Course Overview

In this intensive 3-day course, participants will dive into the world of data analytics, learning essential techniques and tools to extract valuable insights from raw data. Through hands-on exercises and real-world case studies, attendees will develop a solid foundation in data analytics, enabling them to make data-driven decisions and solve complex business problems.

Course Objectives

  • Understand the fundamentals of data analytics and its importance in modern business environments.
  • Learn essential techniques for data cleaning, preprocessing, and transformation.
  • Explore various data visualization methods to communicate insights effectively.
  • Gain proficiency in using popular analytics tools such as Python, R, and SQL.
  • Discover advanced analytical techniques including predictive modeling and machine learning.
  • Apply learned concepts to real-world datasets and scenarios.
  • Develop the skills to interpret and present analytical findings to stakeholders.

Course Outputs

    • To pivot when needed especially recognize when the current strategy or direction is not working and be willing to make necessary changes. This could involve adjusting goals, reallocating resources, or reevaluating priorities.
    • Proficiency in data preprocessing and cleaning techniques.
    • Competency in data visualization using tools like Matplotlib, Seaborn, or ggplot2.
    • Practical experience in utilizing Python, R, and SQL for data analysis.
    • Understanding of predictive modeling and machine learning algorithms.
    • Ability to extract actionable insights from complex datasets.

 

Course Outlines

Day 1: Foundations of Data Analytics

  • Introduction to Data Analytics
  • Importance and applications of data analytics
  • Data types and formats
  • Data cleaning and preprocessing techniques
  • Hands-on exercises: Data cleaning and preprocessing in Python/R

Day 2: Data Visualization and Exploration

  • Principles of data visualization
  • Choosing the right visualization for your data
  • Introduction to Matplotlib, Seaborn, or ggplot2
  • Hands-on exercises: Creating visualizations and exploring datasets

Day 3: Advanced Analytics Techniques

  • Introduction to predictive modeling and machine learning
  • Overview of popular algorithms (Regression, Classification, Clustering)
  • Introduction to Python libraries for machine learning (scikit-learn)
  • Hands-on exercises: Building predictive models and evaluating performance.

Duration

3 Days

Delivery Methodology

  • Classroom
  • Labwork

 

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Data Analytics Essentials

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