Online courses directory (209)
This short course will provide an introductory, hands-on introduction to statistics used in educational research and evaluation. Participants will learn statistical concepts, principles, and procedures by building Excel spreadsheets from scratch in a guided learning approach using very short video-based tutorials.
Learn A/B Testing secret short cuts and forget the statistics. In under 35 minutes you can learn how to do it right!
Gain a deeper understanding of Spark by learning about its APIs, architecture, and common use cases. This statistics and data analysis course will cover material relevant to both data engineers and data scientists. You’ll learn how Spark efficiently transfers data across the network via its shuffle, details of memory management, optimizations to reduce compute costs, and more. Learners will see several use cases for Spark and will work to solve a variety of real-world problems using public datasets. After taking this course, you should have a thorough understanding of how Spark works and how you can best utilize its APIs to write efficient, scalable code. You’ll also learn about a wide variety of Spark’s APIs, including the APIs in Spark Streaming.
Algorithms power the biggest web companies and the most promising startups. Interviews at tech companies start with questions that probe for good algorithm thinking.
In this computer science course, you will learn how to think about algorithms and create them using sorting techniques such as quick sort and merge sort, and searching algorithms, median finding, and order statistics.
The course progresses with Numerical, String, and Geometric algorithms like Polynomial Multiplication, Matrix Operations, GCD, Pattern Matching, Subsequences, Sweep, and Convex Hull. It concludes with graph algorithms like shortest path and spanning tree.
Topics covered:
- Sorting and Searching
- Numerical Algorithms
- String Algorithms
- Geometric Algorithms
- Graph Algorithms
This course is part of the Fundamentals of Computer Science XSeries Program:
Want to know how to avoid bad decisions with data?
Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.
These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.
We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?
This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.
Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.
This course is part of the Business Principles and Entrepreneurial Thought XSeries.
Animal breeding involves the selective breeding of domestic animals with the intention to improve desirable (and heritable) qualities in the next generation. This course introduces the steps required to design a program for breeding animals and teaches the genetic and statistical concepts that are needed to build a solid breeding program.
In this course, you will learn how an animal breeder balances the need for improving the desirable qualities of the animals with the need for genetic diversity and long-term sustainability of the breeding program. You will learn about the scientific concepts in genetics that are applied in animal breeding, as well as how to apply the models and computational methods that are used in animal breeding.
Professionals working with animals will be able to use the knowledge from this course to understand the impact of breeding on animal populations and use genetic principles to make their decisions. This course will allow you an advanced starting point for further studies, such as M.Sc. level courses in breeding.
Knowledge of statistics at a 2nd or 3rd year university level is needed to follow this course successfully.
The course is developed with financial support and input from the Koepon Foundation (http://www.koeponstichting.nl/) and the African Chicken Genetic Gains project (https://africacgg.net/.)
This psychology course is an introduction to the field of psychology. It begins by asking “What is Psychology?” and provides some concrete answers to that question. Next, it covers the history of psychology and provides a look at the state of psychology today.
This course will provide you with research-based study tips — to help you in this course and in the future. You will learn the methods a psychologist uses in their research. From experimental design to coverage of some basic statistics — by the end of this course you will have a comprehensive appreciation for the methods of psychology.
This course includes video-based lectures and demonstrations, interviews with real research psychologists and a plethora of practice questions to help prepare you for the AP® Psychology exam.
This is the first in our six-course AP® Psychology sequence designed to prepare you for the AP® Psychology exam.
Additional Courses:
AP® Psychology - Course 2: How the Brain Works
AP® Psychology - Course 3: How the Mind Works
AP® Psychology - Course 4: How Behavior Works
Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term ‘data science’.
This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.
In this course, you will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge. You will learn about how statistics and probability work together. The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data. Statistics formalizes the process of making decisions, and this course is designed to help you use statistical literacy to make better decisions. Note that this course has applications for the natural sciences, economics, computer science, finance, psychology, sociology, criminology, and many other fields. We read data in articles and reports every day. After finishing this course, you should be comfortable evaluating an author's use of data. You will be able to extract information from articles and display that information effectively. You will also be able to understand the basics of how to draw statistical conclusions. This course will begin with descriptive statistic…
The advent of computers transformed science. Large, complicated datasets that once took researchers years to manually analyze could suddenly be analyzed within a week using computer software. Nowadays, scientists can use computers to produce several hypotheses as to how a particular phenomenon works, create computer models using the parameters of each hypothesis, input data, and see which hypothetical model produces an output that most closely mirrors reality. Computational biology refers to the use of computers to automate data analysis or model hypotheses in the field of biology. With computational biology, researchers apply mathematics to biological phenomena, use computer programming and algorithms to artificially create or model the phenomena, and draw from statistics in order to interpret the findings. In this course, you will learn the basic principles and procedures of computational biology. You will also learn various ways in which you can apply computational biology to molecular and cell…
This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors.
