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Private lessonsStatisticsStatistics in Manchester

5 statistics teachers in Manchester

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5 statistics teachers in Manchester

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Amine - Paris, France£37
£37Expert and experienced Professor: SPSS, R, jamovi, jasp, STATA, hypothesis testing, ANOVA, multivariate analysis, linear/log regression
Trusted teacher: During this course you will learn: ✓ SPSS ✓ R Studio ✓jamovi/jasp/Stata/xlstat ✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS ✓ Simple and multiple linear regression and Logistic regression ✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers) ✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means) ✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman) ✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters) ___________________________________ ✓ My courses are based on exercises with the essentials of the course to remember. ✓ Working method for better understanding. ✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.
Statistics · Math · Economics for students
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William - Accra, Ghana£21
£21Research Methodology Toolkit for Biologists: The Golden Solution to Every Novice Scientist's Dilemma
Trusted teacher: Are you eager to master the foundational principles of research methodology and unlock the tools for solving complex research challenges? This dynamic and practical course is your gateway to becoming a confident and skilled researcher. Packed with engaging lessons, real-world applications, and hands-on activities, you will acquire essential skills to design, execute, and publish impactful research. Whether you are a beginner or looking to enhance your expertise, this course will empower you to confidently tackle research projects and turn your findings into publications that make a difference. Join me and take your research capabilities to the next level! SYLLABUS Module 1: Foundations of Biological Research 🔵 Lesson 1.1: Understanding the Research Process in Biology ◘ Definition and scope of biological research ◘ Types of biological research (basic, applied, translational) 🔵 Lesson 1.2: Identifying Research Questions in Biology ◘ Characteristics of impactful biological research questions ◘ Refining questions for molecular biology, ecology, genomics, etc. 🔵 Lesson 1.3: Conducting a Literature Review in Biology ◘ Identifying relevant biological journals and databases (e.g., PubMed, Web of Science) ◘ Critical analysis of biological papers Module 2: Designing Your Biological Research 🔵 Lesson 2.1: Research Design for Biologists ◘ Experimental vs. observational studies in biology ◘ Designing robust controls and replicates 🔵 Lesson 2.2: Hypothesis Formulation in Biology ◘ Writing testable biological hypotheses ◘ Defining null and alternative hypotheses 🔵 Lesson 2.3: Sampling in Biological Studies ◘ Strategies for collecting biological samples (field and lab-based) ◘ Addressing sample size in population studies and molecular analyses Module 3: Biological Data Collection Techniques 🔵 Lesson 3.1: Experimental Techniques in Biology ◘ Common lab methods (e.g., PCR, Western blotting, microscopy) ◘ Good lab practices (GLP) for reproducibility 🔵 Lesson 3.2: Fieldwork for Biologists ◘ Designing ecological surveys and biodiversity studies ◘ Tools for field sampling (e.g., GPS, quadrats, transects) 🔵 Lesson 3.3: Handling Biological Specimens ◘ Sample preservation techniques for DNA, RNA, and proteins ◘ Best practices for labeling and storage Module 4: Biological Data Analysis and Interpretation 🔵 Lesson 4.1: Introduction to Statistical Analysis for Biologists ◘ Biostatistics fundamentals (e.g., t-tests, ANOVA, regression) ◘ Using R, Python, or SPSS for biological data 🔵 Lesson 4.2: Analyzing Genomic and Proteomic Data ◘ Tools like BLAST, MEGA, and Galaxy for sequence analysis ◘ Basics of bioinformatics workflows 🔵 Lesson 4.3: Interpreting Biological Results ◘ Connecting results to biological hypotheses ◘ Identifying and discussing limitations in biological research Module 5: Writing and Publishing in Biological Sciences 🔵 Lesson 5.1: Structuring a Biological Research Paper ◘ IMRAD format tailored for biological journals ◘ Writing clear and concise methods and results 🔵 Lesson 5.2: Referencing for Biologists ◘ Citation styles in biological sciences (e.g., Vancouver, APA) ◘ Using referencing tools specific to biology (e.g., EndNote, Zotero) 🔵 Lesson 5.3: Publishing in Biological Journals ◘ Identifying target journals (e.g., Nature, Cell, Microbial Genomics) ◘ Addressing reviewer comments Module 6: Ethics and Best Practices in Biological Research 🔵 Lesson 6.1: Ethical Considerations in Biology ◘ Handling live organisms and human samples ◘ Regulatory approvals (e.g., IACUC, IRB) 🔵 Lesson 6.2: Managing Biological Data ◘ FAIR principles (Findable, Accessible, Interoperable, Reusable) for biological data ◘ Data repositories for biology (e.g., NCBI, Dryad) 🔵 Lesson 6.3: Collaboration in Biology ◘ Building interdisciplinary teams (ecologists, geneticists, bioinformaticians) ◘ Leveraging platforms like ResearchGate for biologists Module 7: Practical Toolkit and Case Studies in Biology 🔵 Lesson 7.1: Tools for Efficient Biological Research ◘ Lab-specific tools (e.g., electronic lab notebooks, ELNs like LabArchives) ◘ Visualization tools (e.g., GraphPad Prism, BioRender) 🔵 Lesson 7.2: Case Studies in Biological Research ◘ Genomic studies on antimicrobial resistance pathogens ◘ Population studies in biodiversity hotspots ◘ Analyzing molecular mechanisms in model organisms
