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Free Online Data Science Courses At Harvard
Nowadays, Free online courses are becoming highly popular for many professionals due to their accessibility and validity. This popularity has led Harvard University in the USA to offer many professional free online courses for students across the world to access high-quality education and highly experienced faculty. In this blog, we are going to explain to you about the free online data science course at Harvard. These courses are online, easy to access, and for a short duration. The need for knowledgeable data scientists is expanding quickly in both the private and public sectors. The HarvardX Data Science program equips students with the fundamental knowledge base and practical skills needed to take on challenging data analysis tasks in the real world. The course enables you to build a crucial skill needed for becoming a skilled data science analyst by covering topics including probability, inference, regression, and machine learning.
What is Data Science?
Data Science is an analysis of data to get important information for businesses. It is a multidisciplinary method for analyzing massive volumes of data that integrates concepts and methods from the domains of mathematics, statistics, artificial intelligence, and computer engineering. Data scientists ask and receive answers to queries like what occurred, why it occurred, what will occur, and what else can be performed with the outcomes. Businesses in this era are equipped with data and the use of devices that can gather and store data automatically is also increasing. The online payment portals of many businesses such as e-commerce, medicine, finance, and other online product and service portals gather huge amounts of data. There is a requirement for data analysts to analyze and offer productive outcomes to businesses through this data.
Free Courses To Study Data Science At Harvard
There are many online courses available to study at Harvard University. Here we are listing the free Online Data Science Course at Harvard University.
1. Course of Introduction to Linear Models and Matrix Algebra at Harvard University
Many of today's tools for experimental design and high-dimensional data processing are based on Matrix Algebra. At the beginning of this online data analysis course, you will learn how to utilize matrix algebra to show linear models that are widely used to model changes between experimental units. You will also conduct statistical analysis on these inequities. During the study, you will execute matrix operations using the R programming language. The course is divided into seven parts due to the diversity of students' educational backgrounds.
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What will you learn? |
Matrix algebra operations |
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Linear models |
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Application of matrix algebra to data analysis |
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Matrix algebra notation |
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A brief introduction to the QR decomposition |
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Duration of Course |
4 weeks |
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Course Fee Structure |
Fee to study and $129 for a verified certificate. |
2. Course of Statistic And R at Harvard University
The R programming language is taught about the topic of statistical data and statistical analysis in the biological sciences within this course. You will master the fundamentals of statistical inference and how to construct p-values and confidence intervals while analyzing data with R code. The course uses R programming samples to assist students in realizing the interrelationship between theory and execution. To assess understanding and ability to conduct fundamental data analysis, problem sets involving R programming will be used. You will use visualization methods to investigate new data sets and choose the best strategy. You will be capable of presenting robust statistical procedures as substitutes when data does not fulfill the assumptions needed by traditional methodologies after completing this course of study. You will master the fundamentals of performing reproducible research by analyzing data using R scripts.
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What will you learn? |
Distributions |
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Random variables |
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Inference: p-values and confidence intervals |
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Non-parametric statistics |
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Exploratory Data Analysis |
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Duration of Course |
4 Weeks |
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Course Fee Structure |
Fee to study and $129 for a verified certificate |
3. Course of Statistical Inference and Modeling for High-throughput Experiments at Harvard University
This course will cover a variety of statistical subjects such as numerous test difficulties, error rates, error rate regulating strategies, incorrect discovery rates, q-values, and exploring data analysis. Later on, you will be exposed to the concept of statistical modeling and the way it is used to deal with high-throughput data. You will gain information about parametric distributions, which include binomial, exponential, and gamma distributions, as well as maximum likelihood estimation. The course includes various examples to demonstrate how these concepts are implemented in next-generation sequencing and microarray data. In the end, you will learn about hierarchical models and empirical Bayes, as well as a few examples of how they are applied in practice. The course provides R programming samples in an efficient manner that will assist in making the relationship between principles and execution.
