Dispute Resolution and Arbitration in Contracts
26/10/2024Data Management and Visualization Mastery
26/10/2024Concise Data Science Track
£4,000.00
Overview:
The Concise Data Science Track is a hands-on, 5-day intensive course designed to introduce participants to essential data science concepts, tools, and techniques. This program provides a practical learning experience in data handling, cleaning, visualization, and analysis. The course also includes an introduction to artificial intelligence (AI) and its applications in data science, equipping participants with knowledge of real-world AI tools and techniques. Through interactive sessions, hands-on coding, and a capstone project, participants will gain the confidence and skills to manage data-driven projects in their own work environments.
Program Objectives:
By the end of this course, participants will be able to:
- Understand fundamental data science concepts and their relevance across various industries.
- Set up a functional data science environment with tools like Python, Jupyter Notebook, Pandas, and NumPy.
- Perform data handling and processing tasks, including data collection, cleaning, and manipulation.
- Utilize data visualization tools to create insightful visual reports that drive data-based decisions.
- Apply basic AI models to enhance data analysis, gaining exposure to technologies like neural networks, NLP, and computer vision.
- Design and execute a data science project, from data collection to analysis and presentation, applying their new skills to a real-world problem.
- Continue advancing their skills in data science with suggested resources and communities.
Target Audience:
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- Business Professionals and Analysts interested in using data science to enhance business insights.
- IT and Data Management Professionals seeking to expand their technical skill set in data processing and analysis.
- Aspiring Data Scientists who want an accelerated introduction to core data science tools and practices.
- Project Managers involved in data-driven projects who wish to understand data workflows and project planning.
- Any individual looking to integrate data science and AI concepts into their daily tasks for more data-informed decision-making.
Program Outline:
Day 1: Introduction to Data Science & Tools
- Interactive Overview of Data Science: Explore key concepts, real-world applications, and industry trends through case studies and examples. Emphasis on how data science is transforming various industries.
- Introduction to Python for Data Science: Hands-on setup with guided exercises. Participants will install and configure Python, exploring its role in data science projects.
- Environment Setup: Practical session on installing and setting up essential tools (Jupyter Notebook, Pandas, NumPy). Discussion on best practices for maintaining an efficient data science workflow.
- Hands-on Coding Session: Interactive coding exercises focused on basic Python programming for data manipulation, including working with datasets, reading data files, and simple data operations. Participants will start working on a mini-project involving real data to ensure immediate application of skills.
Day 2: Data Handling & Processing
- Data Collection Techniques: Interactive session on gathering data from various sources, including APIs, CSV files, and web scraping. Participants will learn how to access and import data relevant to their work environment.
- Data Cleaning Techniques: Practical session focused on common data cleaning challenges like handling missing values, duplicates, and outliers. Participants will work on exercises that simulate real-world data issues.
- Advanced Data Manipulation: Deep dive into Pandas for sorting, filtering, and aggregating data. Interactive group activity to clean and prepare a messy dataset for analysis, reinforcing learning through practice.
- Hands-on Exercise: Participants will perform data wrangling on a new dataset, practicing their skills in cleaning and manipulation. They will work towards building a clean, analyzable dataset for future use.
Day 3: Data Visualization & Analysis
- Importance of Data Visualization: Explore why visualization is crucial in data science. Participants will review compelling visualizations and discuss their impact on decision-making.
- Using Visualization Tools (Matplotlib, Seaborn): Hands-on session where participants create their visualizations, including bar charts, histograms, and scatter plots. Focus on the story behind the data.
- Applying Data Analysis Techniques: Participants will learn to perform descriptive statistics and identify patterns. They will engage in exercises designed to turn raw data into insightful reports.
- Creating Visual Reports: Interactive session where participants create visual dashboards using their analysis, focusing on presenting data clearly and effectively.
Day 4: Introduction to AI in Data Science
- AI Concepts in Data Science: Overview of AI technologies such as neural networks, NLP, and computer vision. Discussion on how these technologies are reshaping data analysis and predictions.
- Practical Applications of AI: Participants explore how AI can enhance data analysis tasks, including predictive analytics, data classification, and anomaly detection. Real-world examples will be used to highlight AI's value.
- Hands-on with AI Tools: Participants will be introduced to popular AI libraries like TensorFlow and Scikit-Learn. They will work on integrating basic AI models with their data analysis workflows.
- Hands-on Exercise: A practical session where participants implement simple AI-driven analysis, such as sentiment analysis or predictive modeling, to see AI's impact on their daily data tasks.
Day 5: Capstone Project Planning
- Project Overview and Planning: Introduction to the capstone project where participants apply all the skills they’ve learned. Guidance on selecting data, defining project objectives, and structuring their analysis.
- Best Practices in Project Planning: Focus on organizing a data science project from start to finish, including data collection, cleaning, analysis, and presentation. Participants will outline their project steps.
- Project Execution and Presentation: Participants work on their projects, applying data manipulation, visualization, and AI techniques. They will present their findings to the group, receiving feedback and suggestions.
- Roadmap for Continued Learning: Discussion on resources and strategies for continued development in data science, including recommended courses, books, and involvement in data science communities (e.g., Kaggle, GitHub). Emphasis on integrating learned skills into their daily work.