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Database lessons in Amellagou

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1 database teacher in Amellagou

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1 database teacher in Amellagou

Nikita - RussiaC$33
C$33Computer Programming with Python 3. Basic + Advance levels. From basics to industry develepment and Statistics/Data Analysis with Python.
What am I teaching? Python 3 programming with libraries: Pillow, PyQT, Pygame, SQLite/MongoDB, Flask + SQLAlchemy, NumPy, Pandas, Sklearn HTML and CSS basics due to the Web-programming topic (Flask) SQl/NoSQL (MySQL and PostgreSQL and MongoDB) due to the SQLite/MongoDB library What does it mean? Python is a powerful tool for basic algorithmic tasks, projects with images/sounds/..., web development, data science and so on. I would be glad to learn you in a practical way to use these tools to solve ample different tasks. Information about me: - I am 3rd-year student in ITMO University (Software engineering and programming) - I'm teaching Python for more than 2 years - My students succeeded with their Python exams (100% passed) - I have 5 years experience with Python programming language and get several certificates: Python Basics and Projects (excellent mark) from Yandex Lyceum, Python in Industry programming (excellent mark) from Yandex Lycuem, Advanced Python (Stepik online cource) - English level (B2-C1), Swedish (A2-B1), Russian (native) Teaching features: - All topics I explain clearly with ample comparisons to real life - After every piece of information I'm checking student's understanding (asking similar questions to discuss) - All lessons are split into 2 vital parts: discussing new topics and practicing coding - Basics projects after each module Three ways of learning: - Basic track: Module 1 - Basics: installing/if/else/for/while Module 2 - Data Structures: lists/sets/dicts Module 3 - Functions: def Module 4 - Libraries: math/os/pillow/... Module 5 - OOP: classes/methods - Advanced track: Module 1 - Revicing: data structures and OOP Module 2 - Advanced topics: itherators/try/except Module 3 - PyQT Module 4 - Web-programming Module 5 - API and Applied Projects - Custom track: Possible topics: All in Basic and Advance tracks, Numpy, Pandas, Sklearn - Data Analysis basics What is the format of teaching: Online
Computer programming · Python · Database
Olesia - Paris, FranceC$40
C$40Computer training, Database, Data science, Data Analytics, BI, Big Data
Trusted teacher: I offer courses in data development / database / machine learning / data science (python): I also offer the possibility of helping you with the realization of your academic projects. We support you in the Data development of your business. -1- Databases & Data warehouses (AWS / Google Cloud / Azure Cloud) -2- Machine Learning -3- Deep Learning (tensorflow, pytorch, RNN, CNN, LSTM) -4- Data Processing -5- Machine Learning design and deployment (docker, ...) -6- Data Pipelines -7- Google Sheets with Realtime Pipelines, Macro (VBA) & Database Connection -8- Online dashboards on browsers or on your Excel, Google Sheets (Python, R, Power BI, Tableau, Kibana, etc.) - Our Tech Stack - - Databases: AWS DynamoDB, Amazon Redshift, PostgreSQL, MySQL, multi-cube DBs (EPM / BI platform) - Languages: Python, Spark (Scala, Python, Java), JavaScript, CSS, HTML - Development environment: JSON, SQL, NoSQL, Bash Shell Scripting, Jupyter Notebook, Anaconda, REST API, VSCode, DBeaver, Google services, Platform as a Service (PAAS), Apache Airflow, Serverless Computing, SublimeText - Clouds: Amazon Web Services, Azure Databricks, Google GCP (Google Firebase) - Data Lake AWS / Databricks: EC2 (Linux), IAM, Amazon MWAA (Managed Workflows for Apache Airflow), Lambda, S3, DynamoDB, RedShift; Kibana, Azure Databricks, CloudFormation - Web crawling / Scraping: Python Scrapy - Data streaming: Airflow, Kafka - Data visualization / ETL: Python, Kibana, Tableau, Power BI & DAX, Excel Power Query (and lang.M) - Continuous integration workflows (CI / CD): Docker / Google cloud / Kubernetes; Amazon ECS) - Containerized applications: Docker (Docker container, Docker-compose) - Virtualization technologies: VirtualBox, Vmware - Agile tools: Version control (Git / GitLab), tickets (JIRA), Bitbukets, Trello, Wiki (Confluence), Jetbrains - OS: Linux, Windows
Numerical analysis · Information technology · Database
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William - Accra, GhanaC$22
C$22Fundamentals 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
Muhammad - Istanbul, TurkeyC$7
C$7Teaching for Data Science, Machine learning and Python Programming
Trusted teacher: I will be teaching in Programming from Zero to Hero and will be touching in areas such as Programming fundamentals, Object-oriented programming, Design, and Analysis of Algorithms, Databases, Data lakes/warehouses, AWS CLOUD, Artificial Intelligence, Machine learning, Deep learning, and Natural Language processing. If you don't understand and know about the above topics, the recommended curriculum that I have is Introduction to Computing and Programming This Class is recommended for people above 14 years of age. About Me : Muhammad (Business Intelligence Engineer AMAZON LLC) 4+ years of professional Software Development 4+ years of experience in Data Sciences and Data Engineering 3+ years of experience in Machine Learning 3+ years of experience in Business Intelligence 1+ year of experience in Deep Learning Public Repos: * - AWS Certified Machine Learning Specialist. - 2020 * - AWS Certified Solutions Architect Associate.- 2020 * - AWS Certified Developer Associate - 2019 * - AWS Certified Cloud Practitioner. - 2019 * - Certified Python Specialist - 2018 * - Certified Executive Data Science Specialist. * - Statistics Specialization. * - Deep Learning Specialization. * - Machine learning Specialization. * - Software Code Quality Specialization. * - Reinforcement Learning Specialization. (in progress) 13 successfully delivered client-side projects. 10 projects in Machine Learning and Data Sciences. Accomplishments: The major Interest is in Data Sciences, Machine learning, and Data analysis with python/R/ SQL. Experienced in Data Science (Machine learning) in three major companies. Appropriate Domains with Extensive Knowledge Graph intends to be around - Data Sciences - Cloud Computing - Machine Learning/ Artificial Intelligence - Deep Learning/ Reinforcement Learning - Computer Vision Analysis/ Image Manipulations. - Automation of OS/ Programs - Custom development using Python/C++ as the main Language. - Data Warehousing using Python/SQL. (Snowflake, AWS services) - Serverless Data Lakes and Machine Learning Pipelines - Business Intelligence Pipelines with AWS Been to: United States Turkey Qatar Egypt
Computer programming · Database · Python
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