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What will you learn? |
False Discovery Rate |
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Multiple comparison problem |
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Organizing high-throughput data |
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Bonferroni Correction |
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Family-Wise Error Rates |
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Duration of Course |
4 weeks |
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Course Fee Structure |
Fee to study and $129 for a verified certificate |
4. Course of Case Studies in Functional Genomics at Harvard University
In this course, starting with raw data, you will learn how to execute the standard processing and normalization processes that will bring you to the stage where you may examine important biological problems in this course. During the case studies, you will use investigative plots to acquire a broader understanding of the data shape and experiment results. You will also learn about FASTQ file quality control, aligning RNA-seq reads, visualizing alignments, and analyzing RNA-seq at the gene level, including counting reads in genes, Investigative Data Analysis, and variance stabilization for counts, count-based differential expression, normalization, and batch effects. Finally, the course will address RNA-seq at the transcript level, including calculating transcript expression and differential exon usage. You'll explore the fundamentals of analyzing DNA methylation data, such as analyzing raw data, normalizing it, and identifying regions of differential methylation throughout numerous samples. The course will conclude with a brief overview of the fundamental methods for analyzing ChIP-seq datasets, including read alignment, peak calling, and examining differential binding patterns across several samples.
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What will you learn? |
Analyzing RNA-seq data |
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Quality Assessment of Next-Generation Data |
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Analyzing DNA methylation data |
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Analyzing ChIP Seq data |
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Mapping reads |
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Duration of Course |
5 Weeks |
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Course Fee Structure |
Fee to study and $219 for a verified certificate |
5. Course of High-Dimensional Data Analysis at Harvard University
This is the data science course for you if you're interested in data analysis and interpretation. You begin by studying the mathematical notion of distance, which you then use to justify the usage of singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling, as well as its relationship to principal component analysis. The course will teach you about the batch effect, which is the most difficult data analytical aspect of genomics today, and how to recognize and correct it. The course will particularly define the basic concepts of principal component analysis and factor analysis, as well as explain how these principles are used for data visualization and analysis of high-throughput experimental data. In the end, the course will offer a quick overview of machine learning and its application to high-throughput data. It describes the broad idea of clustering analysis, as well as K-means and hierarchical clustering, and how they are used in genomics. It also describes prediction algorithms such as k-nearest neighbors, as well as the ideas of training sets, test sets, error rates, and cross-validation.
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What will you learn? |
Factor Analysis Dealing with Batch Effects |
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Multiple Dimensional Scaling Plots |
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Mathematical Distance |
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Dimension Reduction |
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Singular Value Decomposition and Principal Component Analysis |
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Duration of Course |
4 Weeks |
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Course Fee Structure |
Fee to study and $219 for a verified certificate. |
6. Course of Advanced Bioconductor at Harvard University
The course begins with approaches to the visualization of genome-scale data and provides tools to build interactive graphical interfaces to speed discovery and interpretation. Using knitr and markdown as basic authoring tools, the concept of reproducible research is developed, and the concept of an executable document is presented. In this framework reports are linked tightly to the underlying data and code, enhancing reproducibility and extensibility of completed analyses. You will investigate out-of-memory strategies for the study of very massive data resources, employing relational databases or HDF5 as "back ends" with familiar R interfaces. A regulated version of The Cancer Genome Atlas is used to demonstrate multi-omics data integration. Finally, you will investigate cloud-based resources created for the Encyclopaedia of DNA Elements (the ENCODE project). These look at transcription factor binding, ATAC-seq, and RNA-seq with CRISPR interference.
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What will you learn? |
Reproducible analysis methods |
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Memory-sparing representations of genomic assays |
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Working with multi-omic experiments on cancer |
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Targeted interrogation of cloud-scale genomic archives |
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Static and interactive visualization of genomic data |
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Duration of the course |
5 Weeks |
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Course Fee Structure |
Fee to study and $219 for a verified certificate. |
7. Course of Introduction to Bioconductors at Harvard University
The Course starts with a brief overview of essential biology, outlining what and why we measure. The emphasis of course then shifts to the two primary measurement methods: next-generation sequencing and microarrays. The training session then describes how to input raw data and observations into R and how to organize all of this data, whether generated locally or taken from public repositories or institutional archives. In general, genomic characteristics are recognized using intervals in genomic coordinates, and advanced algorithms for computing with genomic intervals will be thoroughly investigated. Packages such as limma have statistical approaches for evaluating gene-centric or pathway-centric hypotheses using genome-scale data; a few of these techniques will be demonstrated in lectures and laboratories.
