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Our Courses
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models.
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Self Paced
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English
Introduction to Bayesian Statistics Using R
Learn the fundamentals of Bayesian approach to data analysis, and practice answering real life questions using R.
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Self Paced
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14
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English
Fat Chance: Probability from the Ground Up
Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.
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Self Paced
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30
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English
Mathematics and Statistics Fundamentals Proctored Exam
Test your knowledge and ability to apply the concepts and methods from the four courses included in the LSE MicroBachelors program in Mathematics and Statistics Fundamentals and take your first step towards further study at undergraduate level or upskill in high-growth careers.
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Self Paced
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English
Statistics 1 Part 1: Introductory statistics, probability and estimation
The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
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Self Paced
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English
Statistics 1 Part 2: Statistical Methods
The second in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
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Self Paced
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English
Statistics 2 Part 1: Probability and Distribution Theory
The third in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
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Self Paced
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English
Statistics 2 Part 2: Statistical Inference
The final part in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
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Course by
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Self Paced
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English
Statistics Fundamentals Proctored Exam
Test your knowledge and ability to apply the concepts and methods from the four courses included in the LSE MicroBachelors program in Statistics Fundamentals and take your first step towards further study at undergraduate level or upskill in high-growth careers.
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Course by
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Self Paced
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English
Supply Chain Analytics
Master and apply the core methodologies used in supply chain analysis and modeling, including statistics, regression, optimization and probability - part of the MITx Supply Chain Management MicroMasters Credential.
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English
MathTrackX: Statistics
Understand fundamental concepts relating to statistical inference and how they can be applied to solve real world problems.
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Self Paced
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30
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English
Statistics for the Behavioral Sciences
How do statistics apply to your life and how can we use statistics to draw conclusions about the world? This course will provide you with an integrated and engaging online experience to explore statistics in the behavioral sciences.
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27
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English
Statistics
This course provides an overview of bachelor-level statistics. You will review the concepts of descriptive and inferential statistics. You will use the statistical software package R on real data to gain insight in these topics.
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Self Paced
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35
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English
Intro Course: Spreadsheets and Statistics
Bridge the gap between what you know and the skills you need to ensure your success and efficiency in an office setting. This course is ideal for learners who are just starting out in their professional careers, those looking to add some hard skills to their soft skills, or those looking to make the leap from contractor to full-time employee.
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Self Paced
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27
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English
Probability and Statistics IV: Confidence Intervals and Hypothesis Tests
This course covers two important methodologies in statistics – confidence intervals and hypothesis testing. Confidence intervals allow us to make probabilistic statements such as: “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” Hypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug?"
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Self Paced
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English
Advanced Bayesian Statistics Using R
Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods
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21
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English
Data Science: Foundations using R
Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data. The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization.
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Self Paced
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English
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program.
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Self Paced
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7 hours
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English
Emerging Technologies: From Smartphones to IoT to Big Data
This Specialization is intended for researchers and business experts seeking state-of-the-art knowledge in advanced science and technology. The 4 courses cover details on Big Data (Hadoop, Spark, Storm), Smartphones, Smart Watches, Android, iOS, CPU/GPU/SoC, Mobile Communications (1G to 5G), Sensors, IoT, Wi-Fi, Bluetooth, LP-WAN, Cloud Computing, AR (Augmented Reality), Skype, YouTube, H.264/MPEG-4 AVC, MPEG-DASH, CDN, and Video Streaming Services.
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Self Paced
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English
Data Understanding and Visualization
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication. Learning Objectives: 1.
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25 hours
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English
Risk Management
This 4-course Specialization from the New York Institute of Finance (NYIF) is intended for STEM undergraduates, finance practitioners, bank and investment managers, business managers, regulators, and policymakers. This Specialization will teach you how to measure, assess, and manage risk in your organization.
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Self Paced
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English
Unsupervised Text Classification for Marketing Analytics
Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment.
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Self Paced
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13 hours
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English
BI Foundations with SQL, ETL and Data Warehousing
The job market for business intelligence (BI) analysts is expected to grow by23 percent from 2021 to 2031 (US Bureau of Labor Statistics). This IBM specialization gives you sought-after skills employers look for when recruiting for a BI analyst. BI analysts gather, clean, and analyze key business data to find patterns and insights that aid business decision-making. During this specialization, you’ll learn the basics of SQL, focusing on how to query relational databases using this popular and powerful language. You’ll use essential Linux commands to create basic shell scripts.
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Self Paced
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English
Business Analytics
Nowadays data is essential in the business environment to be gain a global view of an organization's situation and to be able to make better decisions. Against this background, Big Data is a booming sector in multiple areas that opens up different job opportunities, such as the role of Data Analyst. This course is an introduction to data analytics from a business perspective, focusing on the relevance of basing strategic decision-making on the knowledge provided by data.
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English
Mindware: Critical Thinking for the Information Age
Most professions these days require more than general intelligence. They require in addition the ability to collect, analyze and think about data. Personal life is enriched when these same skills are applied to problems in everyday life involving judgment and choice. This course presents basic concepts from statistics, probability, scientific methodology, cognitive psychology and cost-benefit theory and shows how they can be applied to everything from picking one product over another to critiquing media accounts of scientific research.
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Self Paced
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13 hours
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English