Data Science

Data Science Courses

Data Science is the field of turning raw data into meaningful insights and intelligent decisions. In this category, you'll learn how to collect, process, analyze, visualize, and model data using tools like Python, SQL, machine learning, and BI platforms — preparing you for real-world data-driven challenges.

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Gained skills:

A/B Test DesignAI Ethics Fundamentals AI Transparency AwarenessARIMA ModelingAccountability in AI Active Learning FundamentalsAdaptive AlgorithmsAdvanced ARIMA TechniquesAdvanced Ensemble IntegrationAdvanced Text CleaningAdversarial Training ConceptsAlgorithm Evaluation and ComparisonAnalyzing GAN Training ChallengesAnomaly detection evaluation Applied Calibration WorkflowsApplying RNNs to NLP tasks (sentiment analysis) Artificial immune systemsAttention Mechanisms TheoryAutomated Search with scikit-learnAutoregressive GenerationBackward Difference CodingBagging and Random ForestsBayes' TheoremBayesian OptimizationBoosting AlgorithmsCalibration Metrics (ECE, MCE, Brier Score)CatBoost ModelingCategorical Feature HandlingChain-of-Thought Prompting Chi-Square AnalysisClassification Loss AnalysisClassification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) Clustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) Clustering fundamentals and algorithms Coefficient VisualizationCommittee-Based QueryingCompression Trade-off ReasoningConvergence TheoryConvex AnalysisConvolutional Neural NetworksCoordinate Reference SystemsCorrelation AnalysisCovariance and eigen decomposition Cross-validation techniquesDBSCAN: noise handling and irregular shapes Data Cleaning Data Leakage PreventionData PreprocessingData Privacy Concepts Data StorytellingData Transformation Data Visualization with matplotlib and seabornData normalization and distance metrics Deduplication AlgorithmsDensity-Weighted SamplingDeployment Best PracticesDescriptive StatisticsDiffusion Model TheoryDiffusion Models Dimensionality reduction Dimensionality reduction evaluation Document Chunking and IndexingDrift Detection FundamentalsDynamic Programming MethodsEncoding Leakage PreventionEnd-to-end model development and evaluationEnsemble Learning FundamentalsEntropy and Rate–Distortion AnalysisEthical AI PrinciplesEthical Decision-Making Evaluation Metrics for Generative AIEvolutionary optimization Experiment Tracking with MLflowExperimental Data PreparationExplainable AI FundamentalsExploratory Data AnalysisFailure Analysis in RAGFairness and Bias Analysis Fairness in MLFeature Encoding Feature Engineering Feature Engineering for TSFeature ScalingFeature Scaling Feature Selection Feature Selection MethodsFew-Shot Prompting Fine-tuning Pre-trained ModelsForecast Evaluation MetricsForecasting StrategiesFunctions & SetsFuzzy Matching in PythonGAN FundamentalsGANs Gaussian Mixture Models: probabilistic clusteringGenerative AI Genetic algorithms Geometric InterpretabilityGeospatial Data FundamentalsGeospatial VisualizationGradient Boosting for TSGradient DescentGradient Descent Graph Embedding IntuitionGraph Representation in PythonGraph Theory for MLGraphSAGE ConceptsGymnasium BasicsHandling missing and categorical data Helmert CodingHierarchical clustering and dendrograms High-Cardinality Feature EncodingHistogram BinningHybrid Rule-Based SystemsHyperparameter TuningHyperparameter Tuning FundamentalsHypothesis TestingImage Processing with OpenCVImplementing recurrent networks in PyTorchIn-Context Learning TheoryInformation Bottleneck and MDLInformation Theory in NLPInformation-Theoretic LossesIntegrals Isolation Forest ImplementationIsotonic RegressionK-Means: principles and cluster optimization Knowledge Distillation TheoryKnowledge Graph Embedding ModelsKnowledge Graph FundamentalsKnowledge Integration in LLMsKolmogorov–Smirnov TestL1, L2, and Elastic Net RegularizationLLM