Data Science Curriculm

Introduction to Data Science

• Prelude: Briefly introduce the role and importance of data science in today's world.

• The Problem Landscape: Discuss the types of problems data scientists solve.

• Defining Data Science: Define data science and its scope.

• Demystifying Data Science, Decision Science, AI, ML, and DL: Clarify the differences and relationships between data science, AI, machine learning, and deep learning.

• Overview of Data Scientist's Toolbox: Introduce the main tools and technologies data scientists use (Python, R, SQL, etc.).

Data Science Tool Box

• Python Quick Recap: Introduce Python basics, and discuss differences between Python 2.7.x and 3.x. Python 3.x is generally recommended.

• Installation and Setup: Walkthrough the setup for Python and necessary libraries (like Anaconda).

• Data Types, Functions, and Important Packages: Introduce data structures like lists, dictionaries, NumPy, pandas, and key libraries like NumPy, pandas, and Matplotlib.

• Data Manipulation & Engineering: Cover data preprocessing steps like cleaning, transformation, feature engineering, and handling missing data.

• Data Visualization: Discuss basic charting libraries (Matplotlib, Seaborn).

Probability and Statistics

• Theoretical Foundations of Statistics: Basics of statistics, focusing on its importance in data science.

• Describing Data, Populations, and Sampling: Focus on descriptive statistics and sampling techniques.

• Analyzing Data Distribution and Measures of Central Tendency: Mean, median, mode, and standard deviation.

• Probability Distributions: Gaussian, Bernoulli, Binomial, and Poisson distributions.

• Statistical Tests: Z-test, t-test, chi-square test, Type 1/Type 2 errors.

• Analyzing Correlations: Pearson and Spearman’s rank.

• Probability Rules: Addition, multiplication, permutations, and combinations.

Numpy

• Introduction to Numpy: Basics of NumPy arrays and operations.

• Random Data Generation: Discuss random number generation and seeding.

• Numpy Array Operations: Indexing, slicing, and mathematical operations with NumPy arrays.

Pandas

• Importing Datasets: Loading data from CSV, Excel, and databases.

• Data Wrangling: Cleaning and reshaping data (drop, fill, merge, join).

• Exploratory Data Analysis (EDA) and Model Development: Basic EDA steps like summary statistics, visualizations, and correlation analysis.

SQL for Data Science

• Introduction to SQL: Basics of querying relational databases.

• SQL Queries: SELECT statements, filtering, sorting, etc.

• Joins and Subqueries: INNER, LEFT, RIGHT joins and subqueries.

• Aggregation and Filtering: GROUP BY, HAVING, aggregate functions.

• Working with Databases: Introduction to relational databases and basic interactions.

Scipy and Seaborn

• Scipy Introduction: Discuss the scientific computing functionalities of SciPy.

• Numerical Computations: Handling advanced mathematical and statistical functions.

• Exploratory Data Analysis (EDA): Using Seaborn for data visualization.

• Model Generation: Using SciPy for solving optimization and other mathematical problems.

Plotting, Charting & Data Visualization

• Information Visualization Principles: Importance of effective communication using charts.

• Basic Charting and Applied Visualizations: Tools for making charts with Matplotlib and Seaborn.

• AI Tools for Data Science: Using AI-powered notebooks for enhancing visualizations.

Tableau Basics

• Introduction to Tableau: Basics of the Tableau interface.

• Data Import and Visualization: Load data and build basic charts.

• Creating Interactive Visualizations: Creating dashboards and graphs with filters and interactive features.

Exploratory Data Analysis (EDA) and Hypothesis Testing

• Machine Learning Methodology Overview: Discuss how to approach machine learning problems.

• Feature Engineering: Importance of transforming raw data into usable features.

• Statistical Inference and Probability Distributions: Use of hypothesis testing to draw conclusions.

• Hypothesis Testing: Applying tests like t-tests and chi-squared tests.

• AI Tool: Pandas Profiling: Automatically generate EDA reports.

Machine Learning Introduction

• Core Concepts of ML: Introduction to supervised vs unsupervised learning.

• Clustering, Classification, and Regression: Discuss key tasks in machine learning.

• Supervised vs Unsupervised Learning: Define and contrast these two major ML paradigms.

Supervised Learning

• Linear Regression: Best fit line and prediction techniques.

• Logistic Regression: Introduction to classification and evaluation metrics.

• Support Vector Machine (SVM): Concepts of margin, hyperplanes, and kernels.

• K-Nearest Neighbors (KNN): KNN algorithm, distance metrics, and evaluation.

AutoML for Model Building

• AutoML: Automating the model building and selection process with tools like TPOT and H2O.ai.

Unsupervised Machine Learning

• Clustering Overview: Introduction to clustering methods like K-Means.

• K-Means Algorithm: Theory and implementation of K-Means.

• Principal Component Analysis (PCA): Dimensionality reduction with PCA.

Text Mining in Python

• Natural Language Processing (NLP): Introduction to working with text data.

• Text Preprocessing: Tokenization, stop word removal, and stemming.

• Regular Expressions (Regex): Text cleaning and extraction.

• Text Classification: Classifying text using machine learning models.

Prompt Engineering for Data Science

• Introduction to Prompt Engineering: Effective communication with large language models.

• Iterative Improvement: Improving prompts for better results.

• Applications of LLM in Data Science: Using AI tools like GPT for data-related tasks.

ML Web App Development with Streamlit

• Introduction to Streamlit: Building interactive web apps for machine learning.

• Setting up and Deploying: Creating an interactive web interface for models.

• Interactive Visualizations: Integrating plots and models into the app.

FastAPI and ML Deployment

• Introduction to FastAPI: Building APIs for machine learning models.

• Asynchronous Processing: Handling high-load scenarios.

• Deployment & Scaling: Best practices for deploying machine learning models in production.

Projects

• Exploratory Data Analysis: Perform EDA on a real-world dataset.

• Regression Analysis: Build and evaluate a regression model.

• Sentiment Analysis: Implement a text classification model.

• Classification-based Projects: Apply machine learning algorithms like Logistic Regression, SVM, etc.

• Clustering Projects: Work with clustering algorithms like K-Means and hierarchical clustering.

• Real-time ML Model Deployment: Deploy an ML model for real-time predictions.