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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1 hour 10 chapters
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1 hour 16 chapters
2 hours 12 chapters
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1 hour 15 chapters
2 hours 18 chapters
Level
Type of lesson
Technologies
course
AI Ethics 101
1 hour 12 chaptersBeginner
Acquired skills: AI Ethics Fundamentals , Ethical Decision-Making , Fairness and Bias Analysis , Transparency Principles , Accountability in AI , Data Privacy Concepts , Responsible AI Frameworks , Regulatory Awareness
course
Active Learning with Python
1 hour 10 chaptersIntermediate
Acquired skills: Active Learning Fundamentals, Label Efficiency Techniques, Sampling Strategies in ML, Uncertainty-Based Querying, Committee-Based Querying, Density-Weighted Sampling, scikit-learn Active Learning Implementation, Learning Curve Analysis
course
Advanced Tree-Based Models with Python
1 hour 9 chaptersIntermediate
Acquired skills: CatBoost Modeling, XGBoost Modeling, LightGBM Modeling, Model Regularization, Categorical Feature Handling, Model Interpretation, Model Blending, Deployment Best Practices
course
Applied Hypothesis Testing & A/B Testing
3 hours 31 chaptersBeginner
Acquired skills: Hypothesis Testing, t-test and z-test Application, Chi-Square Analysis, A/B Test Design, Experimental Data Preparation, Statistical Interpretation
course
Bio-Inspired Algorithms with Python
1 hour 16 chaptersBeginner
Acquired skills: Evolutionary optimization , Swarm intelligence, Genetic algorithms , Particle swarm optimization, Artificial immune systems, Neuroevolution
course
Data Cleaning Techniques in Python
2 hours 12 chaptersIntermediate
Acquired skills: Fuzzy Matching in Python, Deduplication Algorithms, Record Linkage Techniques, Advanced Text Cleaning
course
Data Preprocessing and Feature Engineering with Python
1 hour 12 chaptersBeginner
Acquired skills: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building
course
Diffusion Models and Generative Foundations
2 hours 12 chaptersAdvanced
Acquired skills: Diffusion Model Theory, Markov Chains in Generative Modeling, Variational Inference & ELBO, Score Matching, Stochastic Differential Equations (SDEs), ODE Formulations in Generative Models
course
Ensemble Learning Techniques with Python
1 hour 14 chaptersBeginner
Acquired skills: Ensemble Learning Fundamentals, Bagging and Random Forests, Boosting Algorithms, Advanced Ensemble Integration
course
Evaluation Metrics in Machine Learning with Python
2 hours 16 chaptersIntermediate
Acquired skills: Classification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) , Regression metrics (MSE, RMSE, MAE, R²) , Clustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) , Dimensionality reduction evaluation , Anomaly detection evaluation , Cross-validation techniques
course
Explainable AI (XAI) Basics
1 hour 15 chaptersBeginner
Acquired skills: Explainable AI Fundamentals, XAI Methods and Concepts, Ethical AI Principles, AI Transparency Awareness
course
Exploratory Data Analysis with Python
2 hours 18 chaptersBeginner
Acquired skills: Exploratory Data Analysis, Descriptive Statistics, Data Visualization with matplotlib and seaborn, Correlation Analysis, Multivariate Analysis, Data Storytelling
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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.
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250+ Top-Rated coursesCompletion certificatesAI-Assistant in all courses40+ hands-on Real-world projectsPersonalized study tracksUnlimited workspacesTop courses in the Data Science category
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 |
1. Introduction to Neural Networks with Python
Hours
Chapters2. Introduction to Machine Learning with Python
HoursChapters3. Introduction to NLP with Python
HoursChapters4. Introduction to TensorFlow
HoursChapters5. Linear Regression with Python
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