Failure ModesLabel Efficiency TechniquesLatent Space GeometryLatent Space ReasoningLayer-wise Representation AnalysisLearning Curve AnalysisLeave-One-Out EncodingLightGBM ModelingLimits & Derivatives Limits of LLM GeneralizationLinear Regression with PythonLinear Transformations Link PredictionLocal Outlier Factor AnalysisLogistic RegressionLoss Function Selection and ComparisonLow-Rank Matrix IntuitionMLOps FundamentalsMachine Learning with scikit-learnManifold IntuitionManual Search MethodsMarkov Chains in Generative ModelingMathematical Formulation of GANsMathematical Foundations of AttentionMathematical Foundations of Loss FunctionsMathematical OptimizationMatrix Decomposition Mean-CenteringMissing Value Imputation Model BlendingModel Deployment with FastAPI and DockerModel Evaluation and DiagnosticsModel Evaluation and GeneralizationModel InterpretabilityModel InterpretationModel Monitoring and CI/CDModel RegularizationModel Training and EvaluationModel-Based Drift DetectionMomentum MethodsMonitoring Model DegradationMonte Carlo TechniquesMulti-Armed Bandit AlgorithmsMulti-Head Attention ConceptsMultivariate AnalysisNatural Language HandlingNatural Language ProcessingNeural Network Compression TheoryNeural NetworksNeuroevolutionNode ClassificationNormalization (L1, L2, Max)ODE Formulations in Generative ModelsObject Detection ApproachesOne-Class SVM for Novelty DetectionOptimization Constraints in Fine-TuningOutlier Detection Outlier Detection FundamentalsOverfitting and RegularizationPEFT Deployment ReasoningPEFT TheoryParticle swarm optimizationPattern MiningPipeline Automation with AirflowPipeline BuildingPipeline ConstructionPlatt ScalingPolynomial CodingPopulation Stability IndexPositional Encoding ConceptsPreprocessing PipelinesPrincipal component analysis (PCA) Probabilistic Model CalibrationProbability DistributionsProbability Rules Processing time series and sequential dataPrompt Engineering Fundamentals Prompt EvaluationPrompt Refinement Prompt-Based GeneralizationPyTorch BasicsPython Classification ModelsPython Data StructuresPython ProgrammingQuantization and Pruning MathematicsRAG Evaluation MetricsRAG Pipeline ArchitectureRAG System Design PatternsRIPPER AlgorithmRandom Walks on GraphsReasoning over Knowledge GraphsRecord Linkage TechniquesRegression Loss AnalysisRegression metrics (MSE, RMSE, MAE, R²) Regulatory AwarenessReinforcement Learning FoundationsReliability DiagramsResponsible AI Frameworks Retrieval-Augmented Generation FundamentalsRisk Minimization TheoryRole and Context Prompting Rule PruningRule Quality MetricsRule-Based ModelingRuleFit AlgorithmSampling StrategiesSampling Strategies in MLScore MatchingSelf-Attention IntuitionSelf-Attention MechanismSemantic Directions in LLMsSemantic Retrieval ConceptsSeries Analysis Similarity Scoring for GraphsSpatial Joins and OverlaysSpatial OperationsStandardizationStatistical Anomaly DetectionStatistical Drift MetricsStatistical InterpretationStatistical Measures Stochastic Differential Equations (SDEs)Stochastic OptimizationStructured Output Design Swarm intelligenceTemporal ValidationTemporal-Difference LearningTensorFlow BasicsTheoretical Foundations of Zero-Shot GeneralizationTime Series AnalysisTime Series WindowingTrade-off Analysis in Model DesignTransfer Learning FundamentalsTransfer Learning in CVTransfer Learning in NLPTransformer Architecture TheoryTransformer Architecture UnderstandingTransformers Transparency Principles Tree-Based ForecastingTriple Scoring FunctionsUncertainty-Based QueryingUnderstanding GAN VariantsUnderstanding RNNs, LSTMs, and GRUsUnderstanding Representation CollapseVAEs Variational Inference & ELBOVector Search TheoryVector and Raster Data HandlingVectors & Matrices Weight-of-Evidence EncodingWhitening and DecorrelationXAI Methods and ConceptsXGBoost Modelingscikit-learn Active Learning Implementationt-test and z-test Application

