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For over a decade, our private Database tutors have been helping learners improve and fulfil their ambitions. With one-on-one lessons online, you’ll enjoy high-quality, personalised teaching that’s tailored to your goals, availability, and learning style.

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137 online database teachers

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
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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