Course Overview:

In today’s world, data is at the heart of decision-making. With the rise of AI, machine learning, and automation, the ability to understand and work with data has never been more critical. This 5-day introductory course in Data Science is designed to provide participants with foundational skills in data handling, analysis, and visualization, as well as an introduction to more advanced concepts like machine learning and AI. The course will include practical sessions to help participants build hands-on experience using Python, data manipulation libraries, and data visualization tools.

Introduction:

As businesses, organizations, and governments increasingly rely on data to drive decision-making, the demand for skilled data scientists is rapidly growing. This course serves as an introduction to the diverse field of data science, focusing on providing the essential tools and methodologies needed to begin a career in this high-demand industry. By the end of the course, participants will be equipped to handle data effectively, uncover insights, and make informed decisions using statistical and computational methods.

Data science is not just about analyzing data, but about understanding the challenges, processes, and ethics of working with data in the real world. With an eye on the future, this course is designed to adapt to the evolving trends of data science, including AI, big data, and the ever-growing role of automation in data analysis.

Objectives:

By the end of this course, participants will be able to:

1. Understand the Data Science Landscape:
   • Define data science and its importance in various industries.
   • Identify key areas within data science: data wrangling, analysis, visualization, and machine learning.
2. Master Key Data Science Tools:
   • Get hands-on experience using Python and key libraries such as Pandas, NumPy, and Matplotlib.
   • Perform basic data cleaning, wrangling, and exploration.
3. Data Analysis and Visualization:
   • Learn to analyze data using statistical methods.
   • Create compelling and informative visualizations with tools like Seaborn and Matplotlib.
4. Introduction to Machine Learning:
   • Understand the basics of supervised and unsupervised learning.
   • Implement simple machine learning models using Scikit-learn.
5. Develop a Data Science Project:
   • Plan and execute a data science project from start to finish.
   • Communicate findings effectively to both technical and non-technical stakeholders.
6. Prepare for the Future of Data Science:
   • Understand the impact of AI and automation on data science roles.
   • Gain awareness of ethical issues and challenges in data science, including privacy and bias in algorithms.

Who Should Attend?:

This course is ideal for professionals or students who are new to data science and want to explore its applications and potential career opportunities. Specific audiences include:

1. Aspiring Data Scientists: Those looking to transition into data science from other fields such as business, engineering, or IT.
2. Business Analysts: Professionals who want to enhance their analytical capabilities and apply data-driven decision-making in their organizations.
3. Engineers and Developers: Individuals with a programming background who want to expand their skills into data science.
4. Entrepreneurs and Managers: Business owners or team leaders who wish to better understand data science to improve business strategy and operational efficiency.
5. Students: Recent graduates or students in STEM fields who want to explore data science as a career path.

Course Schedule and Topics:

Day 1: Introduction to Data Science & Data Handling

Objectives: Understand the fundamentals of data science, tools, and data preparation processes.

• Morning Session:
  • Introduction to Data Science: What is data science? The data science workflow.
  • Overview of essential tools in data science (Python, Jupyter Notebooks, Git).
  • Python Basics for Data Science: Variables, data types, loops, and functions.
• Afternoon Session:
  • Working with Data: Introduction to Pandas and NumPy.
  • Data import, export, and cleaning techniques.
  • Hands-on exercises: Loading datasets, inspecting data, and performing basic cleaning.

Day 2: Data Exploration and Visualization

Objectives: Learn how to explore and visualize data to uncover patterns and trends.

• Morning Session:
  • Exploratory Data Analysis (EDA): Key steps in EDA and its importance.
  • Descriptive statistics and summary measures.
  • Hands-on exercises: Applying basic statistics using Pandas and NumPy.
• Afternoon Session:
  • Data Visualization: Introduction to Matplotlib and Seaborn.
  • Creating different types of plots (line, bar, scatter, histograms).
  • Hands-on exercise: Visualizing datasets and identifying insights.

Day 3: Statistical Analysis for Data Science

Objectives: Understand core statistical concepts and their application in data science.

• Morning Session:
  • Intro to Statistics: Probability distributions, hypothesis testing, p-values.
  • Correlation and Regression: Understanding relationships between variables.
  • Hands-on exercises: Implementing basic statistical tests using Python.
• Afternoon Session:
  • Introduction to Inferential Statistics: Sampling methods, confidence intervals.
  • Hands-on exercises: Applying statistical tests and visualizing results.

Day 4: Introduction to Machine Learning

Objectives: Learn basic machine learning concepts and build simple predictive models.

• Morning Session:
  • Supervised Learning: Understanding regression and classification.
  • Introduction to the Scikit-learn library and machine learning workflow.
  • Hands-on exercises: Building a simple regression model.
• Afternoon Session:
  • Unsupervised Learning: Clustering and dimensionality reduction.
  • Hands-on exercise: Implementing a k-means clustering model.
  • Model Evaluation: Accuracy, precision, recall, and overfitting.

Day 5: Building a Data Science Project & Future Trends

Objectives: Learn how to structure a data science project and stay updated with emerging trends.

• Morning Session:
  • End-to-End Data Science Project: Defining the problem, data collection, preprocessing, and modeling.
  • Project Work: Participants start their individual or group projects using the skills learned during the course.
  • Collaboration and Version Control: Introduction to Git for collaboration and code versioning.
• Afternoon Session:
  • Communicating Results: How to present data insights to stakeholders.
  • The Future of Data Science: Trends in AI, automation, big data, and ethics in data science.
  • Course Wrap-up and Q&A.