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 Activation Function AnalysisActive 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) Approximate ReasoningApproximation TheoryArrow Data ModelArtificial immune systemsAttention Mechanisms TheoryAutomated Search with scikit-learnAutoregressive GenerationBackward Difference CodingBagging and Random ForestsBayes' TheoremBayesian NetworksBayesian OptimizationBernoulli DistributionBias–Variance TradeoffBoosting AlgorithmsCalibration Metrics (ECE, MCE, Brier Score)CatBoost ModelingCatastrophic Forgetting AnalysisCategorical Feature HandlingChain-of-Thought Prompting Chi-Square AnalysisClassification Loss AnalysisClassification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) Cloud Compute PatternsCloud Data Science WorkflowsCloud Mental ModelsCloud Networking ConceptsCloud Storage ArchitecturesClustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) Clustering fundamentals and algorithms Coefficient VisualizationColumnar Data RepresentationCommittee-Based QueryingCompression Trade-off ReasoningConcentration of MeasureConditional IndependenceConjugate PriorsContinual Learning TheoryConvergence TheoryConvex AnalysisConvolutional Neural NetworksCoordinate Reference SystemsCorrelation AnalysisCovariance and eigen decomposition Cross-validation techniquesCurse of DimensionalityDBSCAN: noise handling and irregular shapes Data Access PatternsData Cleaning Data InteroperabilityData Leakage PreventionData PreprocessingData Preprocessing with TransformersData Privacy Concepts Data StorytellingData Transformation Data Visualization with matplotlib and seabornData normalization and distance metrics Deduplication AlgorithmsDegrees of TruthDensity-Weighted SamplingDeployment Best PracticesDescriptive StatisticsDiffusion Model TheoryDiffusion Models Dimensionality reduction Dimensionality reduction evaluation Distance CollapseDocument Chunking and IndexingDrift Detection FundamentalsDynamic Programming MethodsEffective DocumentationEmpirical Risk MinimizationEncoding Leakage PreventionEnd-to-end model development and evaluationEnsemble Learning FundamentalsEntropy and CompressionEntropy and Rate–Distortion AnalysisEstimator IntrospectionEthical AI PrinciplesEthical Decision-Making Evaluation Metrics for Generative AIEvaluation Under Distribution ShiftEvolutionary optimization Experiment Tracking with MLflowExperimental Data PreparationExplainable AI FundamentalsExploratory Data AnalysisExponential Family UnderstandingExpressivity of Neural NetworksFailure Analysis in RAGFailure Mode DiagnosisFairness 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 If–Then RulesFuzzy Inference SystemsFuzzy Logical OperatorsFuzzy Matching in PythonFuzzy SetsGAN FundamentalsGANs Gaussian DistributionGaussian Mixture Models: probabilistic clusteringGeneralization BoundsGenerative AI Genetic algorithms Geometric Implications for ML AlgorithmsGeometric InterpretabilityGeospatial Data FundamentalsGeospatial VisualizationGradient Boosting for TSGradient DescentGradient Descent Graph Embedding IntuitionGraph LaplaciansGraph Representation in PythonGraph Theory for MLGraphSAGE ConceptsGymnasium BasicsHandling missing and categorical data Helmert CodingHierarchical clustering and dendrograms High-Cardinality Feature EncodingHigh-Dimensional Geometry IntuitionHistogram BinningHybrid Rule-Based SystemsHyperparameter TuningHyperparameter Tuning FundamentalsHypothesis TestingIdentity and Access ManagementImage Processing with OpenCVImplementing recurrent networks in PyTorchImplicit Bias in Machine LearningImplicit Regularization in Deep NetworksIn-Context Learning TheoryInductive BiasInductive Bias ReasoningInformation Bottleneck and MDLInformation Theory BasicsInformation Theory in NLPInformation-Theoretic LossesIntegrals Interpreting Generalization BoundsIsolation Forest ImplementationIsotonic RegressionJupyter Notebook ProficiencyK-Means: principles and cluster optimization Kernel MethodsKnowledge 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 ModelingLikelihood vs ProbabilityLimits & Derivatives Limits of LLM GeneralizationLinear Algebra FoundationsLinear Algebra for Deep LearningLinear 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 ModelingMarkov Random FieldsMathematical Formulation of GANsMathematical Foundations of AttentionMathematical Foundations of Loss FunctionsMathematical OptimizationMatrix Decomposition Maximum-Margin SolutionsMean-CenteringMembership FunctionsMinimum-Norm SolutionsMissing Value Imputation Model BlendingModel Deployment with FastAPI and DockerModel Evaluation and DiagnosticsModel Evaluation and GeneralizationModel InterpretabilityModel InterpretationModel Monitoring and CI/CDModel RegularizationModel Scaling ConceptsModel Selection UtilitiesModel Training and EvaluationModel-Based Drift DetectionMomentum MethodsMonitoring Model DegradationMonte Carlo TechniquesMulti-Armed Bandit AlgorithmsMulti-Head Attention ConceptsMultinomial DistributionMultivariate AnalysisNatural Language HandlingNatural Language ProcessingNeural Network Architecture AnalysisNeural Network Compression TheoryNeural Network TheoryNeural NetworksNeuroevolutionNode ClassificationNormalization (L1, L2, Max)Null Handling in ArrowODE Formulations in Generative ModelsObject Detection ApproachesOffline vs Online Evaluation ReasoningOne-Class SVM for Novelty DetectionOptimization Constraints in Fine-TuningOptimization in Neural NetworksOutlier Detection Outlier Detection FundamentalsOverfitting and RegularizationPAC Generalization BoundsPEFT Deployment ReasoningPEFT TheoryPGM Inference and LearningParameter Space GeometryParticle swarm optimizationPattern MiningPipeline Automation with AirflowPipeline BuildingPipeline CompositionPipeline ConstructionPlatt ScalingPolynomial CodingPopulation Stability IndexPositional Encoding ConceptsPreprocessing PipelinesPrincipal Component Analysis TheoryPrincipal component analysis (PCA) Probabilistic Graphical ModelsProbabilistic Model CalibrationProbability DistributionsProbability Distributions IntuitionProbability Rules Probability in Loss FunctionsProcessing time series and sequential dataPrompt Engineering Fundamentals Prompt EvaluationPrompt Refinement Prompt-Based GeneralizationPyArrow API UsagePyTorch BasicsPython Classification ModelsPython Data StructuresPython ProgrammingQuantization and Pruning MathematicsRAG Evaluation MetricsRAG Pipeline ArchitectureRAG System Design PatternsRIPPER AlgorithmRademacher ComplexityRandom Walks on GraphsReasoning over Knowledge GraphsRecord Linkage TechniquesRegression Loss AnalysisRegression metrics (MSE, RMSE, MAE, R²) Regulatory AwarenessReinforcement Learning FoundationsReliability DiagramsReproducibility in ML WorkflowsReproducible Analysis HabitsResponsible AI Frameworks Retrieval-Augmented Generation FundamentalsRisk Minimization TheoryRobust Model AssessmentRole 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 Serverless and Event-Driven DesignSimilarity Scoring for GraphsSpatial Joins and OverlaysSpatial OperationsSpectral Graph TheorySpectral TheoryStability–Plasticity Trade-OffsStandardizationStatistical Anomaly DetectionStatistical Drift MetricsStatistical InterpretationStatistical Measures Stochastic Differential Equations (SDEs)Stochastic OptimizationStress Testing ML ModelsStructured Output Design Subword Tokenization AlgorithmsSwarm intelligenceTemporal ValidationTemporal-Difference LearningTensorFlow BasicsTheoretical Foundations of Zero-Shot GeneralizationTheoretical Limits of LearningTheoretical OverfittingTime Series AnalysisTime Series WindowingTokenization TheoryTrade-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 CollapseUniform ConvergenceVAEs VC DimensionVariational Inference & ELBOVector Search TheoryVector and Raster Data HandlingVectors & Matrices Vocabulary OptimizationWeight-of-Evidence EncodingWhitening and DecorrelationWorkflow AutomationXAI Methods and ConceptsXGBoost Modelingscikit-learn API Usagescikit-learn Active Learning Implementationt-Norms and t-Conormst-test and z-test Application