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What will you learn? |
Preprocessing and normalization |
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The Bioconductor Genomic Ranges utilities |
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Genomic annotation |
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Introduction to high-throughput technologies |
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What we calculate with high-throughput technologies. |
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Duration of the course |
5 weeks |
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Course fee structure |
Fee to study and $219 for a verified certificate |
8. Course of Statistical, Principles, and Computational Tools for Reproducible Data Science at Harvard University
This course is intended for those who are conducting extensive data research. While many of us have a biomedical background, this course is intended for a wide range of data scientists. This course will cover the principles of methodologies and instruments for reproducible research to address the specific requirements of the scientific community. Students will take part in six sessions, each of which will consist of multiple case studies illustrating the importance of repeatable research methodologies in the field of science. Students as well as experts in biostatistics, computational biology, bioinformatics, and data science will benefit from this course. The course curriculum will include video lectures, case studies, collaborative interactions, and the usage of computational tools and software and services (such as R/RStudio and Git/Github), with a final presentation of a reproducible research project. The course will focus on Principles of Reproducible Science, Case Studies, Data Provenance, Statistical Methods for Reproducible Science, Computational Tools for Reproducible Science, and Reproducible Reporting Science.
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What will you learn? |
Fundamentals of Reproducible Science |
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Major elements for confirming data provenance and reproducible experimental design |
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Understand a series of concepts, thought patterns, analysis paradigms, and computational & statistical tools. |
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Statistical methods for reproducible data analysis |
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Computational tools for reproducible data analysis and version control |
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How to make novel methods and tools for reproducible research and reporting |
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How to write your reproducible paper. |
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Duration of the course |
8 weeks |
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Course fee structure |
Fee to study and $149 for a verified certificate |
9. Course of Causal Diagrams:- Drawing Assumptions Before Conclusions at Harvard University
Causal diagrams have transformed the way scholars ask: Does X have a causal effect on Y? They have developed into an important tool for researchers studying the impacts of treatments, exposures, and regulations. Caustic diagrams have helped clarify apparent paradoxes, highlight prevalent biases, and uncover adjustment variables by summarizing and communicating assumptions about a problem's causal structure. As a result, a solid knowledge of causal diagrams is growing more and more relevant in many scientific disciplines. The first five modules in this course present the idea of causal diagrams and explain their applications to causal inference. The fifth lesson presents a straightforward graphical representation of the bias of traditional statistical methods for confounding adjustment in the presence of time-varying covariates. The second portion of the course consists of a series of case studies that illustrate the practical uses of causal diagrams as they relate to real-world problems in the health and social sciences.
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What will you learn? |
Using causal diagrams to identify common biases |
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Using causal diagrams to guide data analysis |
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Using causal diagrams to guide data analysis |
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Method of drawing causal diagrams under various assumptions. |
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Duration of course |
9 weeks |
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Course fee structure |
Fee to study and $149 for a verified certificate |
10. Course of Introduction to Digital Humanities at Harvard University
How can we use these data sources to generate fresh inquiries? In the past, how did Chinese households organize themselves and their surroundings? How did African slaves from various civilizations establish colonies in the Americas? How can I make an interactive visualization for my students? These problems can be investigated using various digital tools, methodologies, and sources. As museums, libraries, archives, and other establishments have digitized their collections and artifacts, new methods and standards for converting such items into data that can be processed by machines have emerged. Humanities academics can now process massive amounts of textual material because of optical character recognition (OCR) and the Text Encoding Initiative (TEI). These advancements, however, are not restricted to text. These new types of inquiry have included sound, pictures, and video. This course will teach you the basics of how to manage the various components of digital humanities research and scholarship. This online course is designed to help you bring your field of study or curiosity to life using digital tools.
