This article aims to provide a comprehensive overview of the best Stata courses available online. Stata is a statistical software package used for data analysis, data management, and graphics. It is extensively used in academic research, government agencies, and industries worldwide. With the increasing demand for Stata skills, many online courses have emerged to cater to the needs of learners across the globe. This article will review and compare various online courses that offer Stata training to help learners make an informed decision when selecting the best option suited for their needs. The article will evaluate the courses based on various criteria such as course content, instructor quality, price, and learner reviews.
Here’s a look at the Best Stata Courses and Certifications Online and what they have to offer for you!
Stata Online Course Certificate
- Stata Online Course Certificate
- 1. Complete STATA Workflow + Tips by Mauricio Maroto (Udemy) (Our Best Pick)
- 2. The STATA OMNIBUS: Regression and Modelling with STATA by F. Buscha (Udemy)
- 3. The Essential Guide to Stata by F. Buscha (Udemy)
- 4. Modeling Count Data using Stata by Najib Mozahem (Udemy)
- 5. Machine Learning and Data Science in STATA by Ed Spot (Udemy)
- 6. Data Management and Analysis with Stata. by Ihsan Ullah (Udemy)
- 7. 120 Quick Stata Tips by F. Buscha (Udemy)
- 8. Stata Level 1 Fundamentals of Data Analysis by Juan Sebastian Cuervo Sánchez (Udemy)
- 9. Data Visualization in Stata by F. Buscha (Udemy)
- 10. Statistics Explained Easy 5 – STATA by Antonie van Voorden (Udemy)
1. Complete STATA Workflow + Tips by Mauricio Maroto (Udemy) (Our Best Pick)
The Complete STATA Workflow + Tips course is a comprehensive program that aims to help students master STATA for data management, graphs, and data analysis. The course boasts over 3,800 students enrolled and over 900 ratings averaging 4.3. Some reviews from past students praise the course’s ability to introduce Stata and provide useful examples. The course provides over 100 detailed lectures and 9.5 hours of video, coupled with tips and hints to enable students to handle STATA with ease.
The course is targeted at both undergraduate and graduate students and professionals with some STATA knowledge. It aims to equip students with the best workflow, reducing the time taken to learn STATA and improving the time taken in getting results. The course covers topics such as input, output, and related file commands, dataset management, variables management, graphs, descriptive statistics, and Stata functions.
The instructor, Mauricio Maroto, promises to help students become STATA experts. To achieve this, the course provides resources such as lectures, exercises, and messages, and students have lifetime access to the course once enrolled. Overall, the course provides a comprehensive program to help students master STATA for data management, graphs, and data analysis.
The STATA OMNIBUS: Regression and Modelling with STATA is a comprehensive course package that covers linear and non-linear regression, regression modelling, and STATA. The course focuses on providing an easy-to-understand introduction to statistical methodology, without complicated equations or mathematics. The course is designed for anyone who needs to engage with quantitative analysis, and no prior knowledge is necessary. The learning outcomes include understanding the basic statistical intuition behind regression, interpreting and analyzing complicated regression output, and learning tips and tricks for linear and non-linear regression analysis.
The course covers various topics, including correlation versus causation, parametric and non-parametric lines of best fit, the least squares method, R-squared, beta’s, standard errors, t-statistics, p-values, and confidence intervals. Additionally, it covers logistic and probit regression, latent variables, marginal effects, dummy variables, goodness-of-fit statistics, and practical model building. Practical examples are demonstrated using the computer software Stata.
Regression modelling is another crucial aspect of the course. The course examines common modelling issues, including fundamental regression modelling philosophy, modelling non-linear relationships in a linear regression, using and interpreting interaction effects, exploring dynamic relationships with time information, coding and interpreting categorical explanatory variables, dealing with multicollinearity, and handling missing data.
The course also includes an introduction to Stata and its various uses in modern data analysis. The focus is on creating a good practice and emphasizing the practical application and interpretation of commonly used statistical techniques without resorting to deep statistical theory or equations. Topics covered include viewing and exploring data, manipulating data, visualizing data, correlation, ANOVA, regression, regression model building, hypothesis testing, binary outcome models, fractional response models, categorical choice models, simulation techniques, count data models, survival data analysis, panel data analysis, difference-in-differences analysis, instrumental variable regression, epidemiological tables, power analysis, and matrix operations.
The Essential Guide to Stata is a course that teaches the application of statistical techniques using the Stata software. The course covers topics such as data manipulation, visualization, regression, hypothesis testing, survival analysis, panel data analysis, instrumental variable regression, and more. The course does not require prior engagement with Stata but some basic quantitative/statistical knowledge would be helpful. The course is designed for anyone interested in data analytics using Stata. The course focuses on the proper application of code and interpretation of results, without delving into deep statistical theory or equations. Each session emphasizes practical application.
