Data Science Methodology

🎓 Course Title: Data Science Methodology

👩‍🏫 Course Instructor: Neena Sathi

Headline: Understand Steps and Tasks Needed for Designing and Building a Data Driven AI Engagement

Course Description:

Data Science has evolved from the early days of Business Intelligence in the 1990s, revolutionizing how we leverage Artificial Intelligence to drive decisions and actions. As we transition from traditional BI methodologies to AI deployments, it's crucial to understand how these methodologies are adapting to handle the complexities of model deployment, machine learning, and data science.

Today's Data Science landscape is markedly different from its predecessors. It grapples with three major challenges:

1. Handling Big Data: With the advent of massive data volumes, traditional sampling methods and universal population measures like census projections are often rendered obsolete. The focus now shifts to dealing with significant samples where bias and outliers can significantly impact analysis.

2. High Velocity Data: Modern data science must cater to the high velocity at which data is generated and consumed. Real-time analytics and scoring engines enable immediate insights, enabling services like real-time customer support on websites or products.

3. High Variety Data: A substantial portion of today's data is unstructured, such as speech, text, or videos. The ability to interpret and extract meaningful information from this variety is a cornerstone of modern data science.

In this course, we will explore a comprehensive 7-step methodology for conducting data science/AI-driven engagements:

Step 1: Understand Use Case - We'll delve into illustrative examples and case studies to uncover strategies for defining use cases and data science objectives.

Step 2: Understand Data - This step involves defining the characteristics of big data, selecting appropriate data sources, and understanding the data science perspective on data selection, cleaning, and construction.

Step 3: Prepare Data - Learn how to select, clean, and construct big data for model development, considering analytics or AI techniques.

Step 4: Develop Model - Discover the process of ingesting structured and unstructured data from various sources to build models that provide data insights using AI and Analytics.

Step 5: Evaluate Model - Gain insights on how to engage users, evaluate model decisions, and understand the measurements needed for your models.

Step 6: Deploy Model - Explore strategies for deploying AI models, incorporating learning from production use to enhance your model.

Step 7: Optimize Model - Fine-tune your model and optimize its performance over time using feedback from real-world use, while ensuring that the process does not lead to biases or sabotage.

For those interested in practical application of these concepts using Python, we offer an additional course titled "Data Science in Action using Python." This course is designed to provide hands-on experience and deepen your understanding of data science methodologies through programming.

Join us on this journey to master the art of Data Science Methodology and harness the power of AI for impactful engagements! 💻✨