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What will you learn? |
What does the term “digital humanities” mean in different disciplines? |
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Method of using command-line functions to analyze text. |
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Method of using free tools to create visual text analysis. |
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How several file types can be utilized to create, gather, and organize data. |
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How basic digital tools function |
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Duration of course |
7 weeks |
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Course fee structure |
Fee to study and $219 for a verified certificate |
11. Course of Data Science: Capstone at Harvard University
It takes practice and experience to become an expert data scientist. By completing this capstone project, you will be able to put your knowledge and skills in R data analysis that you have acquired throughout the course to use. This final project will put your knowledge of data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning to the test. Unlike all of the other courses of our Professional Certificate Programme in Data Science, you will get substantially less help from the professors in this course. After you finish the project, you will be able to use your data product to present potential employers or educational programs, which will be an effective measure of your knowledge and skills in data science.
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What will you learn? |
Independently work on a data analysis project |
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How to apply the knowledge base and skills learned throughout the series to a real-world problem |
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Duration of course |
2 weeks |
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Course Fee Structure |
Fee to study and $149 for a verified certificate |
12. Course of Data Science: Inference and Modeling at Harvard University
Statistical inference and modeling are necessary for interpreting data that has been influenced by chance, and hence for data scientists. Students will learn these fundamental ideas in this course through an enlightening case study on election prediction. This course will demonstrate how inference and modeling may be used to construct statistical methodologies that make polls a useful tool, and how to do so using R. You will learn the ideas required to construct estimations and margins of error, as well as how to apply these to produce reasonably accurate predictions and provide an indication of the accuracy of your predictions. Confidence intervals and p-values, two ideas that are used frequently in data science, will become clear to you once you have learned this. Then, you will study Bayesian modeling to comprehend claims made about the likelihood of a candidate prevailing. Finally, the course will bring everything together at the end to develop a streamlined version of an election forecast model.
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What will you learn? |
Method of using models to aggregate data from different sources |
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The various concepts required to define estimates and margins of errors of populations, parameters, estimates, and standard errors to estimate data |
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Duration of course |
8 weeks |
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Course fee structure |
Fee to study and $149 for a verified certificate |
13. Course of Data Science: R Basics at Harvard University
The course will teach about the fundamentals of R programming. You will be able to use R more efficiently when using it to tackle a particular issue. You will learn the R skills required to address critical questions concerning inequalities in crime between states. The course module will teach you about R's functions and data types before diving into vector operations and when to employ more complex functions like sorting. You will learn ways to use general programming concepts such as "if-else" and "for loop" commands, as well as how to manipulate, analyze, and interpret data. Instead of learning each R skill you might need, you'll develop a solid foundation to prepare for the series' following, more in-depth courses, where you will learn ideas like probability, inference, regression, and machine learning. The need for knowledgeable data science specialists is growing rapidly and this course will equip you to handle practical data analysis problems.
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What will you learn |
Basic R syntax |
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How to carry out operations in R along with sorting, data wrangling using dplyr (Computer Program), and making plots. |
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Foundational R programming concepts such as data types, vector arithmetic, and indexing |
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Duration of course |
8 weeks |
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Course fee structure |
Fee to study and $219 for a verified certificate |
14. Course of Data Science: Machine Learning at Harvard University
Machine learning is perhaps where the most well-liked data science approaches originate from. Machine learning differs from previous computer-assisted decision-making methods in that it constructs prediction algorithms using data. A few of the most well-known products that make use of machine learning are spam detectors, speech recognition, movie suggestion systems, and handwriting readers used by the postal service. Students will master common machine learning techniques, principal component analysis, and regularization by creating a movie recommendation system in this course, which is an aspect of our Professional Certificate Program in Data Science. In this course, you'll learn about training data and how to use an array of data to look for relationships that might be predictive. You will learn how to develop algorithms with training data as you construct the movie recommendation system so that you can forecast the results for upcoming datasets. Moreover, you will know how to avoid overtraining using strategies like cross-validation.
