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Introduction

In the past decades, a career in statistics was primarily associated with an academic job in the university. However, with several multi-disciplinary advances, data has become a vital component for successful operations. 

In addition, the emergence of several sub-branches such as data science and big data and machine learning plays an integral part in organizations across industries to derive meaningful insights from an enormous amount of data generated every day. From universities, sports professionals, finance, healthcare, engineering, technology, marketing and advertising, and e-commerce, statisticians are highly in demand to provide a competitive advantage. 

With an expected growth rate of 33%, the U.S Bureau of Labor Statistics has highlighted the job trend for statisticians to grow faster than most occupations. Therefore, with increasing opportunities, it is the opportune moment for aspirants to upskill themselves and deep dive into a successful career in some of the most trending industries.

Related reading: Top 13 Online Courses to Learn Data Analysis

1. Become a Probability and Statistics Master – Udemy

Become a Probability and Statistics Master – Udemy

This is one of the bestselling courses on statistics on the Udemy platform. The course allows participants to build comprehensive knowledge with concepts ranging from the fundamentals to the most advanced concepts spanning 163 lessons, including demonstrations, text explanations, quizzes, and assignments.

The key takeaways from the course include:

  • Data visualization using bar graphs, pie charts, histograms, and plots.
  • Analyzing data using mean, median, mode, and IQR.
  • Data distributions and probability including mean, variance, and standard deviation.
  • Bayes theorem, union and intersections, and independent and dependent events.
  • Discrete random variables, Poisson, and geometric random variables.
  • Sampling and types of studies, bias, confidence intervals.
  • Hypothesis testing, statistical inference analysis, significance levels, and test statistics.
  • P-values, regression, scatter plots and correlation coefficients, and chi-square.

The course curriculum includes:

  • Getting started
  • Visualizing data
  • Analyzing data
  • Probability
  • Discrete random variables
  • Sampling
  • Hypothesis testing and regression
  • Final exam and wrap-up

Instructor: Krista King

Level: Beginner

Duration: 14 hours and 21 minutes

User Review: 4.7/5

No. of Reviews: 7747

Price: $47.8

2. Introduction to Statistics by Stanford University – Coursera

Introduction to Statistics by Stanford University – Coursera

This course is available on Coursera. This training program focuses on building solid foundational statistical skills for learners to work with data and communicate insights effectively. This course’s key topics are descriptive statistics, sampling and randomized controlled experiments, probability, sampling distributions, and central limit theorem. 

In addition, some of the essential components such as regression, a common test of significance, and resampling are covered in-depth. At the end of the course, the learners will learn data analysis, differentiate between descriptive statistics and prescriptive statistical concepts, work with data sets, and select proper tests for multiple contexts. 

The concepts covered in the course are sufficient for learners aiming to pursue advanced statistical analysis and machine learning courses. 

The curriculum includes:

  • Introduction and descriptive statistics for exploring data
  • Producing data and sampling
  • Probability
  • Normal approximation and binomial distribution
  • Sampling distributions and central limit theorem
  • Regression, confidence intervals, and tests of significance
  • Resampling and analysis of categorical data
  • One-way analysis of variance (ANOVA) and multiple comparisons

Instructor: Guenther Walther

Level: Beginner

Duration: 15 hours

User Review: 4.6/5

No. of Reviews: 405

Price: Free Enrollment (Additional charges for certification may apply)

3. Statistics for Data Science and Business Analysis – Udemy

Statistics for Data Science and Business Analysis – Udemy

This online program is offered on Udemy. In this course, the learners will learn to understand complicated statistical problems and analysis in an organizational environment. 

The program offers easy-to-understand concepts with plenty of practical assignments, additional resources and introduces all the necessary statistical lingo. Furthermore, the learners will explore the concepts of data visualization and critical statistical concepts required for a data scientist and data analyst to provide insights from large volumes of data. At the end of the course, the learners will be well-equipped to know statistical fundamentals, plotting various data types, correlation and covariance, confidence intervals, and making data-driven decisions.

 In addition, the learners will gain a basic understanding of how to use a programming language like Python and R to perform hypothesis testing, regression analysis, and other statistical measures to work with different types of data distributions and variables. 

The course contents are:

  • Introduction
  • Sample or population data
  • The fundamentals of descriptive statistics
  • Measures of central tendency, asymmetry, and variability
  • Practical examples
  • Distributions
  • Estimators and estimates
  • Hypothesis testing and fundamentals of regression analysis
  • Assumptions for linear regression analysis

Instructor: 365 Careers Team

Level: Beginner

Duration: 4 hours 51 minutes

User Review: 4.6/5

No. of Reviews: 26,029

Price: $47.8

4. Practical Statistics – Udacity

Practical Statistics – Udacity

This online certification program is available on Udacity. The course aims to build foundational skills in statistics to analyze data. Furthermore, the learners will understand the modern use cases and learn about the techniques to tackle real-world challenges. 

In addition, the learners will explore the concepts on A.B. tests and build regression models using programming languages such as Python and SQL. The prerequisites of this program include working knowledge with SQL, Python, and the libraries such as Pandas or NumPy to perform data analysis. 

The course modules are:

  • Simpson’s paradox
  • Binomial distribution
  • Bayes rule
  • Sampling distributions and central limit theorem
  • Hypothesis testing
  • T-tests and A/B tests
  • Logistic regression
  • Course project

Instructor: Josh Bernhard, Sebastian Thrun, Derek Steer, Juno Lee, Mike Yi, David Venturi, and Sam Nelson

Level: Intermediate

Duration: 35 hours

User Review: N.A.

No. of Reviews: NA

Price: $310/month

5. Advanced Statistics for Data Science Specialization by John Hopkins University – Coursera

Advanced Statistics for Data Science Specialization by John Hopkins University – Coursera

This is a specialization course available on Coursera. The certification program offers fundamental concepts in probability and statistics in the initial modules. There is also a mathematical statistics bootcamp that covers the concepts and methods used in biostatistics applications. 

Additionally, the learners will explore the advanced concepts of linear models, modeling tools in data science, least-square and linear regression, hypothesis testing, likelihood concepts, and distribution. Next, the learners will deep dive into the models’ practical implementations to perform multivariate regression using the R programming language. 

The course also builds a strong foundation in linear algebraic requirements for data science and the mathematical perspective of linear statistical models. 

The curriculum includes:

  • Mathematical biostatistics boot camp 1 and 2
  • Advanced linear models for data science
  • Least-squares
  • Statistical linear models

Instructor: Brian Caffo

Level: Advanced

Duration: 5 months

User Review: 4.5/5

No. of Reviews: 111

Price: Free Enrollment (Additional charges for certification may apply)

6. Probability and Statistics for Business and Data Science – Udemy

Probability and Statistics for Business and Data Science – Udemy

This certification course is available on Udemy. The training program offers concepts on probability and statistics from a business and data science perspective. The learners will cover the theoretical aspects and the implementations of statistics in real-world problems. 

Additionally, the learners will explore the basics of data and understand how to analyze data with various statistical measurements such as central tendency, dispersion, and understanding how bivariate data sources are associated with each other. Furthermore, the learners will delve into the concepts of probability, the combinations and permutations, and conditional probability and using the Bayes theorem. 

Besides, the concepts of distributions are covered in-depth. Finally, the learners will cover a few advanced topics such as ANOVA, regression analysis, and chi-square analysis. The course contents are:

  • Measurements of Data
  • Mean, Median, and Mode
  • Variance and Standard Deviation
  • Covariance and Correlation
  • Permutations and Combinations
  • Unions and Intersections
  • Conditional Probability
  • Bayes Theorem
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Sampling
  • Central Limit Theorem
  • Hypothesis Testing
  • T-Distribution Testing
  • Regression Analysis
  • ANOVA
  • Chi-Squared

Instructor: Jose Portilla

Level: Beginner/Intermediate

Duration: 5 hours and 14 minutes

User Review: 4.6/5

No. of Reviews: 4531

Price: $47.8

7. Statistics with R Specialization by Duke University – Coursera

Statistics with R Specialization by Duke University – Coursera

This is among the highly rated online courses on statistics on Coursera. The training program offers critical data analysis and visualization concepts using R and how to create reproducible analytical reports. Additionally, the learners will understand how to perform statistical inference, Bayesian statistical inference, and modeling to make data-based decisions. 

Besides, the learners will understand how to communicate statistical results effectively and evaluate the data-driven decisions, including data wrangling with R packages for performing data analysis. 

The course modules are:

  • Introduction to probability and data with R
  • Inferential statistics
  • Linear regression and modeling
  • Bayesian
  • Statistics with R Capstone

Instructor: Mine Cetinkaya- Rundel

Level: Beginner

Duration: 7 months

User Review: 4.6/5

No. of Reviews: 5132

Price: Free Enrollment (Additional charges for certification may apply)

8. Data Analysis and Fundamental Statistics – Futurelearn

Data Analysis and Fundamental Statistics – Futurelearn

The certification program is offered on the Futurelearn platform. In this program, the learners will begin with statistics and expand their knowledge of data analysis using Excel. 

