with our progress tracking feature.


Course Outline

Chapter 1: Introduction to Data-Driven Decision Making

Topic 1.1: What is Data-Driven Decision Making?

  • Definition and importance of data-driven decision making
  • Benefits and challenges of data-driven decision making

Topic 1.2: The Role of Data Analysis in Decision Making

  • Types of data analysis: descriptive, predictive, and prescriptive
  • Data analysis tools and techniques

Chapter 2: Data Analysis Fundamentals

Topic 2.1: Data Types and Structures

  • Numeric, categorical, and text data
  • Data structures: arrays, lists, and data frames

Topic 2.2: Data Visualization

  • Types of data visualization: plots, charts, and graphs
  • Data visualization tools and techniques

Chapter 3: Data Preprocessing and Cleaning

Topic 3.1: Handling Missing Values

  • Types of missing values: MCAR, MAR, and MNAR
  • Strategies for handling missing values

Topic 3.2: Data Normalization and Transformation

  • Types of data normalization: min-max scaling and standardization
  • Data transformation techniques: log transformation and feature scaling

Chapter 4: Data Analysis Techniques

Topic 4.1: Descriptive Statistics

  • Measures of central tendency: mean, median, and mode
  • Measures of variability: range, variance, and standard deviation

Topic 4.2: Inferential Statistics

  • Types of inferential statistics: hypothesis testing and confidence intervals
  • Common statistical tests: t-test, ANOVA, and regression analysis

Chapter 5: Machine Learning Fundamentals

Topic 5.1: Introduction to Machine Learning

  • Definition and types of machine learning: supervised, unsupervised, and reinforcement learning
  • Machine learning algorithms: linear regression, decision trees, and clustering

Topic 5.2: Model Evaluation and Selection

  • Metrics for evaluating machine learning models: accuracy, precision, and recall
  • Techniques for model selection: cross-validation and grid search

Chapter 6: Data Visualization and Communication

Topic 6.1: Data Visualization Best Practices

  • Principles of effective data visualization: simplicity, clarity, and accuracy
  • Common data visualization mistakes: 3D plots, pie charts, and misleading scales

Topic 6.2: Communicating Insights and Results

  • Strategies for communicating complex data insights: storytelling and analogies
  • Best practices for presenting data results: clear, concise, and actionable

Chapter 7: Case Studies and Real-World Applications

Topic 7.1: Case Study 1: Predicting Customer Churn

  • Problem statement and data description
  • Step-by-step solution: data preprocessing, feature engineering, and model evaluation

Topic 7.2: Case Study 2: Analyzing Customer Purchase Behavior

  • Problem statement and data description
  • Step-by-step solution: data preprocessing, feature engineering, and model evaluation

Chapter 8: Advanced Topics in Data Analysis

Topic 8.1: Time Series Analysis

  • Types of time,
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