Opt for Data Science courses for free by Harvard University, 9 free courses list here

Have you ever wondered why Data Science courses are gaining immense recognition in recent times?

Opt for Data Science courses for free by Harvard University, 9 free courses list here

The answer is that in this growing, competitive job world, learning about data science will help young students choose from various job profiles offered by businesses and organisations across the globe. Knowledge in data science helps freshers get a high-remuneration job.

About the course

In this complex professional world, a data science course uses scientific methods, algorithms, and computer programming to extract insights and knowledge from raw data and create actionable information for businesses and other applications, including healthcare, defence and space applications, and government services.

List of free courses by Harvard University

Considering the growth, job possibilities, and interest among students, Harvard University is offering free courses on Data Science. Check the list of 9 free courses here.

1. Case Studies in Functional Genomics: The aim of this course is to teach how to perform the standard processing and normalisation steps, starting with raw data, to get to the point where one can investigate relevant biological questions. The duration of this course is 5 weeks, and per week, a student will need 2-4 hours.

2. Statistics and R: This course introduces basic statistical concepts and R programming skills necessary for analysing data in the life sciences. It lasts 4 weeks and requires 2-4 hours per week.

3. Introduction to Linear Models and Matrix Algebra: In this introductory data analysis course, matrix algebra is used to represent the linear models commonly used to model differences between experimental units. The course lasts four weeks, and each week, 2-4 hours are required.

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4. Statistical Inference and Modeling for High-throughput Experiments: This course involves learning various statistical topics, including multiple testing problems, error rates, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. The students are introduced to statistical modeling and how it is applied to high-throughput data.

5. Advanced Bioconductor: This course involves static and interactive visualisation of genomic data. It provides tools to build interactive graphical interfaces to speed discovery and interpretation. The course duration is five weeks. Each week, students allot 2-4 hours.

6. High-Dimensional Data Analysis: This course will teach students mathematical distance, dimension reduction, singular value decomposition, principal component analysis, multiple-dimensional scaling plots, factor analysis, and dealing with batch effects. It lasts four weeks and requires a time commitment of 2-4 hours per week.

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7. Principles, Statistical, and Computational Tools for Reproducible Data Science: This course teaches students a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools that support data science and reproducible research. It also includes learning statistical methods for reproducible data analysis. The required time is 3-8 hours per week.

8. Data Science: Visualisation: This course covers the basics of data visualisation and exploratory data analysis. Students will learn to communicate data-driven findings, motivate analyses, and detect flaws. This 8-week course can be accessed for free. Each week, enrolled students will have to give 1-2 hours.

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9. Data Science: Probability: This course teaches probability theory. Its motivation is the circumstances surrounding the financial crisis of 2007–2008. Important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the central limit theorem are introduced in this course. This 8-week course requires 1-2 hours per week.

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