# Statistics 1 Part 2: Statistical Methods Review (2026): 8.5/10 · EDX > Independent review of Statistics 1 Part 2: Statistical Methods Course on EDX. Rated 8.5/10 by our editorial team. Pros, cons, price, and top alternatives. Fr… Statistics 1 Part 2: Statistical Methods Course ![Statistics 1 Part 2: Statistical Methods Course](/api/media/file/hero/statistics-1-part-2-statistical-methods-course.jpg?width=800) # Statistics 1 Part 2: Statistical Methods Course — Review (8.5/10) This course effectively builds on introductory statistics with clear explanations of inference, regression, and data presentation. While it assumes only moderate math skills, the pacing may challenge ... Explore This Course Statistics 1 Part 2: Statistical Methods Course is a 5 weeks online intermediate-level course on EDX by The London School of Economics and Political Science that covers data science. This course effectively builds on introductory statistics with clear explanations of inference, regression, and data presentation. While it assumes only moderate math skills, the pacing may challenge beginners. The practical focus supports real-world application, though deeper theoretical context is limited. A solid choice for career-oriented learners seeking applied statistical fluency. We rate it 8.5/10. ## Prerequisites Basic familiarity with data science fundamentals is recommended. An introductory course or some practical experience will help you get the most value. ## Pros - Clear progression from basic to intermediate statistical concepts - Practical emphasis on data presentation and interpretation - Strong focus on real-world applications of inference and regression - High-quality instruction from a respected institution (LSE) ## Cons - Fast pace may overwhelm learners without prior stats exposure - Limited theoretical depth for mathematically inclined students - Verified certificate required for formal credential ## Statistics 1 Part 2: Statistical Methods Course Review Platform: EDX Instructor: The London School of Economics and Political Science Updated Apr 23, 2026·Editorial Standards·How We Rate ## What will you learn in Statistics 1 Part 2: Statistical Methods course - be familiar with some further key ideas of statistics that are accessible to a student with a moderate mathematical competence - be able to routinely apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly using appropriate diagrams, titles and labels when required - To explain the fundamentals of statistical inference and perform inference to test the significance of common measures such as means and proportions and conduct chi-squared tests of contingency tables - be able to use simple linear regression and correlation analysis and know when it is appropriate to do so ### Program Overview ### Module 1: Foundations of Statistical Inference Duration estimate: Week 1-2 - Sampling distributions and central limit theorem - Confidence intervals for means and proportions - Hypothesis testing principles and p-values ### Module 2: Categorical Data Analysis Duration: Week 3 - Chi-squared distribution and goodness-of-fit tests - Contingency tables and independence testing - Applications in real-world datasets ### Module 3: Correlation and Regression Fundamentals Duration: Week 4 - Scatter plots and correlation coefficients - Simple linear regression model assumptions - Interpreting regression output and R-squared ### Module 4: Practical Data Interpretation and Presentation Duration: Week 5 - Effective visualisation techniques - Labelling and titling graphs correctly - Reporting statistical findings clearly ### Get certificate #### Job Outlook - High demand for statistical literacy in data analytics and research roles - Skills applicable in business, government, and health sectors - Strong foundation for advanced data science or social science study ## Editorial Take The London School of Economics delivers a rigorous yet accessible continuation of its foundational statistics series with 'Statistics 1 Part 2: Statistical Methods'. This course targets learners who have completed introductory material and are ready to engage with inference, regression, and data communication in a structured, academically grounded format. Hosted on edX, it balances theoretical concepts with practical application, making it ideal for those aiming for data fluency in professional or academic contexts. ### Standout Strengths - Academic Rigor with Accessibility: LSE maintains high academic standards while ensuring content remains approachable for students with moderate mathematical backgrounds. Concepts are introduced with clarity and reinforced through examples. This balance supports broad accessibility without sacrificing depth. - Applied Focus on Data Communication: The course emphasizes how to explain, summarize, and present data effectively. Learners gain skills in creating well-labeled diagrams and clear reports—critical for real-world data roles where communication is as important as analysis. - Strong Foundation in Inference: Students master core inferential techniques including confidence intervals, hypothesis testing, and chi-squared tests. These methods are essential for evidence-based decision-making across industries