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Principles of Data Science

Data drives the world…might as well be the one behind the wheel. Start learning today.

(PRIN-DS.AJ1) / ISBN : 978-1-64459-630-2
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Über diesen Kurs

Enroll in our Principles of Data Science Course to combine math, programming, and business intelligence into one practical skillset.

In this course, dive into data cleaning, mining, and machine learning, then apply them to real-world problems with hands-on labs. Learn how to navigate complex datasets, build predictive models, and create visuals that tell compelling stories…all while tackling bias, data drift, and governance like a professional. 

Fähigkeiten, die Sie erwerben werden

  • Data Wrangling & Cleaning: Master techniques to prepare raw, messy data for analysis.
  • Statistical Modeling & Probability: Use advanced stats to extract insights and make predictions.
  • Machine Learning Pipelines: Build, evaluate, and deploy ML models (including NLP with GPT/BERT).
  • Bias Mitigation & Data Governance: Detect and reduce bias in data/models while ensuring ethical AI practices.
  • Data Storytelling & Visualization: Turn complex findings into clear, impactful visuals and reports.
  • Real-World Problem-Solving: Apply data science to case studies, from A/B testing to decision trees.
Kursübersicht herunterladen

1

Introduction

  • Who is this course for?
  • What this course covers
  • To get the most out of this course
  • Conventions used
2

Data Science Terminology

  • What is data science?
  • The data science Venn diagram
  • Some more terminology
  • Data science case studies
  • Summary
3

Types of Data

  • Structured versus unstructured data
  • The four levels of data
  • Summary
4

The Five Steps of Data Science

  • Introduction to data science
  • Exploring the data
  • Summary
5

Basic Mathematics

  • Basic symbols and terminology
  • Linear algebra
  • Summary
6

Impossible or Improbable – A Gentle Introduction to Probability

  • Basic definitions
  • Bayesian versus frequentist
  • How to utilize the rules of probability
  • Introduction to binary classifiers
  • Summary
7

Advanced Probability

  • Bayesian ideas revisited
  • Random variables
  • Summary
8

What Are the Chances? An Introduction to Statistics

  • What are statistics?
  • How do we obtain and sample data?
  • How do we measure statistics?
  • The empirical rule
  • Summary
9

Advanced Statistics

  • Understanding point estimates
  • Sampling distributions
  • Confidence intervals
  • Hypothesis tests
  • Summary
10

Communicating Data

  • Why does communication matter?
  • Identifying effective visualizations
  • When graphs and statistics lie
  • Verbal communication
  • Summary
11

How to Tell if Your Toaster is Learning – Machine Learning Essentials

  • Introducing ML
  • Types of ML
  • Predicting continuous variables with linear regression
  • Summary
12

Predictions Don’t Grow on Trees, or Do They?

  • Performing naïve Bayes classification
  • Understanding decision trees
  • Diving deep into UL
  • Feature extraction and PCA
  • Summary
13

Introduction to Transfer Learning and Pre-Trained Models

  • Understanding pre-trained models
  • Different types of TL
  • TL with BERT and GPT
  • Summary
14

Mitigating Algorithmic Bias and Tackling Model and Data Drift

  • Understanding algorithmic bias
  • Sources of algorithmic bias
  • Measuring bias
  • Consequences of unaddressed bias and the importance of fairness
  • Mitigating algorithmic bias
  • Bias in LLMs
  • Emerging techniques in bias and fairness in ML
  • Understanding model drift and decay
  • Mitigating drift
  • Summary
15

AI Governance

  • Mastering data governance
  • Navigating the intricacy and the anatomy of ML governance
  • A guide to architectural governance
  • Summary
16

Navigating Real-World Data Science Case Studies in Action

  • Introduction to the COMPAS dataset case study
  • Text embeddings using pretrainedmodels and OpenAI
  • Summary