In addition, some of the essential tools and techniques to perform data analysis using Excel formula to achieve various objectives such as importing, cleaning, analyzing, and data manipulation. Furthermore, the learners will delve into the fundamentals of statistics using machine learning and learn the basic statistical principles. 

The course also offers additional mathematical concepts to build solid statistical skills for data analysis. Upon completing the course, the learners will have solid foundations of data analytics and statistics, the tools and techniques and their implementation, data processing tools for business decisions, and advanced Excel techniques to perform statistical analysis from an extensive database.

Instructor: Jacques Haasbroek

Level: Beginner

Duration: 4 weeks

User Review: N.A.

No. of Reviews: NA

Price: $39/month

9. Micromasters Program in Statistics and Data Science by MIT – edX

The Micromasters program is a specialization on the edX platform. This program comprises four modules and a virtually proctored exam. The learners can expect to master the skills of the methods and tools used in data science and machine learning for performing data analysis.

 Additionally, the learners will explore the fundamentals of probability and statistics and learn to implement and experiment with various data analysis techniques and machine learning algorithms. However, there are some prerequisites for the course that include understanding calculus and mathematical reasoning and a basic understanding of Python programming. 

The takeaways from the course include:

  • Mastering the foundations for data science, statistics, and machine learning
  • Analyzing big data and derive insights for business decisions with probabilistic modeling
  • Statistical inference and identifying methodologies, and deploying appropriate models
  • Develop machine learning algorithms to work with unstructured data and learn popular unsupervised learning methods.
  • Basics of deep neural networks, clustered methodologies, and supervised methods.

The course contents are:

  • Probability: The science of uncertainty and data
  • Fundamentals of statistics
  • Machine learning with Python: From linear models to Deep learning
  • Capstone

Electives:

  • Data analysis in social science: Assessing your knowledge
  • Data analysis: Statistical modeling and computation in applications

Instructor: Regina Barzilay, Eren Can Kizildag, Sara Fisher Ellison, Jan-Christian Hutter, Patrick Jaillet, Jagdish Ramakrishnan, Katie Szeto, Kuang Xu, Dimitri Bertsekas, Esther Duflo, Qing He, Tommi Jaakkola, Jimmy Li, Philippe Rigollet and John Tsitsiklis

Level: Intermediate/Advanced

Duration: 1 year 2 months

User Review: N.A.

No. of Reviews: N.A.

Price: $1528

10. Statistics with Python Specialization by University of Michigan – Coursera

Statistics with Python Specialization by University of Michigan – Coursera

This course is available on Coursera. The specialization is designed to teach learners various concepts of statistical analysis using the Python programming language. The learners will understand the data sources, types of data, and the process of collecting, studying, and managing data. 

Furthermore, the learners will understand the techniques of data exploration and visualization. In addition, the learners will deep dive into the data assessment theories, construct confidence interval concepts, and interpret inferential results. Besides, advanced statistical modeling procedures are covered in-depth with practical hands-on sessions to master the skills.

 Finally, the learners will understand research questions and connect them to the statistical and data analysis methods to deal with complex problems in a real-world scenario. The course modules are:

Understanding and visualizing data with Python

This module introduces the field of statistics, data sources, study design, and data management aspects of a data science problem.

In addition, the learners will explore the data and communicate the findings with data visualization techniques. The learners will have a solid understanding of various data types and interpret univariate and multivariate data summaries.

 The module also covers important concepts on probability and non-probability sampling of large populations and learns how each sample varies and how inference can be applied based on probability sampling.

Finally, the learners will apply the statistical concepts using Python during lab sessions and implement various libraries to perform data analysis.

Inferential statistical analysis with Python

The second module focuses on the basic principles behind data for estimation and assessment. First, the learners will analyze categorical and quantitative data, population techniques, and expand to handle two populations. Next, the learners will understand how to use confidence intervals and work with sample data to assess whether specific parameters are consistent within the data set. 

Finally, the learners will learn about interpreting inferential results and work on numerous case studies to solidify their skills by implementing statistical concepts using Python.

Fitting statistical models to data with Python

The course’s final module explores the advanced concepts on statistical inference techniques and learns to fit statistical models to data correctly. In addition, the learners will work on various models and understand the relationship between variables for predictions. 

This module also introduces and explores several statistical modeling techniques such as linear regression, logistic regression, generalized linear models, Bayesian techniques, and hierarchical and mixed-effects models. All of the concepts covered in the module are demonstrated with the help of practical examples using real data sets. Besides, the learners will understand the types of modeling approaches available for different data types based on the underlying study design. 

Finally, the learners will explore data visualization using Python and various libraries such as Statsmodels, Pandas, and Seaborn for advanced statistical analysis.

