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 Laboratorios prácticos — nothing to install
- 16 Lecciones interactivas y 71 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
33 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / Habilidades que obtendrás
What you will be able to do
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
-
16 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
-
33 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
-
1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
16 Lecciones interactivas · 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 Laboratorio en vivo +
- What is data science?
- The data science Venn diagram
- Some more terminology
- Data science case studies
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
03 Types of Data 3 topics · 2 Laboratorio en vivo +
- Structured versus unstructured data
- The four levels of data
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
04 The Five Steps of Data Science 3 topics · 2 Laboratorio en vivo +
- Introduction to data science
- Exploring the data
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Basic Mathematics 3 topics · 3 Laboratorio en vivo +
- Basic symbols and terminology
- Linear algebra
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
06 Impossible or Improbable – A Gentle Introduction to Probability 5 topics · 2 Laboratorio en vivo +
- Basic definitions
- Bayesian versus frequentist
- How to utilize the rules of probability
- Introduction to binary classifiers
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
07 Advanced Probability 3 topics · 3 Laboratorio en vivo +
- Bayesian ideas revisited
- Random variables
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
08 What Are the Chances? An Introduction to Statistics 5 topics · 3 Laboratorio en vivo +
- What are statistics?
- How do we obtain and sample data?
- How do we measure statistics?
- The empirical rule
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
09 Advanced Statistics 5 topics · 4 Laboratorio en vivo +
- Understanding point estimates
- Sampling distributions
- Confidence intervals
- Hypothesis tests
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
10 Communicating Data 5 topics · 3 Laboratorio en vivo +
- Why does communication matter?
- Identifying effective visualizations
- When graphs and statistics lie
- Verbal communication
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
11 How to Tell if Your Toaster is Learning – Machine Learning Essentials 4 topics · 2 Laboratorio en vivo +
- Introducing ML
- Types of ML
- Predicting continuous variables with linear regression
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
12 Predictions Don’t Grow on Trees, or Do They? 5 topics · 4 Laboratorio en vivo +
- Performing naïve Bayes classification
- Understanding decision trees
- Diving deep into UL
- Feature extraction and PCA
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
13 Introduction to Transfer Learning and Pre-Trained Models 4 topics · 1 Laboratorio en vivo +
- Understanding pre-trained models
- Different types of TL
- TL with BERT and GPT
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
14 Mitigating Algorithmic Bias and Tackling Model and Data Drift 10 topics · 1 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
15 AI Governance 4 topics · 1 Laboratorio en vivo +
- Mastering data governance
- Navigating the intricacy and the anatomy of ML governance
- A guide to architectural governance
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
16 Navigating Real-World Data Science Case Studies in Action 3 topics · 1 Laboratorio en vivo +
- Introduction to the COMPAS dataset case study
- Text embeddings using pretrainedmodels and OpenAI
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
33 Laboratorio en vivos- 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
03 / Preguntas frecuentes
Preguntas antes de empezar
What is the principle of data science?+
Is data science full of maths?+
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?+
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
You might also like
Data Science Made Simple
Decode big data, predict outcomes, and get hired because companies need analysts who turn numbers into game plans.
- 1 año de acceso completo
- 33 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito