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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning LifecycleIntroduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data InsightsDescriptive StatisticsInferential StatisticsHow to Understand Population Distributions?
Reading Data Files into PythonDifferent Variable Datatypes
Probability for Data ScienceBasic Concepts of ProbabilityAxioms of ProbabilityConditional Probability
Central Tendencies for Continuous VariablesSpread of DataKDE plots for Continuous VariableOverview of Distribution for Continuous variablesNormal DistributionSkewed DistributionSkeweness and KurtosisDistribution for Continuous Variable
Central Tendencies for Categorical VariablesUnderstanding Discrete DistributionsPerforming EDA on Categorical Variables
Dealing with Missing ValuesUnderstanding OutliersIdentifying Outliers in DataOutlier Detection in PythonOutliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and PopulationCentral Limit TheoremConfidence Interval and Margin of Error
Bivariate Analysis Introduction
CovariancePearson CorrelationSpearman's Correlation & Kendall's TauCorrelation versus CausationTabular and Graphical methods for Bivariate AnalysisPerforming Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical VariablesIntroduction to Hypothesis TestingP-valueTwo sample Z-testT-testT-test vs Z-testPerforming Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares TestBivariate Analysis on Categorical Categorical Variables
Multivariate AnalysisA Comprehensive Guide to Data ExplorationThe Data Science behind IPL
Supervised Learning vs Unsupervised LearningReinforcement LearningGenerative and Descriminative ModelsParametric and Non Parametric model
Machine Learning PipelinePreparing DatasetBuild a Benchmark Model: RegressionBuild a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should knowConfusion MatrixAccuracyPrecision and RecallAUC-ROCLog LossR2 and Adjusted R2
Dealing with Missing ValuesReplacing Missing ValuesImputing Missing Values in DataWorking with Categorical VariablesWorking with OutliersPreprocessing Data for Model Building
Understanding Cost FunctionUnderstanding Gradient DescentMath Behind Gradient DescentAssumptions of Linear RegressionImplement Linear Regression from ScratchTrain Linear Regression in PythonImplementing Linear Regression in RDiagnosing Residual Plots in Linear Regression ModelsGeneralized Linear ModelsIntroduction to Logistic RegressionOdds RatioImplementing Logistic Regression from ScratchIntroduction to Scikit-learn in PythonTrain Logistic Regression in pythonMulticlass using Logistic RegressionHow to use Multinomial and Ordinal Logistic Regression in R ?Challenges with Linear RegressionIntroduction to RegularisationImplementing RegularisationRidge RegressionLasso Regression
Introduction to K Nearest NeighboursDetermining the Right Value of K in KNNImplement KNN from ScratchImplement KNN in Python
Bias Variance TradeoffIntroduction to Overfitting and UnderfittingVisualizing Overfitting and UnderfittingSelecting the Right ModelWhat is Validation?Hold-Out ValidationUnderstanding K Fold Cross Validation
Introduction to Feature SelectionFeature Selection AlgorithmsMissing Value RatioLow Variance FilterHigh Correlation FilterBackward Feature EliminationForward Feature SelectionImplement Feature Selection in PythonImplement Feature Selection in R
Introduction to Decision TreePurity in Decision TreeTerminologies Related to Decision TreeHow to Select Best Split Point in Decision Tree?Chi-SquaresInformation GainReduction in VarianceOptimizing Performance of Decision TreeTrain Decision Tree using Scikit LearnPruning of Decision Trees
Introduction to Feature EngineeringFeature TransformationFeature ScalingFeature EngineeringFrequency EncodingAutomated Feature Engineering: Feature Tools
Introduction to Naive BayesConditional Probability and Bayes TheoremIntroduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive BayesMath behind Naive BayesTypes of Naive BayesImplementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification ProblemEvaluation Metrics: Multi Class Classification
Introduction to Ensemble TechniquesBasic Ensemble TechniquesImplementing Basic Ensemble TechniquesFinding Optimal Weights of Ensemble Learner using Neural NetworkWhy Ensemble Models Work well?
Introduction to StackingImplementing StackingVariants of StackingImplementing Variants of StackingIntroduction to BlendingBootstrap SamplingIntroduction to Random SamplingHyper-parameters of Random ForestImplementing Random ForestOut-of-Bag (OOB) Score in the Random ForestIPL Team Win Prediction Project Using Machine LearningIntroduction to BoostingGradient Boosting AlgorithmMath behind GBMImplementing GBM in pythonRegularized Greedy ForestsExtreme Gradient BoostingImplementing XGBM in pythonTuning Hyperparameters of XGBoost in PythonImplement XGBM in R/H2OAdaptive BoostingImplementing Adaptive BoosingLightGBMImplementing LightGBM in PythonCatboostImplementing Catboost in Python
Different Hyperparameter Tuning methodsImplementing Different Hyperparameter Tuning methodsGridsearchCVRandomizedsearchCVBayesian Optimization for Hyperparameter TuningHyperopt
Understanding SVM AlgorithmSVM Kernels In-depth Intuition and Practical ImplementationSVM Kernel TricksKernels and Hyperparameters in SVMImplementing SVM from Scratch in Python and R
Introduction to Principal Component AnalysisSteps to Perform Principal Compound AnalysisComputation of Covariance MatrixFinding Eigenvectors and EigenvaluesImplementing PCA in pythonVisualizing PCAA Brief Introduction to Linear Discriminant AnalysisIntroduction to Factor Analysis
Introduction to ClusteringApplications of ClusteringEvaluation Metrics for ClusteringUnderstanding K-MeansImplementation of K-Means in PythonImplementation of K-Means in RChoosing Right Value for KProfiling Market Segments using K-Means ClusteringHierarchical ClusteringImplementation of Hierarchial ClusteringDBSCANDefining Similarity between clustersBuild Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to DaskWorking with CuML
Introduction to Machine Learning InterpretabilityFramework and Interpretable Modelsmodel Agnostic Methods for InterpretabilityImplementing Interpretable ModelUnderstanding SHAPOut-of-Core MLIntroduction to Interpretable Machine Learning ModelsModel Agnostic Methods for InterpretabilityGame Theory & Shapley Values
Introduction to AutoMLImplementation of MLBoxIntroduction to PyCaretTPOTAuto-SklearnEvalML
Pickle and JoblibIntroduction to Model Deployment
Deploying Machine Learning Model using StreamlitDeploying ML Models in DockerDeploy Using StreamlitDeploy on HerokuDeploy Using NetlifyIntroduction to Amazon SagemakerSetting up Amazon SageMakerUsing SageMaker Endpoint to Generate InferenceDeploy on Microsoft Azure CloudIntroduction to Flask for ModelDeploying ML model using Flask
Model Deployment in AndroidModel Deployment in Iphone

