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1 statistics teacher in Ar Rifā‘

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1 statistics teacher in Ar Rifā‘

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Amine - Paris, France2529 EGP
2529 EGPExpert 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, Ghana1387 EGP
1387 EGPResearch 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, Belgium3973 EGP
3973 EGPStatistics 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
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Our students from Ar Rifā‘ evaluate their Statistics teacher.

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Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.7 out of 5 based on 26 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
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
Mathematics Tutor (ALL LEVELS~ Highschool To Masters) (Neukölln)
Layane
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Layane is patient, competent, and professional. Our child looks forward to her lessons.
Review by JOEL

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