Online Post Graduate Program in Data Science

What learn

Instructions on how to hold your camera correctly, which will help you to get sharp photographs.

Sound advice on how to choose the right camera lens for each situation.

The necessary confidence to change the most important camera settings correctly at the right time, which in turn will al...

The ability to compose photos that are well balanced and very pleasing to the eye.

Course Content

18 sections • 57 lectures • 55h 00m total length

	• Important concepts in probability theory including random variables and independence		mb

	"Introduction & Descriptive Statistics!" Here, we'll dive into the basics of descriptive statistics-like mean, median, mode, variance, and standard deviation. This module is all about understanding why these measures matter for different types of data. We won't just cover the theory; we'll show you how these stats are used in the real world. By the end, you'll not only know the essentials but also how to apply them for practical data analysis.		mb

	Introduction to Probability Distributions- In this module, we will cover various distributions and understand pdf, pmf and cdf		60min

	Hypothesis Testing- This module aims to equip you with the necessary knowledge to choose the appropriate test when analyzing data and determining their relationships. It will provide a detailed explanation of the assumptions underlying each test and teach you how to interpret the results of a hypothesis test accurately		60min

	• Al Vs ML Vs DL Vs DS		60min
	• Types Of ML Techniques		60min
	• Supervised Vs Unsupervised		60min
	• Linear Regression		60min
	• End To End Project		60min

	• Logistic Regression End		60min
	• To End Project Support		60min
	• Vector Machines		60min
	• Naive Bayes		60min
	• End To End Project		60min

	• PCA		60min
	• Kmeans Clustering		60min
	• Hierarchical Clustering		60min
	• DBSAN Clustering		60min

	• Feature Selection		60min
	• Handling Missing Values		60min
	• Handling Imbalanced Data		60min
	• Handling Outliers		60min
	• Encoding		60min
	• Feature Scaling		60min

	• Decision Tree		60min
	• Forest Ensemble		60min
	• Techniques Adaboost		60min
	• Gradient Boosting		60min
	• Xgboost End to End		60min
	• Project		60min

	• Time Series Using fbprophet Time Series		60min
	• Using AutoTs (Automated time series)		60min

	• Industry Project 1		60min
	• Industry Project 2		60min
	• Industry Project 3		60min
	• Industry Project 4		60min

	• Different Activation Functions		60min
	• Different types of Loss Function		60min
	• Different types Of Optimizers		60min
	• Weight Initialization Techniques		60min
	• Drop Out Layer		60min

	• Artificial Neural Network Working		60min
	• Back Propagation In ANN		60min
	• Vanishing Gradient Problem		60min
	• Exploding Gradient Problem		60min

	• Understanding the concept of convolution and its role in feature extraction.		60min
	• Kernel/filter operations and their impact on image data.		60min

	• Overview of the typical CNN architecture (input layer, convolutional layers, pooling layers, fully connected layers).		60min
	• Deepening networks with multiple convolutional and pooling layers.		60min

	• Introduction to Sequential Data and The Need for Specialized Models Like RNNs.		60min
	• Overview of Applications in Natural Language Processing, Time Series Prediction, and more.		60min

	• The challenges of training RNNs and the concept of backpropagation through time.		60min
	• POOLING LAYERS		60min
	• Different pooling techniques (e.g., max pooling, average pooling).		60min
	• The purpose of pooling in reducing spatial dimensions and computational complexity		60min

	• Basic structure of a recurrent neural network. Understanding the recurrent		60min
	• connection and how it allows information to persist through time		60min

Requirements

No prior coding experience is required! However, having the following will be beneficial: ✅ Basic Mathematical Understanding – Knowledge of algebra, statistics, and probability will help in machine learning concepts. ✅ Logical Thinking & Problem-Solving Skills – A strong analytical mindset is essential for data-driven decision-making. ✅ Basic Computer Skills – Familiarity with usi
+ See More ng a computer, Excel, and basic programming logic is a plus. ✅ Interest in Data & Analytics – A passion for working with data, patterns, and insights will enhance learning. ✅ Laptop/Desktop with Internet – A system with at least 8GB RAM and a stable internet connection for online classes & practical exercises.
- See Less

Description

Modulation Digital, awarded by the Bharat Business Award as the Best IT Institute in Delhi, proudly offers an industry-leading Online Post Graduate Program in Data Science. This program is designed to equip students with in-depth knowledge of data analytics, machine learning, AI, and big data to help them secure top job roles in the industry.

Why Choose Us?

100% Placement Guarantee – Get job assistance with expert career mentorship
Lifetime LMS Access – 24/7 access to study materials, recorded lectures & assignments
Industry-Focused Curriculum – Hands-on training with real-world datasets
Live Projects & Case Studies – Work on practical applications of data science