This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors.
This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors.
This course provides a broad foundation of statistical terms and concepts as well as an introduction to the R statistical software package. The topics covered are fundamental components of biostatistical methods used in both omics and population health research.
Working with biomedical big data presents many challenges; familiarity with common statistical terms and definitions, and understanding basic statistical theory will help you overcome those challenges.
Topic-specific information and examples will be followed by self-assessment opportunities for you to gauge your understanding. In addition, practice datasets and exercises will be provided for you to improve your R programming skills.
This course will introduce you to business statistics, or the application of statistics in the workplace. Statistics is a course in the methods for gathering, analyzing, and interpreting data. If you have taken a statistics course in the past, you may find some of the topics in this course familiar. You can apply statistics to any number of fields from anthropology to hedge fund management because many of us best interpret data when it is presented in an organized fashion (as it is with statistics). You can analyze data in any number of forms. Summary statistics, for example, provide an overview of a data set, such as the average score on an exam. However, the average does not always tell the entire story; for example, if the average score is 80, it may be because half of the students received 100s and the other half received 60s. This would present a much different story than if everyone in the class had received an 80, which demonstrates consistency. Statistics provides more than simple averages. In t…
This course will introduce you to entrepreneurship and business planning. By way of introduction, the word entrepreneur originates from the French word entreprendre, meaning to undertake. Today, we define an entrepreneur as an owner or manager of a business enterprise who attempts to make profits by starting and growing his or her business. In earnest, entrepreneurs are a diverse group of risk-takers who share the same goal of cultivating ideas and developing them into viable business opportunities. Take a quick look at the statistics below to get a sense for some of the (potentially surprising) qualities that have been attributed to entrepreneurs. According to a recent report by the US Census, every day approximately 2,356 Americans are becoming entrepreneurs by starting new businesses. According to 2006 report from Northeastern University’s School of Technological Entrepreneurship, 62% of entrepreneurs in the US claim innate drive as the number one motivator in starting their business. According t…
This course offers a broad foundation in quantitative methods. Anyone interested in business, from seasoned managers to aspiring entrepreneurs, can sharpen their quantitative skills (course can be used as a waiver in Fox Online MBA).
How long should the handle of your spoon be so that your fingers do not burn while mixing chocolate fondue? Can you find a shape that has finite volume, but infinite surface area? How does the weight of the rider change the trajectory of a zip line ride? These and many other questions can be answered by harnessing the power of the integral.
But what is an integral? You will learn to interpret it geometrically as an area under a graph, and discover its connection to the derivative. You will encounter functions that you cannot integrate without a computer and develop a big bag of tricks to attack the functions that you can integrate by hand. The integral is vital in engineering design, scientific analysis, probability and statistics. You will use integrals to find centers of mass, the stress on a beam during construction, the power exerted by a motor, and the distance traveled by a rocket.
1. Modeling the Integral
- Differentials and Antiderivatives
- Differential Equations
- Separation of Variables
2. Theory of Integration
- Mean Value Theorem
- Definition of the Integral and the First Fundamental Theorem
- Second Fundamental Theorem
3. Applications
- Areas and Volumes
- Average Value and Probability
- Arc Length and Surface Area
4. Techniques of Integration
- Numerical Integration
- Trigonometric Powers, Trig Substitutions, Completing the Square
- Partial Fractions, Integration by Parts
This course, in combination with Part 1, covers the AP* Calculus AB curriculum.
This course, in combination with Parts 1 and 3, covers the AP* Calculus BC curriculum.
This course was funded in part by the Wertheimer Fund.
Learn more about our High School and AP* Exam Preparation Courses
Calculus 1C: Coordinate Systems & Infinite Series
*Advanced Placement and AP are registered trademarks of the College Board, which was not involved in the production of, and does not endorse, these offerings.
California Standards Test: Algebra II. California Standards Test: Algebra II (Graphing Inequalities). CA Standards: Algebra II (Algebraic Division/Multiplication). CA Standards: Algebra II. Algebra II: Simplifying Polynomials. Algebra II: Imaginary and Complex Numbers. Algebra II: Complex numbers and conjugates. Algebra II: Quadratics and Shifts. Examples: Graphing and interpreting quadratics. Hyperbola and parabola examples. Algebra II: Circles and Logarithms. Algebra II: Logarithms Exponential Growth. Algebra II: Logarithms and more. Algebra II: Functions, Combinatorics. Algebra II: binomial Expansion and Combinatorics. Algebra II: Binomial Expansions, Geometric Series Sum. Algebra II: Functions and Probability. Algebra II: Probability and Statistics. Algebra II: Mean and Standard Deviation.
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