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William - Accra, Ghana14Fr
14FrFundamentals of SPSS for Biologists: A step by step approach to doing more with SPSS
Trusted teacher: 🔰 SPSS is one of the leading statistical tools used by researchers, data scientists, and students worldwide. 🔰 Whether you are exploring trends in biological research, conducting surveys, or analyzing experimental data, SPSS empowers you to transform raw data into actionable insights. 🔰 In this comprehensive course, you will learn the fundamentals of SPSS in a simple, step-by-step manner, tailored for individuals from any background. 🔰 By the end, you will be equipped with the essential skills to analyze, interpret, and present data confidently, making this course an invaluable tool for your research or professional journey. COURSE OUTLINE ✳️ Module 1: Introduction to SPSS ◘ Lesson 1.1: What is SPSS and Why Should Biologists Use It? • Overview of SPSS • Key features and benefits for biological research ◘ Lesson 1.2: Installing and Navigating SPSS • Installation guide • Understanding the SPSS interface • Data entry basics: How to input data manually ◘ Lesson 1.3: Data Types and Variables • Understanding variable types (Nominal, Ordinal, Scale) • Setting up variables in SPSS ✳️ Module 2: Data Management and Organization ◘ Lesson 2.1: Importing and Exporting Data • Importing Excel, CSV, and other formats into SPSS • Exporting data and results from SPSS ◘ Lesson 2.2: Data Cleaning and Preparation • Handling missing values • Sorting and filtering data • Recode and compute variables ◘ Lesson 2.3: Data Transformation for Biological Analysis • Creating new variables based on existing data • Using conditional statements ✳️ Module 3: Descriptive Statistics ◘ Lesson 3.1: Basic Descriptive Statistics • Mean, Median, Mode, Range, and Standard Deviation • Using SPSS to calculate and interpret descriptive statistics ◘ Lesson 3.2: Visualizing Data • Creating histograms, bar charts, and pie charts • Using boxplots and scatterplots for biological data ✳️ Module 4: Hypothesis Testing in SPSS ◘ Lesson 4.1: Introduction to Hypothesis Testing • Understanding p-values, significance, and confidence intervals ◘ Lesson 4.2: T-Tests and ANOVA • Independent and Paired Sample T-Tests • One-way and Two-way ANOVA ◘ Lesson 4.3: Non-Parametric Tests • Mann-Whitney U Test, Kruskal-Wallis Test • When to use non-parametric tests in biology ✳️ Module 5: Correlation and Regression Analysis ◘ Lesson 5.1: Correlation Analysis • Pearson’s and Spearman’s correlation • Interpreting correlation coefficients in biological research ◘ Lesson 5.2: Linear Regression • Simple linear regression • Multiple linear regression: When and how to use it • Assumptions of linear regression ◘ Lesson 5.3: Logistic Regression • Understanding binary outcomes • Conducting and interpreting logistic regression ✳️ Module 6: Advanced Statistical Techniques ◘ Lesson 6.1: Factor Analysis • Overview and applications in biology • Conducting factor analysis in SPSS ◘ Lesson 6.2: Cluster Analysis • Hierarchical and K-means clustering • Applications in biological data sets ◘ Lesson 6.3: Multivariate Analysis of Variance (MANOVA) • When to use MANOVA in biological research • Conducting and interpreting MANOVA ✳️ Module 7: Reporting and Interpreting Results ◘ Lesson 7.1: Generating and Interpreting Output • Understanding SPSS output tables and charts • Reporting statistical findings in biological research ◘ Lesson 7.2: Writing a Statistical Report • Structuring a scientific report with statistical results • Communicating complex data simply and effectively ✳️ Module 8: SPSS for Biological Research Projects ◘ Lesson 8.1: Designing a Research Project Using SPSS • Setting research objectives and data collection strategies • Using SPSS for hypothesis testing and analysis ◘ Lesson 8.2: Case Studies in Biology • Real-world biological examples using SPSS (e.g., population genetics, ecology, microbiology) • Hands-on project using SPSS to analyze biological data ✳️ Final Project ◘ Lesson 9.1: Capstone Project • Students will analyze a biological dataset using the techniques learned in the course • Submission of a final report including data analysis, results, and conclusions
Database · Biology · Numerical analysis
Noshima - Prilly, Switzerland28Fr
28FrFoundation of Data Science -- For Beginners students.