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Courses

Browse Data Science courses and projects

Level

Type of lesson

Technologies

Feature Encoding Methods in Python

1 hour

9 chapters

Intermediate

Acquired skills: Weight-of-Evidence Encoding, Leave-One-Out Encoding, Helmert Coding, Backward Difference Coding, Polynomial Coding, High-Cardinality Feature Encoding, Encoding Leakage Prevention

Feature Scaling and Normalization in Python

2 hours

19 chapters

Beginner

Acquired skills: Feature Scaling, Mean-Centering, Standardization, Normalization (L1, L2, Max), Whitening and Decorrelation, Preprocessing Pipelines, Data Leakage Prevention

Feature Selection and Regularization Techniques in Python

1 hour

12 chapters

Beginner

Acquired skills: Overfitting and Regularization, L1, L2, and Elastic Net Regularization, Feature Selection Methods, Pipeline Construction, Hyperparameter Tuning, Coefficient Visualization

Generative Adversarial Networks Basics

2 hours

12 chapters

Intermediate

Acquired skills: GAN Fundamentals, Adversarial Training Concepts, Mathematical Formulation of GANs, Understanding GAN Variants, Analyzing GAN Training Challenges

Geospatial Data Science with Python

2 hours

13 chapters

Intermediate

Acquired skills: Geospatial Data Fundamentals, Vector and Raster Data Handling, Coordinate Reference Systems, Spatial Operations, Geospatial Visualization, Spatial Joins and Overlays

Graph Theory for Machine Learning with Python

1 hour

12 chapters

Beginner

Acquired skills: Graph Theory for ML, Graph Representation in Python, Random Walks on Graphs, Graph Embedding Intuition, Similarity Scoring for Graphs, Link Prediction, Node Classification, GraphSAGE Concepts

Handling Data Drift in Production

2 hours

9 chapters

Advanced

Acquired skills: Drift Detection Fundamentals, Statistical Drift Metrics, Kolmogorov–Smirnov Test, Population Stability Index, Model-Based Drift Detection, Monitoring Model Degradation

Hyperparameter Tuning Basics with Python

1 hour

11 chapters

Beginner

Acquired skills: Hyperparameter Tuning Fundamentals, Manual Search Methods, Automated Search with scikit-learn, Bayesian Optimization, Model Evaluation and Generalization

Introduction to Time Series Forecasting

2 hours

15 chapters

Intermediate

Acquired skills: Time Series Analysis, ARIMA Modeling, Forecast Evaluation Metrics, Advanced ARIMA Techniques

Knowledge Graphs and Embeddings

2 hours

13 chapters

Intermediate

Acquired skills: Knowledge Graph Fundamentals, Graph Representation in Python, Knowledge Graph Embedding Models, Triple Scoring Functions, Link Prediction, Reasoning over Knowledge Graphs

Latent Space Geometry in LLMs

2 hours

9 chapters

Advanced

Acquired skills: Latent Space Geometry, Manifold Intuition, Semantic Directions in LLMs, Layer-wise Representation Analysis, Understanding Representation Collapse, Geometric Interpretability

Loss Functions in Machine Learning

2 hours

15 chapters

Intermediate

Acquired skills: Mathematical Foundations of Loss Functions, Risk Minimization Theory, Regression Loss Analysis, Classification Loss Analysis, Information-Theoretic Losses, Loss Function Selection and Comparison

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Video content

Downloadable materials

Esteemed certificates

Interactive learning environment

Error correction

AI assistant

Access to all content with one subscription

Career opportunitiesLearn about the most popular professions, average salaries, and companies actively seeking specialists in this field.

