Data science with AI involves using artificial intelligence techniques to extract insights, patterns, and knowledge from data. It combines traditional data science methods with advanced AI models to enhance data analysis, prediction, and decision-making processes. Here’s an overview of how AI integrates with data science:
1. Data Collection and Preprocessing
- AI in Data Collection: AI can automate the collection of data from various sources like social media, sensors, and online platforms.
- Data Cleaning:Data Science With AI-driven tools can identify and correct errors, fill in missing data, and standardize datasets, ensuring the quality of the data used for analysis.
2. Exploratory Data Analysis (EDA)
- Pattern Recognition: Data Science With AI can identify complex patterns in large datasets that might not be apparent through traditional methods.
- Automated EDA: Data Science With AI algorithms can automatically generate visualizations and summaries of data, speeding up the EDA process.
3. Feature Engineering
- AI-Driven Feature Selection: Data Science With AI models can automatically select the most relevant features from large datasets, improving model accuracy.
- Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) and t-SNE, often powered by AI, reduce the number of variables under consideration.
4. Model Building
- Machine Learning Models: AI enables the use of advanced machine learning models like neural networks, decision trees, and ensemble methods for making predictions or classifications.
- Deep Learning: For tasks like image and speech recognition, deep learning models (e.g., CNNs, RNNs) are used to handle unstructured data.
5. Model Evaluation
- AI in Model Validation: Data Science With AI can automate hyperparameter tuning, cross-validation, and model evaluation, ensuring the best model performance.
- Bias Detection: Data Science With AI algorithms can identify biases in models and suggest corrections, leading to more fair and accurate outcomes.
6. Deployment and Automation
- Automated Pipelines: Data Science With AI can automate the deployment of models, integrating them into production systems with minimal manual intervention.
- Real-Time Analytics:Data Science With AI models can analyze and respond to data in real-time, enabling dynamic decision-making.
7. Interpretability and Explainability
- Model Interpretability: Data Science With AI tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) help in understanding how AI models make decisions, increasing trust in AI-driven insights.
8. Use Cases
- Healthcare: Predicting patient outcomes, personalizing treatment plans.
- Finance: Fraud detection, algorithmic trading, risk assessment.
- Marketing: Customer segmentation, personalized marketing, sentiment analysis.
- Manufacturing: Predictive maintenance, quality control.
9. Challenges and Ethical Considerations
- Data Privacy: Ensuring that AI models respect user privacy and data security.
- Bias and Fairness: Avoiding biases in AI models that could lead to unfair or discriminatory outcomes.
- Transparency: Maintaining transparency in how AI-driven decisions are made.
Conclusion
Integrating AI into data science enhances the ability to handle complex, large-scale datasets, leading to more accurate and insightful analyses. The combination of AI and data science is driving innovation across various industries, making it a powerful tool for data-driven decision-making.
Course Details
Module 1: Introduction to Data Science
- 1.1 Introduction to Data Science and AI
- 1.2 Discussion on Course Curriculum
- 1.3 Introduction to Programming
Module 2: Python Basics
- 2.1 Introduction to Python: Installation and Running
- Jupyter Notebook, .py files, Google Colab
- 2.2 Data Types and Type Conversion
- 2.3 Variables
- 2.4 Flow Control: If, Elif, Else
- 2.5 Loops
Module 3: Python Data Types & Utilities
- 3.1 List, List of Lists, and List Comprehension
- 3.2 Set and Tuple
- 3.3 Dictionary and Dictionary Comprehension
- 3.4 Functions
- 3.5 MapReduce
Module 4: Python Production Level
- 4.1 Error / Exception Handling
- 4.2 File Handling
Module 5: SQL
- 5.1 Basics of DBMS
- 5.2 Basics of SQL
- 5.3 SELECT WHERE Statements
- 5.4 JOINS
- 5.5 GROUP BY and ORDER BY
- 5.6 PARTITION BY
Module 6: Mathematics Basics
- 6.1 Derivatives as Slope of a Curve and Optimality Conditions
- 6.2 Integration as Area Under the Curve
- 6.3 Matrix Algebra
- Vector Matrix Multiplication,Matrix Matrix Multiplication ,Eigenvalues and Eigenvectors
Module 7: Python Essential Packages