The course is divided into sections such as Introduction, Getting Started, Exploring Data, Manipulating Data, Visualising Data, Testing Means, Correlations and ANOVA, Linear Regression, Categorical Choice Models, Fractional/Proportional Variable Models, Random Numbers and Simulation, Count Data Models, Survival Analysis, Panel Data Analysis, Difference-in-Differences Analysis, Instrumental Variable Regression, Epidemiological Tables, Power Analysis, Basic Matrix Operations, and The Big Stata Practice Test. The course is continuously updated and monthly promo codes are available on the course instructor’s Twitter feed (@easystats3).
The course aims to provide a comprehensive introduction to Stata and its various uses in modern data analysis. By the end of the course, students will feel confident in their ability to engage with Stata and handle complex data analytics. The course provides an overview of data analytics using Stata and requires some basic quantitative/statistical knowledge. The course covers various statistical techniques without resorting to deep statistical theory or equations. The sections of the course focus on practical application, emphasizing the proper application of code and interpretation of results.
The course titled Modeling Count Data using Stata is instructed by Najib Mozahem and focuses on Poisson and Negative Binomial Regression techniques. The course is available in two parts and includes an e-book and a set of slides.
In the first part of the course, students are introduced to the theory behind count models in an intuitive manner with minimum math. The course starts with count tables and calculating the incidence-rate ratio. It then moves to Poisson regression, which covers the inclusion of continuous, binary, and categorical variables. The course also discusses overdispersion and its solutions using negative binomial models. Truncated models and zero-inflated models are also explained.
In the second part of the course, students learn how to apply the theory using Stata. They walk through a large project that involves fitting Poisson, negative binomial, and zero-inflated models. The course also introduces tools for comparing these models.
The course is divided into several sections, including count tables, Poisson regression, other count models, prediction, fitting the model, model comparison, and prediction.
Overall, the course provides a comprehensive understanding of count models and their applications in Stata.
The Machine Learning and Data Science in STATA course is designed to provide learners with a practical way to learn Machine Learning and Data Science using STATA. The course is suitable for individuals with no prior programming knowledge, as the course will start from scratch and teach everything from the ground up. The course aims to provide the most up-to-date Machine Learning methodologies using STATA, and will teach learners how to think about data science and Machine Learning in a new way. The course promises to be an excellent approach to starting a career in Machine Learning, as it provides fundamental principles and practical experience.
The course is unique and the only one that uses STATA to apply Machine Learning Models in Credit Risk Scenarios. The course creators chose STATA as the platform because many learners are already familiar with it or want to be familiar with it. The course will start from scratch and provide step-by-step guidance on building new abilities. Learners will work together to create a complete data science project using Credit Risk Data from start to finish. There is plenty of data available, including information on around 40,000 consumers, their level of education, age, marital status, where they live, if they own a home, and other pertinent information.
The course will provide learners with hands-on experience in digging deep into the data and practicing on their own. Additionally, learners will have access to essential resources such as lectures, homework, quizzes, slides, and a literature analysis on modeling methodologies. The course is structured into several sections, including Background Knowledge, STATA and DATA, Data Visualization, Data Inspection and Summarization, Choosing the Variables, Coding of Continues Variables (Fine-Classing), and Machine Learning. Each section will cover the necessary knowledge and skills needed to complete the data science project using Credit Risk Data.
In conclusion, the Machine Learning and Data Science in STATA course is an excellent opportunity for individuals who want to learn Machine Learning and Data Science using STATA.
The Data Management and Analysis with Stata course is a comprehensive introduction to Stata and Statistics. The course is divided into seven sections that cover variables used in Statistics, which include nominal, ordinal, interval, and ratio variables. There are two ways to undertake the course. The first is to start from section 3 if the student has a basic understanding of Stata. The second is to follow the exact order of the course from section 1 to section 7.
If the first alternative is chosen, the student should proceed to section 4 on Descriptive Statistics, which is common to all types of research. Section 5 analyses a relationship between Nominal/Ordinal variables, while section 6 investigates a relationship between Nominal/Ordinal and Interval/Ratio variables. Section 7 finds an effect of one Interval/Ratio variable on another. The advantage of this strategy is that the more important content is studied first, but the student may need to go back to section 2 if they have trouble understanding a particular Stata command.
The second alternative is to follow the exact order of the course, starting from section 1 and proceeding to the next section until section 7. The first 3 sections are meant to prepare the student for the next 4 sections. Therefore, quitting in the first half of the course will deprive the student of the intended benefits. The course also provides accurate captions and resources, including separate data sets for each section, five do-files, and exercises at the end of each section.
The course includes seven sections: Knowing Stata, The Fundamental Commands, Data Management and Do-File, Descriptive Statistics, Analyzing a Relationship between Nominal/Ordinal Variables, Analyzing a Relationship between Nominal/Ordinal and Interval/Ratio Variables, and Regression Analysis (Analyzing a Relationship between Interval/Ratio Variables). There are also bonus videos. The only prerequisites for the course are to install Stata on the computer and remain committed.