Writing · Statistics · Biology
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Ahmed - Brussels, Belgium£58
£58Statistics Courses By a University Professor
Trusted teacher: In order to support students who encounter difficulties in statistics, remedial sessions on descriptive statistics and inferential statistics are offered in particular on: confidence interval, statistical hypothesis test, Paired data or not? "Normal" or not? Parametric and non-parametric tests, Confidence interval and "number of subjects needed" in estimating a proportion, Also the analysis of your thesis data (linear / logistic regression, anova, t-tests, principal component analysis, repeated measures, mixed models, time series, panel analyzes, fixed and random effects models, survival analysis, SEM, ...). Do you need to be accompanied to motivate you? I offer lessons to see or review together a chapter, a theme or others ... During these remediation sessions, I offer you various activities (Q&A sessions, resolution of standard exams, revision of concepts more difficult to integrate, etc.) which help students to fill in the gaps identified in previous courses. Passionate about mathematics and quantum physics, juggles with statistics on a daily basis in my BIGDATA research work, demystifies this test by the precise approach of the subjects via an adapted teaching whose key words - study methodology, - reasoning, and especially learning to reason differently - analysis and response strategies, - targeting of the problem - implementation of the link between theory and practice
Statistics
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Kian - Bern, Switzerland£38
£38Statistics. Machine Learning. Mathematics. Clarity & Competence from Bern – For Swiss Students
Data science, statistics & mathematics – clearly explained, personally supported. My name is Kian, an experienced tutor from Bern. I support students, career starters, and professionals on their journey into the data-driven world—whether in their studies, projects, or everyday work. With my structured, understandable, and motivating approach, I'll help you not only solve problems but also understand data, recognize connections, and make informed decisions. My lessons are personalized, efficient, and at eye level. Who I am – and why I teach: I teach in Bern and successfully completed the MAS program in Statistical Data Science at the University of Bern, focusing on statistics, mathematics, and data science. For several years, I have been passionately teaching, both in academic contexts and for professionals who want to think and work more data-driven. In parallel to my teaching, I have implemented numerous data science projects – from exploratory analysis and data modeling to decision support in a business context. This combination of theory and practice makes my teaching particularly close to reality while remaining accessible. I love making complex topics tangible, creating aha moments, and empowering people to handle data confidently. For me, tutoring isn't just about imparting knowledge—it's about developing their minds on equal terms. -What you can expect from me: One-to-one lessons with a focus on data comprehension, statistics & analytical thinking Support with projects, assignments, exams or getting started in the data world Practical explanations – step by step and adapted to your everyday life Teaching modern methods for data analysis, modeling & interpretation Long-term strategies for problem-solving & structured thinking Flexible lessons in Bern or online – personal, competent & reliable Why I can help you understand data science: Because I work at the interface between science and practice. I know how quickly you can get lost in formulas and tools—and I'll help you see the common thread: How data tells stories, how you analyze it, and how you make smart decisions. With me, you won't just learn methods—you'll learn how to think with data. Whether in your studies or in your career, I'll guide you in truly understanding data and applying it confidently. Learn data analysis. Analyze and model complex data sets – understandable, practical and with structure. If you're ready to get to grips with data, I'm ready to guide you.
Statistics · Computer programming · Math
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Abdou - Riyadh, Saudi Arabia£28
