Introduction to Data Science

Complete syllabus

Every chapter and lesson

1. 01

   Chapter 1 · 3 lessons

   The Data Science Process

   1. 1.1

      What data science is

      8 min · Quick check

      Lesson goal: By the end you can define data science and its significance in various fields.

      • Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
      • It combines techniques from statistics, computer science, and domain expertise.
      • Data science is used in various industries such as healthcare, finance, and marketing to make data-driven decisions.
   2. 1.2

      The data science workflow

      8 min · Quick check

      Lesson goal: By the end you can describe the stages of the data science workflow.

      • The data science workflow consists of several stages including data collection, data cleaning, data exploration, modeling, and deployment.
      • Each stage has specific tasks and methodologies that contribute to the overall data analysis process.
      • Understanding the workflow helps in organizing and managing data science projects effectively.
   3. 1.3

      Asking good questions

      8 min · Quick check

      Lesson goal: By the end you can formulate effective questions that guide data science projects.

      • Good questions are essential for defining the scope and objectives of a data science project.
      • They should be clear, focused, and researchable, allowing for data-driven answers.
      • Asking the right questions can lead to better insights and more impactful results.
2. 02

   Chapter 2 · 3 lessons

   Working with Data

   1. 2.1

      Data types & sources

      8 min · Quick check

      Lesson goal: By the end you can identify and describe various data types and sources used in data science.

      • Define data types including quantitative and qualitative.
      • Identify primary data sources such as surveys and experiments.
      • Recognize secondary data sources including databases and online repositories.
      • Differentiate between structured and unstructured data.
   2. 2.2

      Cleaning & wrangling

      9 min · Quick check

      Lesson goal: By the end you can apply techniques for cleaning and wrangling data to prepare it for analysis.

      • Define data cleaning and its importance in data science.
      • Identify common data issues such as missing values and duplicates.
      • Apply techniques for handling missing data and outliers.
      • Demonstrate data wrangling techniques using tools like pandas.
   3. 2.3

      Exploratory data analysis

      9 min · Quick check

      Lesson goal: By the end you can conduct exploratory data analysis to uncover patterns and insights in data.

      • Define exploratory data analysis and its role in data science.
      • Identify key techniques such as summary statistics and data visualization.
      • Apply graphical methods to explore data distributions and relationships.
      • Interpret findings from exploratory data analysis to inform further analysis.
3. 03

   Chapter 3 · 3 lessons

   Statistics for Data Science

   1. 3.1

      Descriptive statistics

      8 min · Quick check

      Lesson goal: By the end you can understand and explain the key concepts of descriptive statistics.

      • Define descriptive statistics and its purpose in data analysis.
      • Identify measures of central tendency: mean, median, and mode.
      • Explain measures of variability: range, variance, and standard deviation.
      • Discuss the importance of data visualization techniques such as histograms and box plots.
   2. 3.2

      Probability & distributions

      9 min · Quick check

      Lesson goal: By the end you can apply probability concepts and understand different probability distributions.

      • Define probability and its significance in statistics.
      • Explain the concept of random variables and their types: discrete and continuous.
      • Identify common probability distributions: normal, binomial, and Poisson.
      • Discuss the Central Limit Theorem and its implications for sampling distributions.
   3. 3.3

      Inference & sampling

      8 min · Quick check

      Lesson goal: By the end you can perform statistical inference and understand the principles of sampling.

      • Define statistical inference and its role in drawing conclusions from data.
      • Explain the concept of sampling and different sampling methods.
      • Discuss confidence intervals and their interpretation.
      • Introduce hypothesis testing and the significance of p-values.
4. 04

   Chapter 4 · 3 lessons

   Modeling

   1. 4.1

      Correlation & regression

      9 min · Quick check

      Lesson goal: By the end you can explain the concepts of correlation and regression and their applications in data analysis.

      • Define correlation and its significance in statistics.
      • Explain regression analysis and its purpose in predicting outcomes.
      • Differentiate between correlation and causation.
      • Identify different types of regression models.
   2. 4.2

      Classification basics

      8 min · Quick check

      Lesson goal: By the end you can describe the basics of classification and its role in data science.

      • Define classification and its importance in machine learning.
      • Explain different types of classification algorithms.
      • Discuss the concept of training and testing datasets in classification.
      • Identify common applications of classification in real-world scenarios.
   3. 4.3

      Evaluating models

      8 min · Quick check

      Lesson goal: By the end you can evaluate the performance of models using various metrics.

      • Define model evaluation and its importance in data science.
      • Explain common evaluation metrics such as accuracy, precision, recall, and F1 score.
      • Discuss the concept of overfitting and underfitting in model evaluation.
      • Identify techniques for improving model performance.
5. 05

   Chapter 5 · 3 lessons

   Communicating & Ethics

   1. 5.1

      Telling stories with data

      8 min · Quick check

      Lesson goal: By the end you can effectively communicate insights from data through storytelling techniques.

      • Understand the importance of narrative in data presentation.
      • Identify key elements of a compelling data story.
      • Learn how to visualize data to enhance storytelling.
   2. 5.2

      Reproducibility

      8 min · Quick check

      Lesson goal: By the end you can explain the significance of reproducibility in data science.

      • Define reproducibility and its role in scientific research.
      • Discuss methods to ensure reproducibility in data analysis.
      • Explore tools that facilitate reproducible research.
   3. 5.3

      Data ethics & bias

      9 min · Quick check

      Lesson goal: By the end you can recognize and address ethical considerations and biases in data science.

      • Define data ethics and its importance in data science.
      • Identify common types of bias in data collection and analysis.
      • Discuss strategies to mitigate bias and promote ethical practices.