1

Data Science Terminology

  • Extracting and Analyzing Cashtags in Tweets
2

Types of Data

  • Exploring CSV Data
  • Analyzing Temperature Data Using Statistical Methods
3

The Five Steps of Data Science

  • Performing Time-Based Analysis
  • Mastering Data Insights
4

Basic Mathematics

  • Working with Vectors and Matrices
  • Computing Similarities with Set Operations
  • Performing Matrix Operations and Analyzing Execution Time
5

Impossible or Improbable – A Gentle Introduction to Probability

  • Simulating Random Rolls and Calculating Probabilities
  • Generating and Analyzing Random Data
6

Advanced Probability

  • Using Probability to Examine Survival Factors in a Dataset
  • Simulating Dice Rolls and Analyzing Statistical Averages
  • Creating and Visualizing the Normal Distribution
7

What Are the Chances? An Introduction to Statistics

  • Analyzing A/B Testing Results
  • Evaluating the Central Tendency and Variability of Data
  • Applying Z-Scores to Data Analysis
8

Advanced Statistics

  • Estimating Break Lengths and Demographic Proportions
  • Converting Bimodal Data to a Normal Distribution Using Sampling
  • Calculating and Interpreting Confidence Intervals
  • Testing Hypotheses: Type I and II Errors
9

Communicating Data

  • Comparing Distribution Metrics with Histograms and Box Plots
  • Visualizing Data with Scatter and Bar Charts
  • Quantifying Data Relationships Through Correlation Analysis
10

How to Tell if Your Toaster is Learning – Machine Learning Essentials

  • Predicting Alcohol Consumption Using Regression Models
  • Preparing Data for Regression and Visualization
11

Predictions Don’t Grow on Trees, or Do They?

  • Processing and Analyzing SMS Data
  • Transforming Data and Creating Decision Tree Models
  • Clustering Data Using K-Means
  • Optimizing Models Using Feature Selection and PCA
12

Introduction to Transfer Learning and Pre-Trained Models

  • Fine-Tuning a Pre-Trained Model for Sentiment Analysis
13

Mitigating Algorithmic Bias and Tackling Model and Data Drift

  • Generating and Visualizing Word Data
14

AI Governance

  • Interpreting Sentiment Analysis Predictions with LIME
15

Navigating Real-World Data Science Case Studies in Action

  • Visualizing Distributions and Encoding Categorical Variables

Haben Sie Fragen? Schauen Sie sich die FAQs an

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The core data science principles revolve around extracting meaningful insights from raw data using a structured approach. It includes data collection, cleaning, analysis, modeling, and visualization to solve real-world problems.

Yes, but not overwhelmingly so. Data science relies on statistics, probability, and linear algebra, but modern tools (like Python libraries) handle complex calculations, letting you focus on applying concepts rather than deep math theory.

The 5 C’s framework covers:

  • Capture (data collection)
  • Clean (preprocessing)
  • Curate (organizing data)
  • Compute (analysis/modeling)
  • Communicate (visualizing results)

The 7 V’s define big data challenges:

  • Volume (size of data)
  • Velocity (speed of data flow)
  • Variety (different data types)
  • Veracity (data accuracy)
  • Value (extracting usefulness)
  • Variability (inconsistencies)
  • Visualization (presenting insights)

Python is the most popular programming language for data science, thanks to libraries like Pandas (data manipulation), NumPy (math operations), and Scikit-learn (machine learning). It simplifies complex tasks with readable, efficient code.

Data science for beginners starts with:

  • Structured tools: Use Python/R + SQL.
  • Cloud platforms: Leverage AWS, Google Cloud.
  • Distributed computing: Try Apache Spark.
  • Visualization: Power BI/Tableau for clarity.

A data scientist should work to develop the following skillsets: 

  • Technical: Python/R, SQL, ML, statistics.
  • Analytical: Critical thinking, problem-solving.
  • Business Acumen: Translating data into decisions.
  • Communication: Presenting insights clearly.

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