Build Career with Our Comprehensive Data Science Course
Note: Master topic (15 days) separate course on LLM and GenAI not to include if interested we can take admission.
Tools & Topics
- Python
- SQL
- EDA
- Power BI
- Machine Learning
- Deep Learning
Mode
- Online/Offline with Assignments
- Quizzes
- Projects
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Data Science Course Curriculum
- Introduction to Python & Environment Setup
- Basics: Data types, Variables, Operators
- IDEs: Jupyter Notebook, VS Code
- Control Structures: Loops & Conditionals
- Functions and Modules
- Creating and Using Functions
- Modules, Libraries (os, sys, math)
- Exception Handling
- Data Structures
- Lists, Tuples, Dictionaries, Sets
- List Comprehension
- File Handling and Regular Expressions
- Reading/Writing Files
- Regex basics for text data
- Libraries for Data Science
- Numpy: Arrays, Mathematical Operations
- Pandas: DataFrames, Data Manipulation
- Introduction to Databases and SQL
- Basics: SELECT, WHERE, ORDER BY
- Filtering data, Sorting
- Advanced SQL Queries
- Joins: (INNER, LEFT, RIGHT, FULL)
- Subqueries and Nested Queries
- Aggregate Functions
- GROUP BY, HAVING, COUNT, SUM, AVG
- SQL Functions and Views
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- String, Date, and Math Functions
- Creating and Managing Views
- Indexing and Optimization Basics
- Keys (Primary, Foreign)
- Query Optimization
- Understanding Data
- Types of Variables
- Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
- Data Cleaning
- Handling Missing Values
- Removing Duplicates, Outlier Detection
- Data Visualization Tools
- Matplotlib and Seaborn
- Plots: Bar, Histogram, Boxplot, Scatter
- Insights and Correlation
- Analysis
- Correlation Heatmaps
- Understanding Patterns
- Case Study
- EDA on a real-world dataset (Sales, Customer, or Financial data).
- Introduction to Power BI
- Installation, Data Connection (Excel, CSV, SQL)
- Data Transformation
- Power Query Editor
- Data Modeling, Relationships
- Visualization Creation
- Creating Dashboards and Reports
- Charts: Line, Pie, Bar, Maps
- DAX and Filters
- Measures, Calculated Columns
- Slicers, Drill-downs
- Introduction to ML
- Supervised vs Unsupervised Learning
- ML Workflow
- Regression
- Linear Regression, Polynomial Regression
- Evaluation Metrics: RMSE, R²
- Classification
- Logistic Regression, KNN, Decision Trees
- Accuracy, Precision, Recall, F1-Score
- Ensemble Methods
- Random Forest, Gradient Boosting
- Bagging vs Boosting
- Clustering
- K-Means, Hierarchical Clustering
- Model Evaluation
- Train-Test Split, Cross-Validation
- Basics of Neural Networks
- Perceptron, Activation Functions
- TensorFlow/Keras Basics
- Building Simple ANN Models
- Layers, Optimizers, Loss Functions
- Image Classification
- Convolutional Neural Networks (CNN)
- Case Study on MNIST Dataset
- Introduction to NLP
- Text Preprocessing, Tokenization
- Sentiment Analysis
- Deployment Basics
- Saving and Loading Models
- Flask/Streamlit Deployment
- Text Preprocessing and Fundamentals:
- Introduction to NLP
- Tokenization, Stopword Removal, Stemming, Lemmatization
- Bag-of-Words (BoW) and TF-IDF Representation
- Classic NLP Techniques:
- Text Classification (Logistic Regression, Naive Bayes)
- Sentiment Analysis
- Named Entity Recognition (NER)
- Advanced NLP Techniques:
- Word Embeddings (Word2Vec, GloVe)
- Topic Modeling using LDA (Latent Dirichlet Allocation)
- Text Summarization
- Introduction to Large Language Models:
- Understanding LLMs (GPT, BERT, T5, etc.)
- Fine-Tuning Pre-trained LLMs using Hugging Face Transformers
- Prompt Engineering Basics
- Generative AI Applications:
- Introduction to Generative AI Concepts
- Text Generation (using GPT models)
- Applications: Chatbots, Content Creation, and Q&A Systems
- Case Study: Build and Deploy a Simple Chatbot
