Data Science Program

Join our Full Stack Data Science Program to master the entire data science lifecycle. Gain hands-on experience with data collection, machine learning, deep learning, and model deployment. Learn to build scalable data pipelines, work with cloud platforms, and solve real-world business problems.

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Application Deadline: 6th October 2024

Want to upskill?

Enroll today in our Data Science Program to elevate and expand your expertise.

Program Fee

Duration

0 Week(s)

Study Mode

Virtual classes

Introduction

The Full Stack Data Science Program at Indepth Research Institute (IRES) is an intensive, end-to-end learning journey designed to equip participants with the full spectrum of skills needed to succeed in the field of data science. The program covers everything from data collection, processing, and analysis to advanced machine learning, AI, and data-driven decision-making. Through a carefully crafted curriculum spanning data curation, advanced programming, and sophisticated analytics techniques, participants will gain hands-on experience and practical insights into the latest technologies and industry best practices. From understanding the data lifecycle to mastering machine learning algorithms and deploying data-driven solutions in the cloud, this program offers a holistic learning experience tailored to meet the demands of today's data-driven industries.

Application Process

Register

Submit your registration by filling in the form online.

Make Payments

Receive Invoice upon registration and make payments.

Join program

Choose a mode of study and attend course.

Program Prerequisites

Basic knowledge of statistics and mathematics.
Familiarity with programming concepts.
Prior experience in data analysis (preferred but not required).
Experience with SQL and database management is recommended.
Willingness to engage in hands-on exercises and projects.

Program Modules

What is Data Science?
Importance of Data Science in Today’s World
Key Concepts and Terminologies in Data Science
Steps in a Data Science Project
Tools and Technologies in Data Science
Data Science Applications in Industry
Hands-on Exercise: Explore a dataset using Python and summarize key findings.

Sources of Data: Structured vs. Unstructured Data
Web Scraping and API Integration
Data Collection Ethics and Best Practices
Hands-on Exercise: Collect data using web scraping or an API.
Handling Missing Data
Data Transformation Techniques
Dealing with Outliers and Anomalies
Hands-on Exercise: Clean and preprocess a messy dataset.

Basic Probability Concepts
Probability Distributions
Conditional Probability and Bayes’ Theorem
Hands-on Exercise: Solve probability problems using Python.
Hypothesis Testing
Confidence Intervals
P-values and Significance Levels
Hands-on Exercise: Perform hypothesis testing on a sample dataset.

Measures of Central Tendency and Dispersion
Data Visualization Techniques
Understanding Data Distributions
Hands-on Exercise: Perform exploratory data analysis on a dataset.
Visualizing Data with Python (Matplotlib, Seaborn)
Creating Effective Charts and Graphs
Storytelling with Data Visualization
Case Study: Analyze a real-world dataset and present insights through visualizations.

Supervised vs. Unsupervised Learning
Key Algorithms in Machine Learning
Model Evaluation Metrics
Hands-on Exercise: Build a simple classification model using Python.
Linear Regression
Multiple Regression
Regularization Techniques (Ridge, Lasso)
Case Study: Predict housing prices using a regression model.

Bagging, Boosting, and Stacking Techniques
Hyperparameter Tuning with Grid Search and Random Search
Feature Importance and Model Explainability
Hands-on Exercise: Build and tune ensemble models for classification tasks.
Time Series Decomposition and Analysis
ARIMA, SARIMA, and Exponential Smoothing Models
LSTM for Time Series Prediction
Case Study: Time series forecasting for financial data.

Introduction to Deep Learning and Neural Networks
Activation Functions and Backpropagation
Implementing Neural Networks with TensorFlow and Keras
Hands-on Exercise: Build a simple neural network for image classification.
CNN Architecture and Applications
Implementing CNNs for Image Processing Tasks
Transfer Learning with Pretrained Models
Case Study: Object detection using CNNs.

Tokenization, Lemmatization, and Stemming
Bag-of-Words and TF-IDF Models
Word Embeddings with Word2Vec and GloVe
Hands-on Exercise: Build a text classification model with NLP techniques.
Recurrent Neural Networks (RNNs) and LSTM Networks
Transformer Models and BERT
Text Generation and Sequence-to-Sequence Models
Case Study: Sentiment analysis on social media data using NLP models.

Overview of Hadoop and Spark
Distributed Data Processing with PySpark
Data Storage Solutions: HDFS, NoSQL Databases
Hands-on Exercise: Perform distributed data processing with PySpark.
Building ETL Pipelines for Big Data
Batch Processing vs. Real-Time Data Processing
Data Lake and Data Warehouse Architectures
Case Study: Build an end-to-end data pipeline for big data processing.

Deploying Models with Flask, FastAPI, and Streamlit
Building RESTful APIs for ML Models
Model Serving and Scaling with Docker and Kubernetes
Hands-on Exercise: Deploy a machine learning model as a web service.
Introduction to MLOps: CI/CD for Machine Learning
Model Versioning and Continuous Deployment
Monitoring Model Performance and Data Drift
Case Study: MLOps pipeline setup for a production model.

Overview of AWS, Azure, and Google Cloud
Using Cloud Storage and Compute Resources
Cloud-Based Machine Learning Tools (AWS Sagemaker, Google AI Platform)
Hands-on Exercise: Train and deploy models using cloud platforms.
Building Scalable Data Pipelines on the Cloud
Serverless Architectures for Data Processing
Managing Data and Compute Resources Efficiently
Case Study: Scalable architecture for a data science project on the cloud.

Data Privacy and Security
Bias and Fairness in Machine Learning
Ethical Dilemmas in Data Science
Case Study: Analyze ethical issues in a real-world data science project.
Version Control and Reproducibility
Collaboration in Data Science Teams
Continuous Learning and Professional Development
Hands-on Exercise: Create a project structure that follows data science best practices.