Understand what Data Science and Analytics are and why they are essential today.

Learn about the different methodologies and terminologies in analytics.

Explore real-world applications of data science in business.

Get an overview of various analytics tools and their usage.

Introduction to Python and its history in data science.

Learn Anaconda installation and working with Jupyter Notebook.

Understand Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.

Master control structures, loops, functions, and data structures (lists, dictionaries, tuples).

Explore file handling, debugging, and exception handling in Python.

Work with NumPy arrays for numerical operations and data handling.

Learn Pandas for data manipulation – filtering, sorting, merging, and grouping data.

Explore data visualization techniques using Matplotlib and Seaborn.

Create various plots (scatter, histograms, violin plots, box plots, and more) for insightful analysis.

Understand descriptive statistics – mean, median, standard deviation, and variance.

Explore probability distributions (Normal, Binomial, Poisson, etc.).

Learn inferential statistics – hypothesis testing, confidence intervals, and p-values.

Work with correlation, covariance, and causation for data relationships.

Understand the difference between Data Science & Machine Learning.

Learn types of ML algorithms – supervised vs. unsupervised learning.

Implement Regression models (Linear, Multiple, and Logistic).

Work with Decision Trees, Random Forest, SVM, and KNN.

Explore K-Means Clustering and Dimensionality Reduction techniques like PCA.

Introduction to CNN

Types Of Layers

Activation Layer, Pooling, Flattening etc.

Activation Function, Cost Function

Gradient descent back propagation

Introduction to tensor flow

Computation graph

Tensors

Placeholder and Variables

Neural networks

Introduction to NLP

Introduction to NLTK

Object detection overview

How SSD works

Predicting object position

Histogram and oriented gradients

Face & Eye detection

Integral images

Cascading

Merging faces

Facial recognition

Apply theoretical knowledge to industry-based projects in Data Science & AI.w

Work on NLP projects like sentiment analysis using Twitter data.

Implement predictive modelling techniques for business decision-making.

Explore AI-driven automation projects using TensorFlow and Opencv.