19 Free Data Science Courses by Harvard and IBM

Aayush Tyagi
Aayush Tyagi Last Updated : 12 Oct, 2024
5 min read

Introduction

Data science is a rapidly growing tech field that’s transforming business decision-making. To break into this field, you need the right skills. Fortunately, top institutions like Harvard and IBM offer free online courses. These courses cover everything from basic programming to advanced machine learning. In this article, we’ve listed some of the best free courses from Harvard and IBM. They are great for beginners and intermediate learners who want to boost their data science skills.

IBM and Harvard Free Data Science Course

Table of contents

Harvard Free Courses

1. Introduction to Programming with Scratch

Harvard’s CS50’s Introduction to Programming with Scratch is a great starting point for anyone with zero programming experience. It uses Scratch, a visual programming language where you create code by dragging and dropping graphical blocks—ideal for beginners.

Course Overview

  • Functions: Instructions that perform specific tasks.
  • Return Values: Results provided by functions.
  • Conditions: Logic that decides if actions should happen.
  • Loops: Repeat certain actions.
  • Variables: Store information.

Course Features

  • Helps you create animations, games, and interactive art.
  • Prepares you for advanced programming in languages like Java and Python.

Enrollment Details

  • Free course with an option to get a certificate for $299.
  • Part of Harvard’s broader CS50 curriculum.

Checkout the this free Harvard data science courses here.

2. Introduction to Artificial Intelligence with Python

This course dives into artificial intelligence (AI) and machine learning (ML) with Python. It’s designed for those who want to get hands-on with AI.

Course Overview

  • Graph Search Algorithms: Navigate data structures.
  • Reinforcement Learning: Learn how agents make decisions based on rewards and penalties.
  • Machine Learning Principles: Explore techniques that help systems learn from data.
  • Designing Intelligent Systems: Learn to build systems that think and learn.

Course Features

  • Understand AI concepts and algorithms.
  • Be proficient in Python for AI.
  • Work on machine learning libraries and design intelligent systems.

Enrollment Details

  • The course also offers a verified certificate for $299.

You can find more details of this Harvard free data science courses here.

3. Introduction to Data Science with Python

This course focuses on teaching practical skills in data science using Python. It gives you hands-on experience solving real-world problems.

Learning Outcome

  • Coding in Python for statistics and modeling.
  • Working with libraries like Pandas, NumPy, and Matplotlib.
  • Running machine learning models like linear regression and classification models.

Enrollment Details

  • A certificate is available for $299.

Click here to explore this Harvard free data science course.

4. Machine Learning and AI with Python

In this course, you’ll dive deeper into machine learning techniques and their applications.

Learning Outcome

  • Build and evaluate machine learning models.
  • Understand decision trees, random forests, and gradient boosting.
  • Identify and reduce data bias in machine learning models.

Enrollment Details

  • Certificate option is available for $299.

Find all the details for this courses here.

5. Data Science: Visualization

This course teaches the basics of effective data visualization using the R programming language and ggplot2 package. You’ll learn to communicate data-driven insights through visuals.