The course titled 120 Quick Stata Tips is designed for those who want to become proficient in Stata quickly. The course instructor, F. Buscha, provides 120 concise, professional-grade tips for data management, graphing, statistics, and programming. Each video is standalone and takes no more than 2 minutes. The course covers a wide range of topics, including creating code books, generating publication-quality tables, and graphing the variance-covariance matrix.
The course assumes that the student has basic knowledge of Stata and do-files. However, the instructor suggests checking out the Essential Guide to Stata course if the student does not have this knowledge. The course includes four sections: Data Management, Statistics, Programming, and Graphing. Each section covers a set of tips relevant to the respective topic.
The Data Management section covers topics such as searching in variables, verifying data, dropping duplicate observations, and drawing samples. The Statistics section covers topics such as computing elasticities, reducing collinearity in polynomial variables, and identifying outliers from a regression. The Programming section covers topics such as pausing Stata, adding custom ado folders, and creating custom user profiles. Finally, the Graphing section covers topics such as recovering data from a graph, displaying RGB colors in graphs, and drawing histograms with custom bins.
The course includes a cheat sheet that summarizes all the tips covered in the course. Additionally, the instructor provides monthly promo codes and other updates on their Twitter feed (@easystats3). The course is designed to help students learn years’ worth of hard Stata knowledge in just three hours.
The Stata Level 1 Fundamentals of Data Analysis course is designed for complete beginners who want to learn Stata for data analysis. The course is taught by Juan Sebastian Cuervo Sánchez and covers all the basics needed for quantitative data analysis using Stata.
The course is goal-oriented, with each lesson focused on solving a common problem or challenge in quantitative data analysis. Real-world exercises are used to reinforce the lessons, and the course content is organized into eight sections.
Section 1 provides an introduction to the course, while Section 2 covers preparing for work in Stata. Section 3 focuses on loading new data into Stata, while Section 4 covers keeping track of work and calculations using Do and Log files.
Section 5 teaches students to manage and manipulate datasets, while Section 6 covers solving data analysis questions with Stata. Creating and joining databases is the focus of Section 7, and the course concludes with a final project in Section 8.
Note that the course does not cover statistical methods such as regression analysis, logistic regression, or ANOVA.
The Data Visualization in Stata course is designed to teach learners about advanced graphing techniques and how to generate them using Stata. The course is modular, allowing learners to focus on the graphs they are most interested in without having to follow the course linearly. Visualizing and graphing data is essential in modern data analytics, and this course aims to provide learners with a solid understanding of different visualization methods, their advantages and disadvantages, and how to create and modify them in Stata.
The course is suitable for data scientists, students of quantitative methods, and business users who need to get data information across to other stakeholders. The course covers various data visualization techniques, including histograms, density plots, spike plots, rootograms, box plots, violin plots, stem-and-leaf plots, quantile plots, bar graphs, pie charts, dot charts, radar plots, scatter plots, heat plots, hex plots, sunflower plots, lines of best fit, area plots, line plots, range plots, rainbow plots, jitter plots, table plots, balloon plots, mosaic plots, chernoff faces, sparkling plots, bubble plots, and more.
Learners should have some understanding of how Stata works and what .do files are. The course focuses on creating different types of graphs, their possible options and sub-options. The main learning outcomes of the course are to learn and understand the basic methods of data visualization, learn variations and customizations of basic visualization methods, gain experience of different data visualization techniques and how to apply them, learn and code many Stata graphs, and gain confidence in modifying and creating bespoke data visualizations in Stata.
The course instructor, F. Buscha, has outlined some of the most important data visualization methods in an easy-to-understand manner without any equations or complex statistics. Learners will learn how to create, modify, and customize each graph in Stata.
The course Statistics Explained Easy 5 – STATA is focused on teaching individuals how to navigate through Stata, emphasizing on the essential features and tips to remember. The course is designed by Antonie van Voorden and is 40 minutes long. The course content is divided into five sections, which include: how to use Stata, how to use the menu’s, common pitfalls to avoid, easy to remember ways of doing everything and how to learn to use Stata more efficiently.
The course is structured in such a way that it highlights the essential features of Stata, making it easier for individuals to find the commands they need to use. The course also covers the menu’s and how to navigate through them. Additionally, the course covers the common pitfalls that individuals may encounter while using Stata and how to avoid them.
The course emphasizes on easy to remember ways of doing everything in Stata. The aim is to help individuals retain the information taught in the course easily. The course also encourages individuals to learn how to use Stata more efficiently, enabling them to do things even faster.
Overall, the course is designed to be easy to follow, taking just 40 minutes to complete. The focus is on the essential features of Stata, making it easier for individuals to use the software with confidence.