from public policy to business analytics. - Regression Readiness: The introduction to simple linear regression and correlation prepares learners for more advanced modeling. The course clearly defines when such methods are appropriate, fostering responsible statistical practice. - Modular, Time-Efficient Design: Spread over five focused weeks, the course is structured to fit working professionals. Each module builds logically, allowing steady progression without overwhelming time commitments. - Career-Aligned Skill Development: The competencies taught—data interpretation, statistical testing, and regression—are directly transferable to roles in data analysis, research, and business intelligence. This makes the course highly relevant for career advancement. ### Honest Limitations - Pacing May Challenge Some: The five-week format moves quickly, especially for those new to statistics. Learners without a solid grasp of Part 1 may struggle to keep up with inference concepts and regression assumptions. - Limited Hands-On Practice: While concepts are well-explained, the course lacks extensive interactive exercises or coding components. More applied problem sets would enhance retention and skill mastery. - Certificate Requires Payment: Although the course is free to audit, the Verified Certificate comes at a cost. This may deter some learners seeking formal recognition without financial investment. - Theoretical Depth is Moderate: The course prioritizes application over deep mathematical derivation. Those seeking rigorous proofs or advanced theory may need supplementary resources. ### How to Get the Most Out of It - Study cadence: Dedicate 6–8 hours weekly to fully absorb material. Spread sessions across four days to reinforce learning and allow time for reflection on statistical concepts and their interpretations. - Parallel project: Apply techniques to a personal dataset—such as survey results or public data—to practice summarizing, testing, and visualizing findings in context. - Note-taking: Use structured templates for each statistical method, noting assumptions, steps, and interpretation rules to build a personal reference guide. - Community: Join edX discussion forums to clarify doubts, share examples, and compare interpretations of statistical outputs with peers. - Practice: Re-work all provided examples manually or with software like Excel or R to deepen understanding of regression and chi-squared test procedures. - Consistency: Maintain a steady schedule; statistical concepts build cumulatively, and falling behind can hinder comprehension of later modules. ### Supplementary Resources - Book: 'Statistics' by David Freedman et al. provides deeper theoretical context and additional practice problems aligned with LSE's approach. - Tool: Use JASP or Jamovi for intuitive statistical software practice with visual output similar to course examples. - Follow-up: Enroll in 'Statistics 1 Part 3' to continue building toward full quantitative proficiency in the series. - Reference: The LSE StatsHelp website offers tutorials and FAQs that complement course content and clarify common misunderstandings. ### Common Pitfalls - Pitfall: Misinterpreting p-values as effect size or certainty. Always contextualize hypothesis test results with confidence intervals and practical significance. - Pitfall: Applying regression without checking assumptions. Ensure linearity, independence, and homoscedasticity are assessed before drawing conclusions. - Pitfall: Overlooking data presentation standards. Always include clear titles, axis labels, and units to ensure diagrams are interpretable by diverse audiences. ### Time & Money ROI - Time: At 5 weeks with 6–8 hours/week, the time investment is reasonable for the skill gain, especially for career switchers or academic preparation. - Cost-to-value: Free audit access offers exceptional value; even without certification, learners gain actionable statistical knowledge at no cost. - Certificate: The Verified Certificate enhances credibility for resumes and LinkedIn, justifying its fee for job seekers needing formal validation. - Alternative: Free YouTube or MOOC content may cover similar topics, but LSE’s structured curriculum and academic backing provide superior coherence and trust. ### Editorial Verdict The 'Statistics 1 Part 2: Statistical Methods' course stands out as a well-structured, academically rigorous program that bridges foundational knowledge and practical application. By focusing on inference, regression, and data communication, it equips learners with tools essential for data-driven decision-making in business, research, and public policy. The London School of Economics’ reputation ensures high-quality content delivery, and the modular design supports flexible learning. While the pace may challenge some, the course’s clarity and real-world relevance make it a strong choice for motivated learners. For those pursuing careers in data science, analytics, or social sciences, this course offers a valuable credential and skill set—even in audit mode. The free access model lowers barriers to entry, promoting inclusivity in quantitative education. However, learners seeking hands-on coding or deeper theoretical exploration may need