Trusted teacher: Hi Students, Foundation of Data Science is for complete Beginners. This course is In-depth knowledge and hands-on tools to use and work with different kinds of data. Gaining practical experience across the data science pipeline by acquiring proficiency in the data science coding language R. Foundations of Data Science is for those who work with both structured – spreadsheets and data tables – and unstructured data and who need to understand how to automate data to gain insights, contribute to strategic discussions and make data-driven decisions. With my Course you’ll gain a broad understanding of the tools and techniques used to work with data. 1. What is Data: Learn about data types, structures and frameworks 2. Interacting with Data: Understanding how data is used and how to use data 3. Where Data comes from: The processes for acquiring and sharing Data 4. Storing & Structuring Data: Working with different types of databases and data sets 5. Cleaning Data: Working with messy or incomplete data sets 6. Analyzing Data: Gaining insights with data science techniques and tools 7. Visualizing Data: Best practices and tools for data visualization and presentation 8. Machine Learning & Artificial Intelligence: How data is used by machines 9. Working as a Data Scientist: What do data scientists really do? 10. Data & Society: Ethics and the uses of data in society
Database · Computer science
Olga - London, United Kingdom19Fr
19FrMaster Databases through Practical Projects – Experienced Tutor with 16 Years of Teaching Experience
Trusted teacher: As a graduate of the University of Westminster, I began my programming journey by applying my skills to data management and analysis in various projects. Over the past 16 years, I have been providing private lessons in computer science, with a strong focus on teaching databases to students and professionals alike. Whether you're a beginner learning about SQL or a more advanced learner seeking to optimize database performance, I offer tailored lessons designed to help you understand and apply database concepts in real-world scenarios. What I Offer: Learn databases through hands-on projects: You'll develop a deep understanding of relational databases (SQL), non-relational databases (NoSQL), and data modeling by working on real-world projects. Customized lessons: Each lesson is adapted to your learning style and current level, whether you’re just starting or need to refine your skills. Portfolio and project support: Need help building a database project for your portfolio or finalizing a boot camp assignment? I provide guidance to ensure your project is both functional and impressive. Database optimization: Learn how to optimize queries, structure your data efficiently, and improve database performance. Study materials and exercises: Before each session, you'll receive all necessary materials to prepare for the next lesson, followed by homework assignments to reinforce the concepts covered. Flexible online lessons: Access lessons from anywhere, on a schedule that suits you. I currently work as a freelancer with companies, start-ups, and schools, offering online lessons and project support in database management. Ready to Dive into Databases? If you're interested in mastering databases through practical, real-world projects, or if you have any questions, don't hesitate to reach out. Let's build your database skills together! This ad emphasizes practical learning in databases, tailored for students and professionals looking to enhance their skills through real-world projects. Let me know if you'd like any further refinements!
Database · Numerical analysis · Networking
Nicolas - Milan, Italy16Fr
16FrPython & Pandas: A Programming Guide to the data galaxy
Trusted teacher: Welcome to the basics of Data Science with Python applied to real cases! In this course, we will cover the fundamental concepts and techniques of data science using the Python programming language. The course will begin with an overview of the key concepts in data science, including data types, data structures, and statistical analysis. We will then move on to cover the basics of Python programming, including variables, data types, loops, functions, and classes. Once we have covered the fundamentals of Python programming, we will dive into the world of data analysis and manipulation with the Pandas library. You will learn how to import, clean, and transform data using Pandas and how to perform basic statistical analysis on data. Next, we will explore data visualization with Matplotlib and Seaborn libraries. You will learn how to create different types of plots and charts to visualize data and gain insights from it. In the second half of the course, we will apply what we have learned to real-world data science problems. You will work on projects that involve cleaning and analyzing real datasets, such as census data, financial data, or climate data. Throughout the course, you will have access to a variety of resources, including lectures, readings, exercises, and quizzes. You will also have the opportunity to collaborate with other students and receive feedback from your instructors. By the end of this course, you will have a solid understanding of the basics of data science with Python and how to apply it to real-world problems. You will be able to use Python to perform data analysis, create visualizations, and draw insights from data.