- 7.1 NumPy
- 7.2 Pandas
- 7.3 Data Visualization Libraries: Matplotlib, Seaborn
Module 8: Statistics Basics
- 8.1 Descriptive Statistics: Central Tendency
- 8.2 Variance, Standard Deviation
- 8.3 Covariance
- 8.4 Pearson’s and Spearman Correlation Coefficients
- 8.5 Correlation vs. Causation
- 8.6 Different Types of Plots for Continuous and Categorical Variables
Module 9: Probability Theory
- 9.1 Basic Count-based Probability
- 9.2 Conditional Probability
- 9.3 Bayes’ Rule
- 9.4 Probability Distribution: Discrete and Continuous
- 9.5 Normal Distribution
- 9.6 Bernoulli and Binomial Distribution
Module 10: Advanced Statistics
- 10.1 Population and Sample
- 10.2 Sampling Distribution and Central Limit Theorem
- 10.3 Standard Error
- 10.4 Confidence Interval
- 10.5 Hypothesis Testing: One-tail, Two-tail, and p-value
- 10.6 Z-test, t-test
Module 11: Data Visualization using Power BI
- 11.1 How to Use Power BI
- 11.2 Basics of Power BI
- 11.3 Creating Visualizations using Power BI
Module 12: Exploratory Data Analysis
- 12.1 Introduction to Practical Datasets
- 12.2 Missing Values Treatment
- 12.3 Outlier Detection and Treatment
- 12.4 Plotting (Univariate, Bivariate)
- 12.5 Column Standardization
- 12.6 Treating Categorical Variables
- 12.7 Understanding Feature Importance Conceptually
Module 13: Machine Learning Fundamentals
- 13.1 Types of Machine Learning Methods
- 13.2 Classification Problem in General
- 13.3 Validation Techniques: CV, OOB
- 13.4 Different Types of Metrics for Classification
- 13.5 Curse of Dimensionality
- 13.6 Feature Transformations
- 13.7 Feature Selection
- 13.8 Imbalanced Dataset and Its Effect on Classification
- 13.9 Bias-Variance Tradeoff
- 13.10 Overfitting vs. Underfitting vs. Normal Fitting
Module 14: Supervised Machine Learning Part 1
- 14.1 Linear Regression and Its Assumptions
- 14.2 L1 and L2 Regularization
- 14.3 Forward and Backward Selection Methods
- 14.4 Logistic Regression
- 14.5 k-Nearest Neighbor Classifier
- 14.6 Naive Bayes Classifier
- 14.7 Decision Tree
- 14.8 Support Vector Machine
Module 15: Supervised Machine Learning Part 2
- 15.1 Ensemble: Bagging
- 15.2 Random Forest Regressor and Classifier
- 15.3 Ensemble: Boosting
- 15.4 Gradient Boosting: AdaBoost
- 15.6 Gradient Descent Technique
- 15.7 Creating Your Own Ensemble Classifier
- 15.9 Recommendation Engine
Module 16: Unsupervised Learning Part 1
- 16.1 Basics of Clustering: Clustering Metrics, Applications
- 16.2 K-Means Algorithm
- 16.4 Hierarchical Clustering: Agglomerative
Module 17: Unsupervised Learning Part 2
- 17.1 Mathematical Prerequisites: Constraint Optimization, Covariance Matrix, Matrix Calculus
- 17.2 Principal Component Analysis (PCA)
Module 18: Deep Learning Part 1
- 18.1 Biological and Artificial Neuron
- 18.2 Perceptron, Learning Rule, and Drawbacks
- 18.3 Multilayer Perceptron, Loss Function
- 18.4 Activation Functions
- 18.5 Training MLP: Backpropagation
- 18.6 Introduction to TensorFlow and Keras
- 18.7 Vanishing and Exploding Gradient Problem
Module 19: Deep Learning Part 2
- 19.1 Regularization
- 19.2 Optimizers
- 19.3 Hyperparameters and Tuning
Module 20: Basics of Image Processing
- 20.1 Images as Matrix
- 20.2 Histogram of Images
- 20.3 Basic Filters Applied on the Images
Module 21: Deep Learning Part 3
- 21.1 Convolutional Neural Networks (CNN)
- 21.2 ImageNet Dataset
- 21.3 Project: Image Classification
- 21.4 Different Types of CNN Architectures
- 21.5 Recurrent Neural Network (RNN)
- 21.6 Using Pre-trained Model: Transfer Learning
Module 22: Basic Natural Language Processing
- 22.1 Texts, Tokens
- 22.2 Bag of Words
- 22.3 Basic Text Classification Based on Bag of Words
- 22.4 n-gram: Unigram, Bigram
- 22.5 Word Vectorizer Basics, One Hot Encoding
Module 23: Intermediate Natural Language Processing
- 23.1 Count Vectorizer
- 23.2 Word Cloud and Gensim
- 23.3 TF-IDF Vectorizer
- 23.4 Word2Vec
- 23.5 Text Classification using Word2Vec
- 23.6 Mini Project 4
Module 24: Deep Learning Part 4
- 24.1 Recurrent Neural Network (RNN)
- 24.2 Back Propagation through Time
- 24.3 Different Types of RNN: LSTM, GRU
- 24.4 Bidirectional RNN
- 24.5 Seq2Seq Model (Encoder-Decoder)
- 24.6 BERT Transformers
- 24.7 Text Generation and Classification using Deep Learning
- 24.8 Generative-AI (ChatGPT)
Module 25: Langchain
- 25.1 Introduction to Langchain
- 25.2 Langchain Retrievers
- 25.3 Chroma DB
- 25.4 Hugging Face Embedding Models
Module 26: Capstone Project
- 26.1 Capstone projects 3 – will be provided and guided how to work on