Level 1
Data Science Foundations
Registrations Open !!
Registrations Open !!
Data Science is an interdisciplinary field that focuses on extracting meaningful insights and knowledge from large volumes of structured and unstructured data. It combines concepts from statistics, mathematics, programming, and domain knowledge to analyze data and support data-driven decision making. The subject Data Science Foundations introduces students to the fundamental concepts, tools, and techniques used in the field of data science. It provides an understanding of data collection, data preprocessing, exploratory data analysis, visualization, and basic predictive modeling.
| Day | Topic | Sub-Topic No. | Sub-Topic Title | Detailed Contents |
| Day 1 | Introduction to Data Science and Python | 1.1 | What is Data Science? | Definition, purpose, real-world examples |
| 1.2 | Components of a Data Science | Difference between:Data Science vs AI vs ML vs Analytics | ||
| 1.3 | Real-world applications | Healthcare, Finance, Education etc | ||
| 1.4 | Data Science vs AI | Data Science vs AI vs ML vs Generative AI. How AI systems learn from data | ||
| 1.5 | Introduction to Python | Bandwidth, latency, throughput | ||
| Day 2 |
Role of AI in Data Handling Introduction to Conditional statements in Python |
2.1 | Importance of Data Quality in AI |
Why AI fails with bad data Role of data in training AI models |
| 2.2 | Bias in Datasets and Its Impact on AI | Bias introduced through datasets, | ||
| 2.3 | Data Collection and Data Preprocessing | Data collection and preprocessing, Secondary Data Collection Methods, | ||
| 2.4 | Control Flow and Decision Making in Python | CONTROL FLOW, LOOPS, Conditionals: Boolean values and operators, | ||
| Day 3 | Handling Data & Conditional statements in Python | 3.1 | Importance of Data Cleaning and Data Integration | Data cleaning,Data integration |
| 3.2 | Feature Engineering as a Way to Teach AI Models | Data Methods of Data Cleaning, Garbage in → Garbage out in AI Feature engineering as “teaching” the AI Human judgment vs automated preprocessing | ||
| 3.3 | Use of Conditionals and Iteration in Python Programs | conditional (if) alternative (if-else) chained conditional (if-elif-else) Iteration: while, for | ||
| Day 4 | Data Reduction, Model Interpretation, Data Visualization, and Python Functions in AI | 4.1 | Data Reduction Improves Efficiency | Data reduction: Various types of Data Reduction. Learning Models Visualization as explainable AI |
| 4.2 | Storytelling with Data Supports Better Decision Making | AI decisions must be interpretable Storytelling with data , | ||
| 4.3 | Functions and Control Statements Improve Program Structure | Break,continue. FUNCTIONS Implementing these Python components with examples. | ||
| Day 5 | Functions in Python and Fundamentals of Machine Learning in AI | 5.1 | Functions and Program Flow in Python | Functions ---- function and its use , flow of execution, parameters and arguments. |
| 5.2 | Role of Machine Learning as the Core of AI | ML as the core of AI Supervised learning as “learning with a teacher” | ||
| 5.3 | Model Evaluation and Fairness in AI Systems | Model evaluation & fairness | ||
| Day 6 | Case Study / Hands-on Practice /Simulations | |||
| Know your Mentor | Contact Number | Email Id | Teaching Experience (in Yrs.) |
| Dr.Preet Kamal | 8872204800 | preet.e15857@cumail.in | 20 Years |
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