£28Statistics, Data Visualization, and Machine Learning for Beginners
📊 Introduction to Data Science with Python Full Title: Statistics, Data Visualization, and Machine Learning for Beginners (100% online course – for students, professionals in retraining, or curious data enthusiasts) Data science is now at the heart of the most innovative professions and strategic decisions in all sectors. However, when you're just starting out, you can quickly feel overwhelmed by technical jargon, Python libraries, or statistical models. With this course, my goal is to make this exciting discipline understandable and accessible to everyone, even without advanced mathematical training or computer science background. I offer step-by-step support based on practical experience, concrete projects, and a supportive teaching approach. You'll learn how to manipulate data, extract information from it, and create your first machine learning models with ease. 🎯 Course objectives Discover the basic tools of Data Science with Python Understand and apply the fundamental concepts of exploratory statistics Know how to manipulate, clean, visualize and interpret real data sets Carry out initial predictive modeling (linear regression, classification) 📚 Course content ✔ Fundamental libraries in Data Science – pandas: reading, cleaning and transforming data – numpy: mathematical operations and array manipulation – matplotlib & seaborn: clear and aesthetic data visualization – Getting Started with Scikit-Learn for Machine Learning ✔ Data cleaning and analysis – CSV file import and data mining – Management of missing values and duplicates – Creation of variables, filtering, groupings – Visualization: histograms, curves, heatmaps, boxplots... ✔ Introduction to Machine Learning – Understand how linear and logistic regression work – First classification models (KNN, simple decision trees) – Data separation (training/test set), single cross-validation – Interpretation of results and improvement of the model 🧭 How the sessions work 1️⃣ Assessment of the student's objectives: discovery, professional project, preparation for training, etc. 2️⃣ Personalized progression plan, adapted to the starting level. 3️⃣ Alternation of visual theory and intensive practice on real data sets (health, sports, finance, etc.). 4️⃣ Practical mini-projects at each stage: analyzing survey results, predicting simple results, automating analyses. 5️⃣ Explanation of errors encountered, individualized educational monitoring. 6️⃣ Regular assessment, with reinforcement of key points as needed. 🌐 100% online courses – accessible teaching methods Classes via Zoom, Google Meet, or the tool of your choice Live screen sharing, work on interactive notebook (Jupyter or Google Colab) PDF supports + commented code provided after each session Possibility of intensive coaching for training or an interview Flexible hours, adapted to the time zone of the Gulf countries and your availability 👨‍🎓 For whom? Complete beginners in Data Science and Python Students wishing to enrich their profile with practical skills Professionals retraining for data professions Anyone curious about understanding the world through data! This course has been designed so that each participant can progress at their own pace, develop their analytical logic and discover the pleasure of "making the data speak". Feel free to contact me to discuss your goals and build a customized program together. I would be delighted to accompany you on this wonderful adventure that is data science.
Statistics · Math · Numerical analysis
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Our students from Manchester evaluate their Statistics teacher.

To ensure the quality of our Statistics teachers, we ask our students from Manchester to review them.
Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.8 out of 5 based on 32 reviews.

Data science , Analytics, Machin learning, statistics
Srinivas
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Srinivas is a skilled tutor. He is patient and has a problem-solving attitude. Shriniva's goal is that students master the subject. An excellent tutor for someone that wants to work hard and become independant user.
Review by IFI
Statistics, Econometrics (including calibration of models on real data) (Adliswil)
Gianmarco
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Gianmarco helped me understand and solve my problems in a friendly, quick and clear manner. He displayed a thorough understanding of the topic. I very much appreciate him as a teacher and can recommend him to anyone.
Review by CHRISTINA
Programming and Data Analysis, Matlab, Python, Fortran, SPSS
Nikolaos
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Nikolas is a very depentable tutor with an abudant amount of knowledge in programming. He was very helpful in preparing me for my programming exam and I higly recommend him!
Review by KYRIAKOS

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