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:
AI Transparency AwarenessARIMA ModelingAdaptive AlgorithmsAdvanced ARIMA TechniquesAdversarial 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-learnBayes' TheoremBayesian OptimizationClassification 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 NetworksCovariance and eigen decomposition Cross-validation techniquesDBSCAN: noise handling and irregular shapes Data Cleaning Data Leakage PreventionData PreprocessingData Transformation Data normalization and distance metrics Diffusion Models Dimensionality reduction Dimensionality reduction evaluation Drift Detection FundamentalsDynamic Programming MethodsEnd-to-end model development and evaluationEthical AI PrinciplesEvaluation Metrics for Generative AIEvolutionary optimization Experiment Tracking with MLflowExplainable AI FundamentalsFeature Encoding Feature Engineering Feature ScalingFeature Scaling Feature Selection Feature Selection MethodsFine-tuning Pre-trained ModelsForecast Evaluation MetricsFunctions & SetsGAN FundamentalsGANs Gaussian Mixture Models: probabilistic clusteringGenerative AI Genetic algorithms Gradient DescentGradient Descent Gymnasium BasicsHandling missing and categorical data Hierarchical clustering and dendrograms Hyperparameter TuningHyperparameter Tuning FundamentalsImage Processing with OpenCVImplementing recurrent networks in PyTorchInformation-Theoretic LossesIntegrals Isolation Forest ImplementationK-Means: principles and cluster optimization Kolmogorov–Smirnov TestL1, L2, and Elastic Net RegularizationLimits & 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 Deployment with FastAPI and DockerModel Evaluation and GeneralizationModel Monitoring and CI/CDModel Training and EvaluationModel-Based Drift DetectionMomentum MethodsMonitoring Model DegradationMonte Carlo TechniquesMulti-Armed Bandit AlgorithmsMulti-Head Attention ConceptsNatural 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 ConstructionPopulation Stability IndexPreprocessing PipelinesPrincipal component analysis (PCA) Probability DistributionsProbability Rules Processing time series and sequential dataPyTorch BasicsPython Classification ModelsPython Data StructuresPython ProgrammingRegression Loss AnalysisRegression metrics (MSE, RMSE, MAE, R²) Reinforcement Learning FoundationsRisk Minimization TheorySelf-Attention IntuitionSeries Analysis StandardizationStatistical Anomaly DetectionStatistical Drift MetricsStatistical Measures Stochastic OptimizationSwarm intelligenceTemporal-Difference LearningTensorFlow BasicsTime Series AnalysisTransfer Learning FundamentalsTransfer Learning in CVTransfer Learning in NLPTransformer Architecture UnderstandingTransformers Understanding GAN VariantsUnderstanding RNNs, LSTMs, and GRUsVAEs Vectors & Matrices Whitening and DecorrelationXAI Methods and Concepts
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Browse Data Science courses and projects
2 hours
15 chapters
1 hour 16 chapters
2 hours 18 chapters
3 hours 22 chapters
1 hour 11 chapters
2 hours 15 chapters
Level
Type of lesson
Technologies
course
Introduction to Time Series Forecasting
2 hours 15 chaptersIntermediate
Acquired skills: Time Series Analysis, ARIMA Modeling, Forecast Evaluation Metrics, Advanced ARIMA Techniques
course
MLOps for Machine Learning Engineers
1 hour 16 chaptersBeginner
1 STUDYING NOW
Acquired skills: MLOps Fundamentals, Experiment Tracking with MLflow, Model Deployment with FastAPI and Docker, Pipeline Automation with Airflow, Model Monitoring and CI/CD
course
Mathematics of Optimization in ML
2 hours 18 chaptersBeginner
Acquired skills: Mathematical Optimization, Gradient Descent, Convex Analysis, Stochastic Optimization, Momentum Methods, Adaptive Algorithms, Convergence Theory
course
Outlier and Novelty Detection in Practice
3 hours 22 chaptersIntermediate
Acquired skills: Outlier Detection Fundamentals, Statistical Anomaly Detection, Isolation Forest Implementation, Local Outlier Factor Analysis, One-Class SVM for Novelty Detection, Algorithm Evaluation and Comparison
course
Transfer Learning Essentials
1 hour 11 chaptersBeginner
1 STUDYING NOW
Acquired skills: Transfer Learning Fundamentals, Fine-tuning Pre-trained Models, Transfer Learning in CV, Transfer Learning in NLP
course
Understanding Loss Functions in Machine Learning
2 hours 15 chaptersIntermediate
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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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.
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100+ Top-Rated coursesCompletion certificatesAI-Assistant in all courses20+ hands-on Real-world projectsPersonalized study tracksUnlimited workspacesTop courses in the Data Science category
1. | Introduction to Neural Networks |  4 hours |  25 chapters |
2. | ML Introduction with scikit-learn | 4 hours | 32 chapters |
3. | Introduction to NLP | 5 hours | 29 chapters |
4. | Introduction to TensorFlow | 3 hours | 18 chapters |
5. | Linear Regression with Python | 2 hours | 19 chapters |
1. Introduction to Neural Networks
Hours
Chapters2. ML Introduction with scikit-learn
HoursChapters3. Introduction to NLP
HoursChapters4. Introduction to TensorFlow
HoursChapters5. Linear Regression with Python
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