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Top 10 Online Courses on Statistics Available Online - Open Education Online

Top 10 Online Courses on Statistics Available Online

Data is increasingly becoming an important resource in any organization. As a statistician, equipping yourself with modern ways of making that data invaluable will enhance your credibility in the field. I have listed below 10 online courses on statistics from Coursera, edx, and Futurelearn. You will access all course materials and a shareable certificate for a fee. You can also audit the courses for free but with limited study materials and no certificate. Some courses offer financial aid.

1. Statistics and R (edx)

Analyzing data in the life sciences requires relevant statistical software such as R. This course aims to see you understand R programming language in the context of statistical data and statistical analysis in the life sciences.

What you will learn

  • Exploring new data sets using R visualization techniques
  • Using R scripts to analyze data and the basics of conducting reproducible research
  • Robust statistical methods for data that don’t fit standard assumptions

Visit here to enroll.

2. Introduction to Probability (edx, Coursera)

Statistics require probability to bring logic to a world full of randomness and uncertainty. This course gives you a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects that require probability.

Excerpt

  • Uncertainty and randomness
  • Making good predictions
  • Common probability distributions in statistics
  • finding the expected value of a random quantity
  • Using conditional probability to approach complicated problems

Offered by Harvard University and instructed by Joseph Blitzstein, its verified track goes for $ 139.

3. Bayesian Statistics: Techniques and Models (Coursera)

Real-world data often demand more-sophisticated models to reach realistic conclusions. This course builds on the fundamentals of Bayesian statistics that touch on data presentation, probability, and statistical estimation. Offered by a doctoral student in statistics, it will expand your knowledge of the “Bayesian toolbox.”

What you will learn

  • Markov Chain Monte Carlo Methods (MCMC)
  • Construct, fit, assess, and compare Bayesian statistical models for continuous, binary, and count data

It ensures active learning by combining lecture videos, computer demonstrations, readings, exercises, and discussion boards. Enroll here 

4. Statistics for International Business (Coursera)

It introduces you to areas of statistics useful in business and several MBA modules. Offered by the University of London and endorsed by CMI, this course is taught by George Kapetanois, a professor of Finance and Econometrics.

Excerpt

  • Using graphs to describe data
  • Using measures to describe data
  • Probability and probability distributions
  • Statistical estimation

You can request financial aid if you cannot afford the fee. Enroll here.

5. Statistics and Data Analysis in Excel (Futurelearn)

This 5-week course teaches you the essential concepts of statistics and basic probability using Excel. Your instructors are specialists from Cloudswyft, a leading learning as-a-service platform provider. For $ 39/month, you get full Expert Track access.

What’s in the Syllabus?

Using excel’s calculation and visualization environment, you will learn how to;

  • Describe basic probability and how to apply them
  • Unpack random variables, confidence intervals, and more
  • Apply concepts i.e. hypothesis testing

Enroll here and earn yourself a digital course certificate in the field.

6. Data Visualization: Data Dashboards and Storytelling with Tableau (FutureLearn)

Organizations today require data visualization to make business predictions. Ethical issues can arise from the process. This course teaches the best predictive analysis methods and the associated ethical issues. It’s a 4-week course that uses Tableau, Public, and Excel software tools to teach core mechanics of predictive models.

Excerpt

  • Introduction to predictive modeling
  • Data models
  • Data ethics
  • Working as a data analyst

Your instructor is Alastair Gill. Enroll here for $39/month.

7. Data Analysis and Python Fundamentals (FutureLearn)

In 4 weeks, this course will see you apply python programming language to analyze and model data. Python functions make data valuable by supporting data wrangling and ingestion; and data mining. It’s taught by Ed Marks and developed by futurelearn, GitHub, and Coventry University.

Excerpt

You will learn how to;

  • Define loops, conditional logic, various data structures, and collections in python
  • Discuss fundamentals of statistics and its application in data analytics
  • Create and use functions in python

Enroll here for $39/Month.

8. Statistics with SAS (Coursera)

Instructor Jordan Bakerman (Analytical Training Consultant) will teach you how to use SAS/STAT software to perform statistical analyses. It’s offered by SAS, a company that promotes data transformation into intelligence.

Excerpt

  • ANOVA and Regression
  • Complex linear models
  • Model post-fitting for inference
  • Model building for scoring and prediction

Click here to enroll and learn more about its financial aid.

9. Introduction to Statistics (Coursera)

Offered by Stanford University and instructed by Guenther Walther (professor of statistics), it prepares you to pursue more advanced topics in statistical thinking and machine learning. It gives you statistical thinking concepts essential for learning from data and communicating insights.

Excerpt

  • Introduction and Descriptive statistics for exploring data
  • Producing data and sampling
  • Normal Approximations and Binomial distribution
  • Regression
  • Confidence Intervals

Enroll here and take approximately 15 hours to complete the course.

10. High-Dimensional Data (edx)

Instructed by Rafael Irizarry and Michael Love, this course focuses on several techniques widely used in the analysis of high-dimensional data. In an estimated 4-week period, you will be equipped with methods for analyzing and interpreting high-data.

What you’ll learn

  • Mathematical distance
  • Single Value Decomposition and Principal Component Analysis
  • Batch effects
  • Basic Machine Learning

Enroll here.

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