Data Science

About the course

This course offers an in-depth understanding of Data Science, starting with foundational Python programming, data manipulation, and statistical concepts. Explore advanced topics like machine learning, deep learning, and big data technologies. Learn to preprocess and analyze data using popular libraries like Pandas, NumPy, and Matplotlib. Gain hands-on experience building scalable data pipelines, deploying models as web endpoints, and mastering cloud-based workflows. The course concludes with capstone projects to solidify your expertise.

What you will learn:

1. Foundation of Data Science

Introduction to Data Science and its Lifecycle
Python Programming Basics
Data Manipulation with Pandas and NumPy
Probability and Statistics Fundamentals

2. Data Analysis and Visualization

Exploratory Data Analysis (EDA)
Data Visualization with Matplotlib, Seaborn, and Pandas
Cleaning and Preparing Data for Analysis

3. Machine Learning

Supervised Learning: Regression and Classification
Unsupervised Learning: Clustering and Dimensionality Reduction
Feature Engineering and Model Optimization

4. Deep Learning

Neural Networks: Feedforward, CNN, RNN, and LSTM
TensorFlow and Keras for Model Training and Deployment

5. Big Data Tools and Technologies

Hadoop and Apache Spark Basics
PySpark for Batch and Streaming Pipelines
Introduction to Distributed Feature Engineering

6. Data Science in Production

Building Scalable Data Pipelines with PySpark
Model Deployment as Web Endpoints (Flask/Django)
Leveraging Containers with Docker and Kubernetes

7. Capstone Projects and Hands-On Learning

Real-world Data Science Projects
Working with APIs and Public Datasets
Deploying Models in Cloud Environments

8. Emerging Trends and Tools

Automated Machine Learning (AutoML)
Responsible Data Science and Ethics
Introduction to Streaming Workflows with Kafka

₹45,000

(GST included)

(3 Months of Professional Training & 6 Months of Internship)