Data Science Course

Data-Science-Course - Copy

Data Science Course Description


What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!


Introduction to Data Science

We’ll start the course by defining what data science is. We’ll cover the data science workflow and how data science is applied to real-world problems. We’ll finish the chapter by learning about different roles within the data science field.




    • What is data science?
    • Customer segmentation workflow
    • Building a customer service chatbot
    • Applications of data science
    • Assigning data science project
    • Investment research
    • Data science roles and tools
    • Editing a job post
    • Matching skills to jobs
    • Classifying data tasks


Preparation, Exploration, and Visualization


Data preparation is fundamental: data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This chapter will show you how to diagnose problems in your data, deal with missing values and outliers. You will then learn about visualization, another essential tool to both explore your data and convey your findings.

  • Data preparation
  • The truth is out there
  • Are you prepared?
  • Exploratory Data Analysis
  • Numerical EDA
  • Visual EDA
  • Visualization
  • Interactive dashboards
  • Improving a dashboard

Data Collection and Storage


Now that we understand the data science workflow, we’ll dive deeper into the first step: data collection and storage. We’ll learn about the different data sources you can draw from, what that data looks like, how to store the data once it’s collected, and how a data pipeline can automate the process.


  • Data sources
  • Sorting data sources
  • Asthma frequencies
  • Data types
  • Classifying data types
  • Net promoter score
  • Activity tracker
  • Data storage and retrieval
  • Cloud platforms
  • Querying a database
  • Which type of database?
  • Data Pipelines
  • Data pipeline characteristics
  • Extract Transform Load

Experimentation and Prediction


In this final chapter, we’ll discuss experimentation and prediction! Beginning with experiments, we’ll cover A/B testing, and move on to time series forecasting where we’ll learn about predicting future events. Finally, we’ll end with machine learning, looking at supervised learning, and clustering.


    • A/B Testing
    • Creating an A/B testing workflow
    • Statistical significance
    • Intermediate results
    • Time series forecasting
    • Classifying time-series data
    • Interpret a time series plot
    • Supervised machine learning
    • When to use supervised learning
    • Features and labels
    • Model evaluation
    • Clustering
    • Supervised vs. unsupervised
    • Cluster size selection
    • Congratulations!