Learning Outcome

  • Create custom plots.
  • Analyze real-world data from health, economics, and infectious diseases.
  • Learn to spot flaws in common visualizations.

Enrollment Details

  • Certificate available for $219.

Explore this courses here.

6. Data Science: Probability

Harvard’s Data Science: Probability course provides a thorough understanding of probability theory and its importance in data science.

Learning Outcome

  • Understand random variables, independence, and expected values.
  • Conduct Monte Carlo simulations.
  • Use the Central Limit Theorem in data analysis.

Enrollment Details

  • Get a certificate for $149.

Click here to explore this free data science course by Harvard.

7. Data Science: Inference and Modeling

This course covers statistical inference and modeling techniques, often using election forecasting as a case study.

Key Topics

  • Estimations, margins of error, and confidence intervals.
  • Bayesian modeling techniques.
  • Aggregating data for better accuracy.

Enrollment Details

  • A certificate is available for $149.

Click here to explore this free Harvard University free course.

8. Data Science: Wrangling

Data wrangling is essential for any data science project. This course teaches you to clean and prepare data for analysis using R.

Learning Outcome

  • Import and clean data from various sources.
  • Use tidyverse tools for data manipulation.
  • Perform web scraping.

Enrollment Details

  • A certificate is available for $149.

Click here to explore this free Harvard University free data science course.

9. Data Science: Machine Learning

This course provides an introduction to machine learning concepts and focuses on building predictive models.

Learning Outcomes

  • Apply machine learning algorithms.
  • Perform cross-validation.
  • Build a movie recommendation system.

Enrollment Details

  • Certificate available for $149.

You can explore this free course here.

10. Data Science: Linear Regression

Linear regression is a fundamental statistical method, and this course teaches how to use it to analyze real-world data.

Learning Outcome

  • Understand linear regression theory.
  • Implement linear regression in R.
  • Evaluate model performance and make predictions.

Enrollment Detail

  • Certificate available for $149.

You can find details of this Harvard University free data science course here.

IBM Free Courses

1. What is Data Science?

This course provides a broad introduction to the field of data science and covers its core principles.

Learning Outcome

  • Understand what data science is.
  • Recognize its importance across industries.
  • Familiarize yourself with essential tools and techniques.

Click here to explore this IBM data science free course.

2. Analyzing Data with Python

Learn to analyze and visualize data using Python libraries like Pandas and Matplotlib.

Learning Outcome

  • Manipulate data with Python.
  • Perform data cleaning tasks.
  • Create visualizations to extract insights.

Click here to explore this IBM data science free course.

3. Python for Data Science, AI & Development

This course is a comprehensive introduction to using Python for data science and AI applications.

Learning Outcome

  • Using Python for data manipulation.
  • Implementing machine learning algorithms.
  • Creating effective data visualizations.

Click here to explore this IBM data science free course.

4. Python Basics for Data Science

Designed for beginners, this course introduces Python programming, with a focus on data science applications.

Learning Outcome

  • Understand Python’s syntax and data types.
  • Use Python for data analysis.
  • Implement basic data science techniques using popular libraries.

Click here to explore this IBM data science free course.

5. Data Analysis with Python

In this course, you’ll master the basics of data analysis using Python.

Learning Outcome

  • Manipulate data with Pandas.
  • Perform exploratory data analysis.
  • Use visualizations to communicate findings.

Click here to explore this IBM data science free course.

6. Applied Data Science Capstone

This capstone course allows learners to apply their data science skills in a real-world project.

Learning Outcome

  • Complete a full data science project from start to finish.
  • Apply various tools and techniques to solve complex problems.
  • Communicate findings through visualizations and reports.

Click here to explore this IBM data science free course.

7. Visualizing Data with Python

Learn to create effective data visualizations using Python libraries like Matplotlib and Seaborn.

Learning Outcome

  • Understand data visualization principles.
  • Create different types of visualizations.
  • Interpret visual data.

Click here to explore this IBM data science free course.

8. IBM Data Analyst Capstone Project

This capstone course lets you showcase your data analysis skills through a hands-on project.

Learning Outcome

  • Conduct a full data analysis.
  • Create visualizations to effectively communicate insights.
  • Develop a complete data analysis project, solving real-world problems.

Click here to explore this IBM data science free course.

9. Introduction to Data Analytics

This course gives an overview of data analytics and its essential concepts.

You’ll Learn To:

  • Understand key data analytics terms.
  • Apply analytical techniques.
  • Interpret data insights and communicate findings effectively.

Click here to explore this IBM data science free course.

Helpful Resources to Master Data Science

Conclusion

Whether you’re a complete beginner or looking to upskill your data science knowledge, these free courses from Harvard and IBM can be really helpful. So, pick the course that fits your goals and start your data science journey today!

Happy Learning! 📚

Aayush Tyagi
Aayush Tyagi

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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