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Courses

Browse Data Science courses and projects

Level

Type of lesson

Technologies

Explainable AI (XAI) Basics

1 hour

15 chapters

Beginner

2 STUDYING NOW

Acquired skills: Explainable AI Fundamentals, XAI Methods and Concepts, Ethical AI Principles, AI Transparency Awareness

Exploratory Data Analysis with Python

2 hours

18 chapters

Beginner

5 STUDYING NOW

Acquired skills: Exploratory Data Analysis, Descriptive Statistics, Data Visualization with matplotlib and seaborn, Correlation Analysis, Multivariate Analysis, Data Storytelling

Feature Scaling and Normalization in Python

2 hours

19 chapters

Beginner

1 STUDYING NOW

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

Geometry of High-Dimensional Data

2 hours

10 chapters

Advanced

1 STUDYING NOW

Acquired skills: High-Dimensional Geometry Intuition, Curse of Dimensionality, Concentration of Measure, Distance Collapse, Geometric Implications for ML Algorithms

Geospatial Data Science with Python

2 hours

13 chapters

Intermediate

1 STUDYING NOW

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

Introduction to Reinforcement Learning with Python

6 hours

37 chapters

Advanced

3 STUDYING NOW

Acquired skills: Reinforcement Learning Foundations, Multi-Armed Bandit Algorithms, Dynamic Programming Methods, Monte Carlo Techniques, Temporal-Difference Learning, Gymnasium Basics

Introduction to Time Series Forecasting

2 hours

15 chapters

Intermediate

1 STUDYING NOW

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

Machine Learning for Time Series Forecasting

2 hours

12 chapters

Intermediate

1 STUDYING NOW

Acquired skills: Time Series Windowing, Feature Engineering for TS, Tree-Based Forecasting, Gradient Boosting for TS, Temporal Validation, Forecasting Strategies, Model Evaluation and Diagnostics

Mastering scikit-learn API and Workflows

2 hours

19 chapters

Intermediate

1 STUDYING NOW

Acquired skills: scikit-learn API Usage, Pipeline Composition, Data Preprocessing with Transformers, Model Selection Utilities, Estimator Introspection, Reproducibility in ML Workflows

Mathematical Foundations of Neural Networks

2 hours

9 chapters

Advanced

2 STUDYING NOW

Acquired skills: Neural Network Theory, Linear Algebra for Deep Learning, Activation Function Analysis, Approximation Theory, Expressivity of Neural Networks

Principal Component Analysis in Python

2 hours

12 chapters

Intermediate

1 STUDYING NOW

Acquired skills: Dimensionality reduction , Principal component analysis (PCA) , Covariance and eigen decomposition

Probability Distributions for Machine Learning

3 hours

15 chapters

Advanced

1 STUDYING NOW

Acquired skills: Probability Distributions Intuition, Exponential Family Understanding, Gaussian Distribution, Bernoulli Distribution, Multinomial Distribution, Likelihood vs Probability, Conjugate Priors, Probability in Loss Functions

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Codefinity

Video-based learning platforms

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