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What will you learn? |
The basics of machine learning. |
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Several popular machine-learning. |
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How to build a recommendation system. |
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What is regularization and why it is useful |
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Duration of course |
8 weeks |
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Course fee structure |
Fee to study and $149 for a verified certificate |
15. Course of Data Science: Linear Regression at Harvard University
In order to quantify the link between two or more variables, linear regression is frequently used. Additionally, the use of linear regression and affecting correction in practice using R are covered in this course, which is a component of our Professional Certificate Program in Data Science. It is quite common in data science applications to be curious about the correlation between two or more variables. The inspiring case study that you learn in this course refers to the data-driven methodology described in Moneyball to build baseball teams. You will learn how to investigate confounding, which occurs when additional variables influence the relationship between two or more other variables, resulting in misleading associations. Linear regression is an effective method for removing confounders. It is critical to learn when to employ this strategy, and this course will teach you when to do so.
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What will you learn? |
About confounding and how to find it |
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How linear regression was originally made by Galton |
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How to find the relationships between variables by applying linear regression in R |
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Duration of the course |
8 weeks |
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Course fee structure |
Fee to study and $149 for a verified certificate. |
16. Course of Data Science: Productivity Tools at Harvard University
A typical data analysis project might be broken up into numerous pieces, each of which would contain several scripts and data files. Keeping everything organized might be difficult. This course shows how to use Unix/Linux as a means to organize files and directories on a computer and how to maintain the file system orderly. You will learn about the git version control system, a potent tool for tracking changes to your analyses and scripts. The course will give you a brief introduction to GitHub and show you how to utilize it to store your work in a collaborative repository. Last but not least, you will discover how to generate reports in R markdown, which enables you to combine text and code in a single document. Using the robust integrated desktop environment RStudio, we'll put it all together.
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What will you learn? |
How to start a repository on GitHub |
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Usage of Unix/Linux to manage file system |
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How to perform version control with git |
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How to take advantage of the many useful features provided by RStudio. |
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Duration of course |
8 weeks. |
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Course fee structure |
Fee to study and $149 for a verified certificate |
17. Course of Data Science: Probability at Harvard University
You will learn important ideas in probability theory in this course, which is a component of our Professional Certificate Program in Data Science. The events surrounding the financial crisis served as the inspiration for this course. The risk associated with several products sold by financial institutions was understated, which contributed to the financial crisis. We need to understand the fundamentals of probability in order to start understanding this extremely complex event. Important ideas covered in the course include random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical ideas are essential for doing statistical tests on data and determining whether the data you're analyzing is most likely related to an experimental procedure or chance. Probability theory is the mathematical foundation of statistical reasoning, and it is required for understanding data affected by chance, making data scientists necessary.
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What will you learn? |
How to perform a Monte Carlo simulation |
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Major topics in probability theory including random variables and independence |
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The importance of the Central Limit Theorem |
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The understanding of expected values and standard errors and how to calculate them in R. |
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Duration of course |
8 Weeks |
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Course fee structure |
Fee to study and $149 for a verified certificate |
18. Course of Data Science: Visualization at Harvard University
This course is a part of our Professional Certificate Program in Data Science and teaches the basis of data visualization and exploratory data analysis. Three inspiring examples will be used, as well as ggplot2, a data visualization application for the statistical computer language R. This online course will begin with basic datasets and progress through case studies on global health, economics, and infectious disease trends. This course will examine how mistakes, biases, systematic errors, and other unforeseen difficulties frequently result in data that should be handled with caution. The increasing availability of interesting information and computing tools has increased reliance on data visualizations in various fields. Data visualization is an effective tool for communicating data-driven discoveries, motivating analyses, and detecting faults. This course will teach you how to use data to uncover useful insights and boost your career.
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What will you learn? |
Data visualization principles |
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How to use ggplot2 to create custom plots |
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The flaws of several widely-used plots and why you should avoid them |
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Duration of the course |
8 weeks |
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Course fee structure |
Fee to study and $219 for a verified certificate |
19. Course of Digital Humanities in Practice at Harvard University
Computation is altering the basic structure of how we conduct humanities studies. Data Science tools can enable you to investigate the archive of human culture in ways that were previously impossible. Whether you're a student looking to broaden your knowledge, a librarian encouraging new techniques of research, or a journalist who has just received a big cache of hacked e-mails, this course will teach you how to extract information from thousands of documents at once. You will learn how to effectively use metadata—information about our objects of study— on what matters most, and represent your results so that you are able to understand them at an appearance, using only a few lines of code. In this course, you will work on sections of a search engine that is tailored to the needs of academic research. Along the way, you'll master the principles of text analysis, which is a collection of techniques for manipulating the written word that is central to the digital humanities. By the end of the course, you'll be able to successfully apply what you've learned to whatever interests you the most, whether it's current speeches, journalism, caselaw, or even art items.
