Certification Overview
This certification is a comprehensive program designed to transform aspiring data enthusiasts into skilled data science professionals. The curriculum provides a robust foundation in statistics and programming, a deep dive into the data science lifecycle, and hands-on experience with both traditional and cutting-edge machine learning techniques. Participants will learn how to clean and analyze data, build predictive models, and, crucially, communicate their findings through powerful data storytelling. The capstone project, focused on employee attrition prediction, will provide a practical, portfolio-worthy example of applying these skills to a real-world business problem.
Module 1: Foundations of Data Science
- Introduction to Data Science: A foundational overview of the field, its importance in modern business, and the key roles and responsibilities of a data scientist.
- Data Science Life Cycle: A step-by-step breakdown of the process, from defining a business problem to deploying a solution.
- Applications of Data Science: Case studies and examples of how data science is used across various industries, such as healthcare, finance, and marketing.
Module 2: Foundations of Statistics
- Basic Concepts of Statistics: A review of essential statistical concepts, including mean, median, mode, standard deviation, and variance.
- Probability Theory: An introduction to probability, distributions, and their role in making informed decisions from data.
- Statistical Inference: Learn to draw conclusions and make predictions about a population based on sample data.
Module 3: Data Sources and Types
- Types of Data: A classification of data, including structured, unstructured, qualitative, and quantitative data.
- Data Sources: Explore the different places data comes from, such as databases, APIs, and web scraping.
- Data Storage Technologies: An overview of various data storage solutions, from traditional databases to data lakes and data warehouses.
Module 4: Programming Skills for Data Science
- Python for Data Science: A deep dive into Python, the leading language for data science, covering essential libraries like NumPy, Pandas, and Matplotlib.
- R for Data Science: An introduction to R, another powerful language for statistical analysis and data visualization.
Module 5: Data Wrangling and Preprocessing
- Data Imputation Techniques: Learn methods for handling missing values in a dataset.
- Handling Outliers: Strategies for identifying and managing extreme values that can skew analysis.
- Data Transformation: Techniques for normalizing and scaling data to prepare it for machine learning models.
Module 6: Exploratory Data Analysis (EDA)
- Introduction to EDA: The art of exploring and visualizing data to uncover patterns, anomalies, and insights before formal modeling.
- Data Visualization: Master the use of tools like Matplotlib and Seaborn to create compelling charts and graphs.
Module 7: Generative AI Tools for Deriving Insights
- Introduction to Generative AI: Understand what generative AI is and how it can be used to augment the data science workflow.
- Applications of Generative AI: Learn how to use large language models (LLMs) and other generative tools for tasks like code generation, data summarization, and crafting initial analysis reports.
Module 8: Machine Learning
- Supervised Learning Algorithms: A practical guide to supervised learning, including an in-depth look at algorithms like Linear Regression, Logistic Regression, and Decision Trees.
- Unsupervised Learning: An exploration of unsupervised learning, where models find patterns in unlabeled data.
- Clustering Algorithms: Hands-on practice with algorithms like K-Means Clustering to group similar data points.
- Association Rule Learning: An introduction to algorithms like Apriori, which are used to discover interesting relationships between variables in large datasets.
Module 9: Advanced Machine Learning
- Ensemble Learning: Understand how to combine multiple models to create more powerful and accurate predictions with techniques like Random Forest and Gradient Boosting.
- Dimensionality Reduction: Techniques for reducing the number of features in a dataset to improve model performance and interpretability.
- Optimization Techniques: An overview of methods used to fine-tune machine learning models for optimal performance.
Module 10: Data-Driven Decision-Making
- Data-Driven Decision Making: A framework for using data and insights to make strategic business decisions.
- Open Source Tools: An overview of the most popular open-source tools in the data science ecosystem.
- Sales Dataset Insight Generation: A practical exercise in generating actionable insights from a sample sales dataset.
Module 11: Data Storytelling
- Power of Data Storytelling: Learn how to translate complex data analysis into a clear and compelling narrative that resonates with non-technical stakeholders.
- Identifying Business Use Cases: The process of connecting data insights to real-world business problems and opportunities.
- Crafting Narratives: A guide to structuring a data story, from the initial problem statement to the final call to action.
- Data Visualization for Impact: Master advanced data visualization techniques to create visuals that effectively support your narrative.
Module 12: Capstone Project - Employee Attrition Prediction
- Project Setup and Problem Statement: Define the business problem of predicting employee attrition and its importance for human resources.
- Data Collection and Prep: Apply skills learned in Modules 3, 4, and 5 to prepare a dataset for analysis.
- Analysis and Modeling: Use exploratory data analysis (Module 6) and various machine learning models (Modules 8 and 9) to identify factors contributing to attrition and build a predictive model.
- Storytelling and Final Presentation: Craft a comprehensive data story and a final presentation for stakeholders, outlining your findings, the model's accuracy, and strategic recommendations for employee retention.
Optional Module: AI Agents for Data Analysis
- Understanding AI Agents: A deep dive into the concept of autonomous AI systems that can perform complex, multi-step data analysis tasks.
- Case Studies: Explore real-world examples of AI agents being used for tasks like automated data cleaning, insight generation, and report creation.
- Hands-On Practice: A workshop-style session to experiment with building and deploying simple AI agents for specific data analysis workflows.
