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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A/B Test DesignAI Ethics Fundamentals AI Transparency AwarenessARIMA ModelingAccountability in AI Adaptive AlgorithmsAdvanced ARIMA TechniquesAdvanced Text CleaningAdversarial Training ConceptsAlgorithm Evaluation and ComparisonAnalyzing GAN Training ChallengesAnomaly detection evaluation Applying RNNs to NLP tasks (sentiment analysis) Artificial immune systemsAttention Mechanisms TheoryAutomated Search with scikit-learnBackward Difference CodingBayes' TheoremBayesian OptimizationCatBoost 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 VisualizationConvergence TheoryConvex AnalysisConvolutional Neural NetworksCorrelation 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 AlgorithmsDeployment Best PracticesDescriptive StatisticsDiffusion Models Dimensionality reduction Dimensionality reduction evaluation Drift Detection FundamentalsDynamic Programming MethodsEncoding Leakage PreventionEnd-to-end model development and evaluationEthical AI PrinciplesEthical Decision-Making Evaluation Metrics for Generative AIEvolutionary optimization Experiment Tracking with MLflowExperimental Data PreparationExplainable AI FundamentalsExploratory Data AnalysisFairness and Bias Analysis Feature 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 Gradient Boosting for TSGradient DescentGradient Descent Gymnasium BasicsHandling missing and categorical data Helmert CodingHierarchical clustering and dendrograms High-Cardinality Feature EncodingHyperparameter TuningHyperparameter Tuning FundamentalsHypothesis TestingImage Processing with OpenCVImplementing recurrent networks in PyTorchInformation-Theoretic LossesIntegrals Isolation Forest ImplementationK-Means: principles and cluster optimization Kolmogorov–Smirnov TestL1, L2, and Elastic Net RegularizationLeave-One-Out EncodingLightGBM ModelingLimits & Derivatives Linear Regression with PythonLinear Transformations Local Outlier Factor AnalysisLogistic RegressionLoss Function Selection and ComparisonMLOps FundamentalsMachine Learning with scikit-learnManual Search MethodsMathematical 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 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 NetworksNeuroevolutionNormalization (L1, L2, Max)Object Detection ApproachesOne-Class SVM for Novelty DetectionOutlier Detection Outlier Detection FundamentalsOverfitting and RegularizationParticle swarm optimizationPipeline Automation with AirflowPipeline BuildingPipeline ConstructionPolynomial CodingPopulation Stability IndexPreprocessing PipelinesPrincipal component analysis (PCA) Probability DistributionsProbability Rules Processing time series and sequential dataPrompt Engineering Fundamentals Prompt EvaluationPrompt Refinement PyTorch BasicsPython Classification ModelsPython Data StructuresPython ProgrammingRecord Linkage TechniquesRegression Loss AnalysisRegression metrics (MSE, RMSE, MAE, R²) Regulatory AwarenessReinforcement Learning FoundationsResponsible AI Frameworks Risk Minimization TheoryRole and Context Prompting Self-Attention IntuitionSeries Analysis StandardizationStatistical Anomaly DetectionStatistical Drift MetricsStatistical InterpretationStatistical Measures Stochastic OptimizationStructured Output Design Swarm intelligenceTemporal ValidationTemporal-Difference LearningTensorFlow BasicsTime Series AnalysisTime Series WindowingTransfer Learning FundamentalsTransfer Learning in CVTransfer Learning in NLPTransformer Architecture UnderstandingTransformers Transparency Principles Tree-Based ForecastingUnderstanding GAN VariantsUnderstanding RNNs, LSTMs, and GRUsVAEs Vectors & Matrices Weight-of-Evidence EncodingWhitening and DecorrelationXAI Methods and ConceptsXGBoost Modelingt-test and z-test Application

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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