Instructor: Brenda Gunderson, Kerby Shedden, and Brady T. West

Level: Beginner

Duration: 3 months

User Review: 4.6/5

No. of Reviews: 2220

Price: Free Enrollment (Additional charges for certification may apply)

11. Data Science: Statistics and Machine Learning Specialization by Johns Hopkins University – Coursera

Data Science- Statistics and Machine Learning Specialization by Johns Hopkins University – Coursera

This online course is offered on Coursera. The training program is ideal for learners with a basic understanding of statistics and machine learning. It covers concepts on statistical inference, regression models, and machine learning algorithms for data analysis.

The learners will also understand how to develop data products using various tools and techniques and work with real-world data. 

In addition, the learners will also have a thorough understanding of building and applying prediction functions and applying advanced statistics to conclude populations and scientific insights from the data. The course contents are:

Statistical inference

In this module, the learners will understand how to draw conclusions about populations and perform various inferences, including statistical modeling and data-related strategies. Moreover, the learners will understand the uses of designs and randomization in data analysis and the broad theories of frequentists, Bayesian, and Likelihood. In addition, the learners will explore various complexities faced with missing data, observed and unobserved confounding, and bias while handling data.

Regression models

The second module explores the linear models for assumptions, regression models, and a subset of linear models. Furthermore, the learners will explore the statistical analysis tools for modern data scientists and cover many concepts, including regression analysis, least-square, and inference using statistical models.

Besides, the learners will delve into advanced concepts and learn to use ANOVA test and analysis of residuals and variability and building scatterplots for presenting analytical reports.

Practical machine learning

This module covers the essential components of building a prediction function on practical applications. The learners will explore the concepts of training and test data sets, overfitting, and error rates associated with computational models. In addition, the basics of the range of a model and essential machine learning algorithms such as regression. Classification trees, Naïve Bayes, and random forests are covered in-depth with hands-on lab sessions.

Data science capstone

The final module is mandatory for learners as the capstone requires creating usable and public data products from real-world problems in collaboration with industry, government, or academic partners. The learners are required to clear the capstone to attain the certificate of completion.

Instructor: Brian Caffo, Jeff Leek, and Roger D. Peng

Level: Intermediate

Duration: 6 months

User Review: 4.6/5

No. of Reviews: 484

Price: Free Enrollment (Additional charges for certification may apply)

12. Statistics Essentials for Analytics – Edureka

This is a self-paced course offered by Edureka. In this course, the learners will cover the fundamental concepts of statistics from a data science perspective. All the concepts are designed in detail that is in sync with the statistical and probability requirements to deal with data-related problems in data science. 

The learners will learn about data and its types and various statistical sampling procedures to analyze data efficiently. In addition, the learners will cover advanced concepts on statistical inference, testing and clustering, and various machine learning algorithms that have been found effective in handling data-related complexities. 

The key takeaways from the course include:

  • Thorough understanding of data and data types
  • Mastering sampling techniques
  • Descriptive statistics
  • Applying a probabilistic approach to solve complex problems
  • A/B Testing and parametric and non-parametric testing
  • Experimental designing
  • Parametric test types
  • Bayesian inference
  • Understanding clustering techniques
  • Regression modeling
  • Hypothesis testing
  • Machine learning fundamentals

Instructor: Industry Professionals

Level: Intermediate

Duration: Self-Paced

User Review: 5/5

No. of Reviews: 6000

Price: $81.6

Conclusion

In the current scenario of continuous innovations, the competition in the industries is sky-high. On the other hand, millions of data are being generated every day from various sources.

Although several new technologies have aided in faster data analysis, statistics experts remain an asset for various organizations. In the new era of data-driven decisions, deriving meaningful insights from them to generate business decisions to achieve business goals has become vital in all industries. 

From weather forecasting, finances, insurance, sports, supply chain, manufacturing, government agencies, life sciences to healthcare, data has gained prime importance, and appropriate analysis of data can provide significant knowledge about evaluating performance and building future strategies to tackle challenges or to gain competitive advantage in the market.

In addition, multi-disciplinary fields such as data science and big data have generated a high demand for statistics professionals. 

To build a career as a successful statistics professional in multi-disciplinary industries requires upskilling with the appropriate theoretical and practical skills. Thus, it becomes essential to opt for online courses that provide exposure to all the necessary skills and provide industry-recognized certificates to showcase the individuals’ skills. Therefore, the article highlighted some of the top trending courses on statistics on online platforms.

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Kaushik Das

Kaushik is an artificial intelligence researcher and a data scientist with expertise in medical image processing, intelligent automation, computer vision, and big data engineering—a technical and a scientific writer by passion. He is also passionate about sports, photography, traveling, and exploring new technologies.

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