Python · Database · Computer science
Mazuba - London, United Kingdom18Fr
18FrComplete IGCSE Computer Science & ICT: From Basics to Advanced Programming
This course is tailored for IGCSE Computer Science and ICT students who want to master all the key topics in their curriculum. With a focus on practical programming skills, theoretical computer science concepts, and hands-on problem-solving, this class will guide you through the essential topics covered in the IGCSE syllabus. Learn the fundamentals of data representation, hardware and software, databases, and Boolean logic, and then build on those concepts with programming and algorithm design. Gain a deep understanding of the inner workings of computers and their applications in real-world scenarios. By the end of this course, you will have a solid understanding of key computer science concepts and the practical skills to succeed in your IGCSE exams and beyond. What You’ll Learn: Data Representation: Learn how computers represent and process data, including binary, hexadecimal, and conversions between number systems. Data Transmission: Understand how data is transmitted over networks, focusing on packet switching, USB, and data packets. Hardware & Software: Gain a foundational understanding of computer hardware components and the role of software in running programs. Boolean Logic: Explore logic gates, truth tables, and Boolean expressions, and apply them to solve complex problems. Algorithm Design: Learn to design algorithms and solve problems using effective computational methods. Programming: Master the basics of programming with hands-on coding in Python, C++, and C#. Databases: Understand database structures, SQL queries, and primary keys. Build single-table databases and learn data management techniques.
Computer programming · Computer science · Database
Yedija Messa Sirao - Manchester, United Kingdom24Fr
24FrLearning and Mastering Artificial Intelligence and Data Science: From Fundamentals to Advanced Applications for Real-World Success
Welcome to "AI and Data Science" – a comprehensive, customizable course designed for learners at any level, from beginners to advanced professionals. Whether you're just starting your journey into the world of artificial intelligence and data science or looking to enhance your existing skills, this course will provide you with the knowledge and practical tools you need to excel. What You'll Learn: Fundamentals of Data Science: Understanding data collection, cleaning, and preprocessing; learning to analyze and visualize data using tools like Python, Pandas, and Matplotlib. Introduction to AI and Machine Learning: Explore basic concepts of AI, supervised and unsupervised learning, and popular algorithms (e.g., regression, classification, clustering) with hands-on coding exercises. Advanced AI Techniques: Delve into deep learning, neural networks, and advanced algorithms like decision trees, SVMs, and reinforcement learning. Practical Projects: Work on real-world projects such as predictive modeling, sentiment analysis, and building AI applications using Python libraries like TensorFlow and PyTorch. Storytelling with Data: Develop skills to communicate insights effectively, using data visualization tools and storytelling techniques to create compelling narratives from data. Database Management: Learn how to work with databases (SQL and NoSQL) and manage data efficiently for large-scale applications. What to Prepare: Basic Computer Skills: No prior programming experience is required for beginners, but familiarity with basic computer operations is recommended. Software Setup: Students will need to install software like Python, Jupyter Notebooks, and data science libraries (instructions will be provided during the course). Curiosity and Dedication: This course encourages a hands-on approach, so students should come ready to code, experiment, and learn through practical examples. What to Expect: Customized Learning Experience: Lessons are tailored based on the student’s level and goals, ensuring a personalized approach that aligns with your learning pace and interests. Supportive Environment: Receive one-on-one mentoring and support to help you overcome challenges and master complex topics. Skills You Can Apply Immediately: Gain practical, job-ready skills that are in high demand across industries, including AI, finance, marketing, and tech.
Numerical analysis · Database · Computer science
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Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.6 out of 5 based on 8 reviews.

Microsoft Excel for any application [ITA/ENG] 100% Practical (Bologna)
Enrico
Enrico is a very talented, dedicated and calm teacher - he can explain very well, is extremely patient and makes independent examples, which are very helpful and practical. I learn a lot about Excel applications in Finance I am very, very happy that I found him through this platform. His teaching methods are great, very clear and concise. Therefore, I can only recommend Enrico, I am sure you won't be diasappointed!
Review by JUSTINE
Office automation courses with Microsoft Office or Open / LibreOffice (Saint-Gilles)
Corinne
Excellente prestation. Grande disponibilité.
Review by ALAIN
Remote Odoo Functional and Technical ERP Training
Alaa
Good
Review by WAFA

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