to supplement with external resources. Overall, it earns a strong recommendation as a stepping stone in a broader quantitative learning journey, especially when paired with the full four-part series. It’s a smart investment of time for anyone serious about building credible, applicable statistical expertise. ## How Statistics 1 Part 2: Statistical Methods Course Compares | Course | Platform | Rating | Level | Duration | | --- | --- | --- | --- | --- | | Statistics 1 Part 2: Statistical Methods Course | EDX | 8.5/10 | Intermediate | 5 weeks | | Geographic Information Systems (GIS) Specialization Course | Coursera | 9.8/10 | N/A | N/A | | IBM Data Management Professional Certificate Course | Coursera | 9.8/10 | N/A | N/A | | DeepLearning.AI Data Analytics Professional Certificate Course | Coursera | 9.8/10 | N/A | N/A | ## Who Should Take Statistics 1 Part 2: Statistical Methods Course? This course is best suited for learners with foundational knowledge in data science and want to deepen their expertise. Working professionals looking to upskill or transition into more specialized roles will find the most value here. The course is offered by The London School of Economics and Political Science on EDX, combining institutional credibility with the flexibility of online learning. Upon completion, you will receive a verified certificate that you can add to your LinkedIn profile and resume, signaling your verified skills to potential employers. If you are exploring adjacent fields, you might also consider courses in Agile & Scrum Courses, AI Courses, Arts and Humanities Courses, which complement the skills covered in this course. ### Career Outcomes - Apply data science skills to real-world projects and job responsibilities - Advance to mid-level roles requiring data science proficiency - Take on more complex projects with confidence - Add a verified certificate credential to your LinkedIn and resume - Continue learning with advanced courses and specializations in the field ## More Data Science Courses on EDX Explore other highly rated courses in data science available on EDX to expand your learning path: - HarvardX: Data Science: R Basics course 9.7/10 - DavidsonX: Analyzing and Visualizing Data with Power BI course 9.7/10 - HarvardX: Fundamentals of TinyML course 9.7/10 - HarvardX: CS50’s Introduction to Databases with SQL course 9.7/10 - HarvardX: Data Science: Visualization course 9.7/10 - TUMx: Six Sigma Part 2: Analyze, Improve, Control course 9.7/10 - HarvardX: Data Science: Probability course 9.7/10 - HarvardX: Data Science: Inference and Modeling course 9.7/10 - HarvardX: Causal Diagrams: Draw Your Assumptions Before Your Conclusions course 9.7/10 - HarvardX: Data Science: Capstone course 9.7/10 ## Top Alternatives on Other Platforms Looking for a different teaching style or approach? These top-rated data science courses from other platforms cover similar ground: - Geographic Information Systems (GIS) Specialization Course 9.8/10 Coursera - IBM Data Management Professional Certificate Course 9.8/10 Coursera - DeepLearning.AI Data Analytics Professional Certificate Course 9.8/10 Coursera - Prepare Data for Exploration Course 9.8/10 Coursera - Process Data from Dirty to Clean Course 9.8/10 Coursera - Analyze Data to Answer Questions Course 9.8/10 Coursera - Sequence Models Course 9.8/10 Coursera - Generative Adversarial Networks (GANs) Specialization Course 9.8/10 Coursera - Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital Course 9.8/10 Coursera - Executive Data Science Specialization Course 9.8/10 Coursera ## More Courses from The London School of Economics and Political Science The London School of Economics and Political Science offers a range of courses across multiple disciplines. If you enjoy their teaching approach, consider these additional offerings: - Maths Essentials Course 8.5/10 - Mathematics and Statistics Fundamentals Proctored Exam Course 8.5/10 - Mathematics 1 Part 2: Integral Calculus, Algebra, and Applications Course 8.5/10 - Maths Essentials: Proctored Exam Course 8.5/10 - Mathematics 1 Part 1: Differential Calculus Course 8.5/10 - Practical People Management: Motivating and Engaging Teams Course 8.5/10 - Sustainability Fundamentals: Concepts, Policy, and Navigating Transitions Course 8.5/10 - Corporate Communication Strategy Course 8.5/10 View all courses from The London School of Economics and Political Science → ## Related Articles & Guides Deepen your understanding with these articles from our editorial team, covering career advice, industry trends, and learning strategies: - Build AI skills with the Google AI Professional Certificate - Python Tutorial: Best Courses to Learn Python in 2026 - CISSP vs CompTIA Security+: Which Cert Should You Pursue? - Coursera Data Analytics Professional Certificate: Worth It in 2026? - Best edX Courses in 2026: Top Picks by Enrollment and Career Value - Best Online Coursera Courses in 2026: What's Actually Worth Your Time - Udemy Online: What the Platform Actually Delivers in 2026 - OKR for Leaders: 7 Best Training Courses Compared (2026) - Generative AI for Marketing with Microsoft 365 Copilot: Professional Certificate Review - The Best React Courses in 2026, Ranked and Reviewed ## Explore All Course Categories Not sure what to learn next? Browse our full catalog of course categories to find the right fit for your career goals: Agile & Scrum Courses AI Courses Arts and Humanities Courses Business & Management Courses Cloud Computing Courses Computer Science Courses Construction Management Courses Cybersecurity Courses Data Analyst Courses Data Analytics Courses Data Engineering Courses Data Science Courses Design Courses Developer Courses Economics & Finance Courses Education & Teacher Training Courses Entrepreneurship Courses Excel Courses Finance Courses Game Development Courses Graphic Design Courses Health Science Courses Information Technology Courses Language Learning Courses Leadership Courses Lifestyle Courses Machine Learning Courses Marketing Courses Math and Logic Courses Music Courses Negotiation Courses Office Productivity Courses Other Personal Development Courses Photography & Videography Courses Physical Science and Engineering Courses Project Management Courses Python Courses SEO Courses Social Media Marketing Courses Social Sciences Courses Software Development Courses Supply Chain Management Courses Teaching Courses Uncategorized UX Design Courses Web Development Courses Explore related topics Machine Learning Data Analytics Data Analyst Python Explore Related Topics Best Data Science Courses Learning Path How to Become a Data Analyst Browse All Courses ## User Reviews No reviews yet. Be the first to share your experience! ## FAQs What are the prerequisites for Statistics 1 Part 2: Statistical Methods Course? A basic understanding of Data Science fundamentals is recommended before enrolling in Statistics 1 Part 2: Statistical Methods Course. Learners who have completed an introductory course or have some practical experience will get the most value. The course builds on foundational concepts and introduces more advanced techniques and real-world applications. Does Statistics 1 Part 2: Statistical Methods Course offer a certificate upon completion? Yes, upon successful completion you receive a verified certificate from The London School of Economics and Political Science. This credential can be added to your LinkedIn profile and resume, demonstrating verified skills to employers. In competitive job markets, having a recognized certificate in Data Science can help differentiate your application and signal your commitment to professional development. How long does it take to complete Statistics 1 Part 2: Statistical Methods Course? The course takes approximately 5 weeks to complete. It is offered as a free to audit course on EDX, which means you can learn at your own pace and fit it around your schedule. The content is delivered in English and includes a mix of instructional material, practical exercises, and assessments to reinforce your understanding. Most learners find that dedicating a few hours per week allows them to complete the course comfortably. What are the main strengths and limitations of Statistics 1 Part 2: Statistical Methods Course? Statistics 1 Part 2: Statistical Methods Course is rated 8.5/10 on our platform. Key strengths include: clear progression from basic to intermediate statistical concepts; practical emphasis on data presentation and interpretation; strong focus on real-world applications of inference and regression. Some limitations to consider: fast pace may overwhelm learners without prior stats exposure; limited theoretical depth for mathematically inclined students. Overall, it provides a strong learning experience for anyone looking to build skills in Data Science. How will Statistics 1 Part 2: Statistical Methods Course help my career? Completing Statistics 1 Part 2: Statistical Methods Course equips you with practical Data Science skills that employers actively seek. The course is developed by The London School of Economics and Political Science, whose name carries weight in the industry. The skills covered are applicable to roles across multiple industries, from technology companies to consulting firms and startups. Whether you are looking to transition into a new role, earn a promotion in your current position, or simply broaden your professional skillset, the knowledge gained from this course provides a tangible competitive advantage in the job market. Where can I take Statistics 1 Part 2: Statistical Methods Course and how do I access it? Statistics 1 Part 2: Statistical Methods Course is available on EDX, one of the leading online learning platforms. You can access the course material from any device with an internet connection — desktop, tablet, or mobile. The course is free to audit, giving you the flexibility to learn at a pace that suits your schedule. All you need is to create an account on EDX and enroll in the course to get started. How does Statistics 1 Part 2: Statistical Methods Course compare to other Data Science courses? Statistics 1 Part 2: Statistical Methods Course is rated 8.5/10 on our platform, placing it among the top-rated data science courses. Its standout strengths — clear progression from basic to intermediate statistical concepts — set it apart from alternatives. What differentiates each course is its teaching approach, depth of coverage, and the