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Starts : 2017-02-27
No votes Free Closed [?] Education

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.

No votes
Udemy $99 Closed [?]

Learn A/B Testing secret short cuts and forget the statistics. In under 35 minutes you can learn how to do it right!

Starts : 2016-09-21
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX UC BerkeleyX

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. 

Starts : 2017-02-21
No votes
edX Free Computer Sciences English Computer Science EdX IITBombayX

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

Starts : 2017-03-21
No votes
edX Free English BabsonX Business & Management Data Analysis & Statistics EdX

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.

Starts : 2016-09-13
No votes
edX Free English Biology & Life Sciences EdX Science Social Sciences UBCx

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

AP® Psychology - Course 5: Health and Behavior 

AP® Psychology - Course 6: Exam Preparation & Review

Starts : 2016-08-15
No votes
edX Free English Computer Science Data Analysis & Statistics EdX UC BerkeleyX

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.

2 votes Free Closed [?] Life Sciences Biology

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…

2 votes Free Closed [?] Life Sciences Biology

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…

Starts : 2006-09-01
16 votes
MIT OpenCourseWare (OCW) Free Engineering Biological Engineering MIT OpenCourseWare Undergraduate

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.

Starts : 2006-09-01
No votes
MIT OpenCourseWare (OCW) Free Closed [?] Biological Engineering Graduate MIT OpenCourseWare

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.

Starts : 2006-09-01
No votes
MIT OpenCourseWare (OCW) Free Biological Engineering MIT OpenCourseWare Undergraduate

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.

Starts : 2016-11-01
No votes
edX Free English Data Analysis & Statistics EdX UTMBx

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.

3 votes Free Closed [?] Business Business Administration

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…

1 votes Free Closed [?] Business Business Administration

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…

Starts : 2015-01-15
No votes
Iversity Free Closed [?] English Economics

####**Course Summary** Over the last 10 to 15 years, the inception of new products and services has steered corporations to manage this development in a much more efficient manner in order to meet customer demand. This introspection within the business community was comprised of two elements: 1.) Can one identify the deficiencies in previous workings in order to overcome the shortcomings? 2.) What is the employee skillset required in order to ensure proper deployment of these new customer needs? The inception of the Business Analysis profession was a result of this changed situation as well as an overall classification of those competencies that were already being practiced in the enterprise. This course takes a look at the competencies required of a Business Analysis. In addition, you will be exposed to the following elements tied to the subject matter: - The typical business analysis path, starting with a problem statement, the requirements tied to the need to be addressed and a definition of the most robust solution possible. - The tie between the enterprise strategy and business analysis activities. - The relationship between needs and current processes - Document analysis and the importance of historical data - The means by which elicitation is performed - Solutions, business cases and decisions - Business Analysis and quality - System thinking and estimations This course is intended to give you a general overview of what this discipline entails and can act as a springboard into more detailed study at a future date. ####**What do I learn?** According to the U.S. Bureau of Labor Statistics, a 19% job growth rate for Business Analysts is expected till 2022, and the outlook for the rest of the world should be higher still. - What are the underlying competencies of a Business Analyst? - The importance of stakeholder management within the business analysis domain. - Where within the enterprise can a business analyst be found? - In which sectors are business analyst competencies being practiced? - What is the importance of processes within the business analysis domain? - Why business transformation starts with an understanding of the current processes deployed within an organization. - What are some of the tools and techniques used to create, evaluate, modify and analyze processes? - What are organizational process assets and enterprise environmental factors? - What tools and techniques are used to create and analyze enterprise artifacts? - What is elicitation, and what techniques does a BA have at her disposal? - The importance of traceability within an organization. - What is the relationship between solutions and risks? - What elements should one consider when evaluating numerous solution possibilities? - What is the cost of quality? - Why is quality management important to a Business Analyst? - What quality management tools and techniques can a Business Analyst use? - What is the Deming Cycle? - How are the 7 Basic Quality Tools deployed? - What means of estimation does a BA have at her disposal? - Business Analysis, data and forecasting. - What is the theory of constraints? - Game theory, crowdsourcing and the Business Analyst. ####**What do I need to know?** No prior knowledge is required. ####**Course Structure** The **first week**, we look at what makes a Business Analyst; what are the underlying competencies, how a BA fits in the organization, the concept of stakeholders and organizational readiness, and lastly in which sectors those BA competencies can be found. In **week #2**, we look at process analysis; the importance of process documentation, the relationship between processes and business transformation, process diagramming and process modeling. **Week 3** takes us to document analysis. We look at Organizational Process Assets and Enterprise Environmental Factors, Impact Analysis, business rules, traceability and other elements that prepare you to move on in your BA path. Our **fourth week** takes us to the world of requirements gathering; what elicitation is, the different ways of performing the exercise and what steps need to be followed within requirements analysis. So that’s week 4, requirements gathering. **Week # 5** concerns all things tied to solutions; the relationship between a problem statement and a solution, the evaluation of multiple solutions, and how we pick the best one. Our **sixth week** views the subject of quality management. What cost does quality have? What are the seven basic quality tools and of course what this all has to do with Business Analysis? Our **last week** takes us into the realm of system thinking and estimation. How important is probability within our BA activities? What is the theory of constraints? We will perform a walkthrough on subject such as crowdsourcing and game theory, and that pretty much covers our last week. ####**Workload** Approximately 4 hours per week for watching lecture videos and completing quizzes and homework assignments.

Starts : 2016-10-23
No votes
CourseSites Free Business

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).

Starts : 2017-11-01
No votes
edX Free English EdX Math MITx

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

  1. Differentials and Antiderivatives
  2. Differential Equations
  3. Separation of Variables

2. Theory of Integration

  1. Mean Value Theorem
  2. Definition of the Integral and the First Fundamental Theorem
  3. Second Fundamental Theorem

3. Applications

  1. Areas and Volumes
  2. Average Value and Probability
  3. Arc Length and Surface Area

4. Techniques of Integration

  1. Numerical Integration
  2. Trigonometric Powers, Trig Substitutions, Completing the Square
  3. 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 1A: Differentiation

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.

99 votes
Khan Academy Free Closed [?] Mathematics Algebra II California Standards Test

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.

Starts : 2016-03-17
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts of RNA-seq and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level: counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level: inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

statistics-r-life-sciences-harvardx-ph525-1x-0" target="_blank">PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

statistics-life-sciences-harvardx-ph525-3x" target="_blank">PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics

This class was supported in part by NIH grant R25GM114818.

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