Strategic Leadership Mastery: Navigating Change, Driving Innovation, and Leading with Agility
12/11/2024Data Governance, Compliance, and Protection Management
12/11/2024Introduction to Data Science
£4,000.00
Overview:
This introductory program provides a comprehensive foundation in data science, equipping participants with essential skills in data analysis, machine learning, and visualization. Through hands-on exercises and real-world applications, participants will gain practical experience with tools such as Python, Jupyter Notebooks, and data visualization software, along with an understanding of cloud storage and data ethics. By the end of the course, attendees will have a clear understanding of the data science workflow and the ability to apply data science techniques to solve business problems.
Program Objectives:
By the end of this course, participants will be able to:
- Understand the basics and significance of data science in various industries.
- Familiarize themselves with essential data science tools, including Python, Jupyter Notebooks, and visualization software.
- Apply data science techniques to extract valuable insights from data and communicate findings effectively.
- Develop foundational skills in data engineering, including data storage and retrieval.
- Address ethical considerations and data governance in data science workflows.
Target Audience:
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- Aspiring Data Scientists and Analysts beginning their data science journey.
- IT Professionals and Software Engineers looking to broaden their skill sets.
- Business Managers and Analysts seeking to leverage data for decision-making.
- Marketing Professionals and Compliance Officers interested in data-driven insights.
- Anyone looking to understand the foundational principles and applications of data science.
Program Outline:
Day 1: Introduction to Data Science and Data Engineering Fundamentals
- Overview of Data Science – Definition, Importance, and Applications.
- Key Roles in a Data Science Team – Data Scientists, Data Engineers, and Analysts.
- Basics of Data Types and Data Structures.
- Introduction to Data Storage and Cloud Solutions (e.g., Google BigQuery, AWS).
- Hands-On Activity: Setting Up Jupyter Notebooks and Working with Simple Data Sets.
- Reflection & Review: Group Discussion on the Importance of Data in Modern Business.
Day 2: Data Analysis and Preparation
- Introduction to Statistics – Descriptive and Inferential Statistics for Data Science.
- Data Cleaning and Transformation – Handling Missing Values, Outliers, and Data Wrangling.
- Exploratory Data Analysis (EDA) – Identifying Patterns and Insights.
- Using Python Libraries like Pandas for Data Manipulation.
- Hands-On Activity: Performing EDA and Data Cleaning on a Sample Data Set in Jupyter Notebook.
- Reflection & Review: Discussing Challenges in Data Preparation.
Day 3: Data Visualization and Storytelling
- Importance of Data Visualization in Communicating Insights.
- Overview of Visualization Tools – Power BI, Tableau, and Matplotlib.
- Creating Visualizations – Bar Charts, Line Graphs, and Heat Maps.
- Storytelling with Data – Best Practices for Presenting Data Insights.
- Workshop: Building an Interactive Dashboard Using Power BI or Tableau.
- Reflection & Review: Discussing the Role of Visualization in Decision-Making.
Day 4: Introduction to Machine Learning
- Basics of Machine Learning – Definition and Applications in Data Science.
- Types of Machine Learning – Supervised, Unsupervised, and Reinforcement Learning.
- Key Algorithms – Regression, Classification, and Clustering.
- Model Training and Validation Techniques.
- Hands-On Activity: Building a Simple Machine Learning Model Using Scikit-Learn.
- Reflection & Review: Discussing the Challenges and Potential of Machine Learning.
Day 5: Ethics, Future Trends, and Final Project
- Ethical Considerations in Data Science – Privacy, Bias, and Fairness.
- Data Governance – Ensuring Compliance with Privacy Standards.
- Emerging Trends in Data Science – AI, Big Data, and IoT.
- Career Paths in Data Science – Data Engineering, ML Engineering, and Analytics.
- Capstone Project: Analyzing a Real-World Data Set – Applying Data Cleaning, EDA, Visualization, and Modeling.
- Reflection & Review: Project Presentations, Peer Feedback, and Discussion on Future Careers.