credentials of the instructor or institution behind it. We recommend comparing the syllabus, student reviews, and certificate value before deciding. What language is Statistics 1 Part 2: Statistical Methods Course taught in? Statistics 1 Part 2: Statistical Methods Course is taught in English. Many online courses on EDX also offer auto-generated subtitles or community-contributed translations in other languages, making the content accessible to non-native speakers. The course material is designed to be clear and accessible regardless of your language background, with visual aids and practical demonstrations supplementing the spoken instruction. Is Statistics 1 Part 2: Statistical Methods Course kept up to date? Online courses on EDX are periodically updated by their instructors to reflect industry changes and new best practices. The London School of Economics and Political Science has a track record of maintaining their course content to stay relevant. We recommend checking the "last updated" date on the enrollment page. Our own review was last verified recently, and we re-evaluate courses when significant updates are made to ensure our rating remains accurate. Can I take Statistics 1 Part 2: Statistical Methods Course as part of a team or organization? Yes, EDX offers team and enterprise plans that allow organizations to enroll multiple employees in courses like Statistics 1 Part 2: Statistical Methods Course. Team plans often include progress tracking, dedicated support, and volume discounts. This makes it an effective option for corporate training programs, upskilling initiatives, or academic cohorts looking to build data science capabilities across a group. What will I be able to do after completing Statistics 1 Part 2: Statistical Methods Course? After completing Statistics 1 Part 2: Statistical Methods Course, you will have practical skills in data science that you can apply to real projects and job responsibilities. You will be equipped to tackle complex, real-world challenges and lead projects in this domain. Your verified certificate credential can be shared on LinkedIn and added to your resume to demonstrate your verified competence to employers. ## Similar Courses Other courses in Data Science Courses ![Modeling Climate Anomalies with Statistical Analysis](/api/media/file/hero/modeling-climate-anomalies-with-statistical-analysis-course.webp?v=2?width=480) Coursera Data Science Courses ### Modeling Climate Anomalies with Statistical Analysis ★★★½☆ Coursera View Course » Enroll ![Case Studies in Geostatistical Modeling](/api/media/file/hero/case-studies-in-geostatistical-modeling-course.webp?v=2?width=480) Coursera Physical Science and Engineering Courses ### Case Studies in Geostatistical Modeling ★★★★½ Coursera View Course » Enroll ![Statistical Inference and Modeling for High-throughput Experiments Course](/api/media/file/hero/statistical-inference-modeling-high-throughput-experiments-course.jpg?width=480) EDX Data Science Courses ### Statistical Inference and Modeling for High-throughput Experiments Course ★★★★½ EDX View Course » Enroll ![Apply Advanced Statistical Modeling Using SPSS](/api/media/file/hero/apply-advanced-statistical-modeling-using-spss-course.webp?v=2?width=480) Coursera Data Analytics Courses ### Apply Advanced Statistical Modeling Using SPSS ★★★★½ Coursera View Course » Enroll ![Data Analysis: Statistical Modeling and Computation in Applications](/api/media/file/hero/data-analysis-statistical-modeling-computation-applications-course.jpg?width=480) EDX Data Science Courses ### Data Analysis: Statistical Modeling and Computation in Applications ★★★★½ EDX View Course » Enroll ![Analyze Data Using R for Statistical and Predictive Modeling Course](/api/media/file/hero/analyze-data-using-r-for-statistical-and-predictive-modeling-course.webp?v=2?width=480) Coursera Data Analytics Courses ### Analyze Data Using R for Statistical and Predictive Modeling Course ★★★★☆ Coursera View Course » Enroll ## Related Job Opportunities ### High School Teacher Asian College Of Teachers is a trading brand of TTA Training Pvt. Ltd Warszawa, PL Full-Time PLN 54–86/yr ### Alternance chargé(e) de communication & marketing produit SaaS - Paris (F/H) OKTOGONE Paris, FR Full-Time ### Bautechnik Freileitungsmast Planung Infrastruktur (m/w/d) 50Hertz Transmission GmbH Berlin, DE Full-Time ### Ingenieur Energietechnik als Projektmanager Inbetriebnahme & Dokumentation (m/w/d) 50Hertz Transmission GmbH Berlin, DE Full-Time ### IT Governance Compliance Managerin (m/w/d) 50Hertz Transmission GmbH Berlin, DE Full-Time Browse more jobs on JobsNearMe.career → ### Explore Related Categories All Data Science Courses Explore Course Reviews ### Review: Statistics 1 Part 2: Statistical Methods Course Your Name * Email (optional, not displayed) Rating * Your Review * ### Discover More Course Categories Explore expert-reviewed courses across every field AI Courses Python Courses Machine Learning Courses Web Development Courses Cybersecurity Courses Data Analyst Courses Excel Courses Cloud & DevOps Courses UX Design Courses Project Management Courses SEO Courses Agile & Scrum Courses Business Courses Marketing Courses Software Dev Courses Browse all 10,000+ courses »