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What will you learn? |
Know about metadata and tag text to enhance the results of your analysis. |
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Understand which digital methods are most suitable to meaningfully analyze large databases of text |
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Download datasets and make new ones by scraping websites and using APIs |
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Finding the resources required to complete complex digital projects and learn about the possible limitations |
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Create enhanced datasets by scraping websites, identifying character sets and search criteria, and using APIs |
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Duration of course |
10 weeks |
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Course fee structure |
Fee to study and $219 for a verified certificate. |
20. Course of Introduction to Data Science with Python at Harvard University
Data Science is a constantly developing field that analyzes large amounts of complicated data using scientific approaches and algorithms. To gather and analyze data, data scientists utilize a variety of programming languages, including Python and R. Python's role in data science is highlighted in this course. You'll have a basic knowledge of machine learning models and core ideas related to machine learning (ML) and artificial intelligence (AI) by the end of the course. Students will explore classification models (kNN, Logistic) and regression models (Linear, Multilinear, and Polynomial) using Python and well-known libraries like sklearn, Pandas, matplotlib, and numPy. The course will take students through important machine learning ideas like choosing the proper complexity, avoiding overfitting, regularization, evaluating models, assessing uncertainty, and considering trade-offs. Student's proficiency with Python will increase with this course, putting them in a better position to develop their career and pursue more advanced studies in ML and AI.
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What will you learn? |
You will learn Free Python Courses on programming and coding for modeling, statistics, and storytelling. |
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Acquire hands-on experience with Python to solve real data science challenges. |
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Access to popular libraries such as Pandas, numPy, matplotlib, and SKLearn. |
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Working on machine learning models using Python, evaluating how those models are performing, and applying models to real-world problems. |
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Build a basis for the use of Python in machine learning and artificial intelligence, preparing for future Python study. |
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Duration of course |
8 weeks |
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Course fee structure |
Fee to study and $299 for a verified certificate. |
Conclusion
Data has become the foundation of practically all business domains in today's modern, fast-developing digital economy. Unstructured data is being produced at an exponential rate, making it more important than ever to turn it into insightful information. With these free online courses in data science, you can be part of the rapidly growing change. We have provided all the important information regarding these courses. you can choose any of these as per your interest or requirement.
Who are we?
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Frequently Asked Questions
Anyone who wants to learn data science can do so, regardless of experience level. Professionals in engineering, marketing, software, and IT can enroll in part-time or external data science programs. Basic high school level subjects are the minimal need for conventional Data Science courses. And the best part of the above course is that these are free to study.
The basic skill that you will need for the data science course is that you need to develop a solid foundation in mathematics, statistics, and programming if you want to become a data scientist. Learn how to manipulate, analyze, and visualize data. master the methods and algorithms used in machine learning. Create a portfolio of works that demonstrate your abilities in these skills.
edX, a platform where Harvard provides online courses, does not offer free certificates. Certificates are offered only to students registered in the paid verified track and completed the course. Most of the courses offered by edX are free to access but to attain certification and other benefits of the courses you will be required to pay the fee for it. For details regarding the fee structure of getting certificates, you can check out the website.
If you want to graduate with a respectable degree from an Ivy League university, Harvard's online courses are worthwhile. The educational standards are the same as those of Harvard's traditional cl bassrooms. These courses may be ideal for you if you are an excellent self-learner. Also, you can take the certificate for these courses and add it to your resume.
There is no age, education, or nationality limit for free online courses at Harvard University. Anybody having a good internet connection, access to have laptop or smartphone, and dedicated time of 4 - 5 hours a week to study are eligible for these courses. The courses are designed in a manner that helps candidates to upgrade their skills, and acquire new career opportunities.
