PRIN-DS.AJ1
Principles of Data Science
Data drives the world…might as well be the one behind the wheel. Start learning today.
- Practice in 33 Praktische Übungen — nothing to install
- 16 Interaktive Lektionen Und 71 topics mapped to the official exam objectives
Intermediate Selbstgesteuert · 1 Jahr Zugang
33 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
16Interaktive Lektionen
71Topics
33LiveLab
2Videos
101Karteikarten
101Glossar der Begriffe
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
Try Free →
Keine Kreditkarte benötigt
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.
- 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.
Course Highlights
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16 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
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33 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
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1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
16 Interaktive Lektionen · 71 topics01 Introduction 4 topics +
- Who is this course for?
- What this course covers
- To get the most out of this course
- Conventions used
02 Data Science Terminology 5 topics · 1 LiveLab +
- What is data science?
- The data science Venn diagram
- Some more terminology
- Data science case studies
- Summary
1 LiveLab in this lesson — see the labs panel →
03 Types of Data 3 topics · 2 LiveLab +
- Structured versus unstructured data
- The four levels of data
- Summary
2 LiveLab in this lesson — see the labs panel →
04 The Five Steps of Data Science 3 topics · 2 LiveLab +
- Introduction to data science
- Exploring the data
- Summary
2 LiveLab in this lesson — see the labs panel →
05 Basic Mathematics 3 topics · 3 LiveLab +
- Basic symbols and terminology
- Linear algebra
- Summary
3 LiveLab in this lesson — see the labs panel →
06 Impossible or Improbable – A Gentle Introduction to Probability 5 topics · 2 LiveLab +
- Basic definitions
- Bayesian versus frequentist
- How to utilize the rules of probability
- Introduction to binary classifiers
- Summary
2 LiveLab in this lesson — see the labs panel →
07 Advanced Probability 3 topics · 3 LiveLab +
- Bayesian ideas revisited
- Random variables
- Summary
3 LiveLab in this lesson — see the labs panel →
08 What Are the Chances? An Introduction to Statistics 5 topics · 3 LiveLab +
- What are statistics?
- How do we obtain and sample data?
- How do we measure statistics?
- The empirical rule
- Summary
3 LiveLab in this lesson — see the labs panel →
09 Advanced Statistics 5 topics · 4 LiveLab +
- Understanding point estimates
- Sampling distributions
- Confidence intervals
- Hypothesis tests
- Summary
4 LiveLab in this lesson — see the labs panel →
10 Communicating Data 5 topics · 3 LiveLab +
- Why does communication matter?
- Identifying effective visualizations
- When graphs and statistics lie
- Verbal communication
- Summary
3 LiveLab in this lesson — see the labs panel →
11 How to Tell if Your Toaster is Learning – Machine Learning Essentials 4 topics · 2 LiveLab +
- Introducing ML
- Types of ML
- Predicting continuous variables with linear regression
- Summary
2 LiveLab in this lesson — see the labs panel →
12 Predictions Don’t Grow on Trees, or Do They? 5 topics · 4 LiveLab +
- Performing naïve Bayes classification
- Understanding decision trees
- Diving deep into UL
- Feature extraction and PCA
- Summary
4 LiveLab in this lesson — see the labs panel →
13 Introduction to Transfer Learning and Pre-Trained Models 4 topics · 1 LiveLab +
- Understanding pre-trained models
- Different types of TL
- TL with BERT and GPT
- Summary
1 LiveLab in this lesson — see the labs panel →
14 Mitigating Algorithmic Bias and Tackling Model and Data Drift 10 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
15 AI Governance 4 topics · 1 LiveLab +
- Mastering data governance
- Navigating the intricacy and the anatomy of ML governance
- A guide to architectural governance
- Summary
1 LiveLab in this lesson — see the labs panel →
16 Navigating Real-World Data Science Case Studies in Action 3 topics · 1 LiveLab +
- Introduction to the COMPAS dataset case study
- Text embeddings using pretrainedmodels and OpenAI
- Summary
1 LiveLab in this lesson — see the labs panel →
Praktische Übungen Our edge
33 LiveLabs- Extracting and Analyzing Cashtags in Tweets
- Exploring CSV Data
- Analyzing Temperature Data Using Statistical Methods
- Performing Time-Based Analysis
- Mastering Data Insights
- Working with Vectors and Matrices
- Computing Similarities with Set Operations
- Performing Matrix Operations and Analyzing Execution Time
- Simulating Random Rolls and Calculating Probabilities
- Generating and Analyzing Random Data
- Using Probability to Examine Survival Factors in a Dataset
- Simulating Dice Rolls and Analyzing Statistical Averages
- Creating and Visualizing the Normal Distribution
- Analyzing A/B Testing Results
- Evaluating the Central Tendency and Variability of Data
- Applying Z-Scores to Data Analysis
- 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
- Comparing Distribution Metrics with Histograms and Box Plots
- Visualizing Data with Scatter and Bar Charts
- Quantifying Data Relationships Through Correlation Analysis
- Predicting Alcohol Consumption Using Regression Models
- Preparing Data for Regression and Visualization
- Processing and Analyzing SMS Data
- Transforming Data and Creating Decision Tree Models
- Clustering Data Using K-Means
- Optimizing Models Using Feature Selection and PCA
- Fine-Tuning a Pre-Trained Model for Sentiment Analysis
- Generating and Visualizing Word Data
- Interpreting Sentiment Analysis Predictions with LIME
- Visualizing Distributions and Encoding Categorical Variables
Labore laufen direkt im Browser — nichts zu installieren.
03 / FAQS
Fragen, bevor Sie beginnen
What is the principle of data science?+
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.
Is data science full of maths?+
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.
What are the 5 C's of data science?+
The 5 C’s framework covers:
- Capture (data collection)
- Clean (preprocessing)
- Curate (organizing data)
- Compute (analysis/modeling)
- Communicate (visualizing results)
What are the 7 V's of data science?+
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)
What is Python for data science?+
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.
How to analyze big data?+
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.
What is the skill required by a data scientist?+
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.
Keep going
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