Data Scientist

Big Data Analyst

NLP Engineer

Deep Learning Engineer

AI Researcher

$149k

$197k

$246k

Min

Average

Max

Annual salary

(Average in the US)

Hiring companies

*Source: Glassdoor

Chosen by students of top schools

Including 30 out of top-30 U.S. colleges

Data Science Courses: Key Info and Questions

Introduction to Data Science Courses

We have plenty of courses for any aspect of data science, including data visualization (like "Ultimate visualization with Python"), data manipulation with Python (like "Ultimate NumPy" or "Advanced Techniques in pandas"), SQL (like "Introduction to SQL") and machine learning (like "ML Introduction with scikit-learn" or "Introduction to Neural Networks").

Benefits of our Data Science courses

We are providing our clients with comprehensive curriculum, hands-on experience and expert instructors.

Career opportunities after completion of Data Science courses

After completing data science course you potentially can start career at variety of data-driven positions, including data scientist, data analyst, machine learning analyst, business intelligence analyst, data engineer etc.

Data Science Options

We have plenty of courses for any aspect of data science, including data visualization(like "Ultimate visualization with Python"), data manipulation with Python (like "Ultimate NumPy" or "Advanced Techniques in pandas"), SQL (like "Introduction to SQL") and machine learning (like "ML Introduction with scikit-learn" or "Introduction to Neural Networks").

Certificate Information

After completing any of our data science related courses, you will receive a certificate that validates your skills and knowledge in data science.

How to Choose the Suitable Data Science Course?

Choose a data science course based on your current skill level and career goals. If you're new, start with beginner-friendly courses covering Python, data analysis, and visualization. For those with experience, look for courses focused on machine learning, deep learning, or AI-assisted workflows. Prioritize hands-on projects, real datasets, and tools like Jupyter, Pandas, or ChatGPT-based assistants to ensure practical, industry-relevant learning.

Which course is best in the category of Data Science Courses?

We have a plenty of good courses, related to data science field, among which we can highlight "Advanced Techniques in pandas", "Ultimate Visualization with Python" and "ML Introduction with scikit-learn".

Why should I consider taking an online Data Science course with your company?

We are providing our clients with comprehensive curriculum, hands-on experience and expert instructors.

Tips for successful Data Science course completion

You need to stay organized, learn actively and practice regulary.

What is the Cost of Training for Data Science Courses?

We offer flexible pricing options. Our Pro Plan starts at $49 per month or $99 for three months, with savings on our Pro Annual Plan at $144. Our Ultimate Plan is $59 per month, $147 for three months, or $299 annually. Each plan includes access to expert-crafted content, interactive challenges, and certification.

Which Data Science Course is Best Suited for Beginners?

For beginners good options may be "Introduction to SQL" and "Pandas First Steps".

What are the key skills required to excel in Data Science?

To succeed in data science, you need a mix of technical and analytical skills. Key areas include Python or R programming, data wrangling with tools like Pandas, statistical thinking, and the ability to draw insights from data. Experience with machine learning libraries, data visualization tools, and SQL is also essential. Additionally, critical thinking, communication, and familiarity with AI-assisted tools can significantly boost your efficiency and decision-making.

How does Data Science compare to Machine Learning?

Data Science focuses on the entire process of working with data, while machine learning is a subset of data science that specifically deals with developing and applying algorithms that allow computers to learn from and make predictions based on data.

What impact does Data Science have on the industry?

Data Science drives innovation and efficiency across various industries by providing actionable insights, improving decision-making, and optimizing processes. For example, it helps businesses understand market trends, customer behavior, and operational efficiency.

Is a data science course difficult?

The difficulty level can vary depending on your background and the course's complexity. Courses that offer hands-on practice and support can make the learning process more manageable. Basic understanding of statistics and programming can help ease the difficulty.

What degree do you need for data science?

While a specific degree is not always required, many data scientists hold degrees in fields such as Computer Science, Statistics, Mathematics, or Engineering. Some positions may require advanced degrees or specialized certifications, but practical experience and skills can also be highly valuable.

1.	Introduction to Neural Networks with Python	4 hours	25 chapters
2.	Introduction to Machine Learning with Python	4 hours	32 chapters
3.	Introduction to NLP with Python	5 hours	29 chapters
4.	Introduction to TensorFlow	2 hours	16 chapters
5.	Linear Regression with Python	2 hours	19 chapters