We’ll cover Python basics like data types, loops, data structures, functions, error handling, and debugging, with assessments and assignments. Topics include Python’s importance, IDE setup, and key concepts like variables, strings, and common errors.

We’ll explore advanced Python concepts, including comprehensions, file handling, regular expressions, OOP, pickling, and more. Key topics include functions as arguments, debugging, classes, lambdas, iterators, and working with JSON and Excel data.

Learn the importance of algorithmic thinking and efficient coding through data structures, algorithms, and memory management. Topics include time complexity, binary search, stacks, heaps, best coding practices, and assessments.

Learn to handle large datasets with the Pandas library, covering essential features like Series, DataFrames, merging, grouping, pivot tables, and date/time functionality, with practical examples.

Explore SQL-based databases, covering queries, schemas, normalization, joins, functions (math, conversion, date/time, string), conditional expressions, subqueries, ranking, window functions, and Python-SQL integration.

We will continue with MongoDB beyond basics like embedding documents, dealing with types of data, schema types, and Data relationships in MongoDB, etc.

Dive into Probability and Statistics, essential for analyzing and interpreting data. Topics include probability theory, Bayes’ theorem, distributions (discrete and continuous), expectation, variance, and sampling, with hands-on practice using NumPy.

We will continue with statistics and probability, and we will deal with descriptive and inferential statistics along with Hypothesis testing and a lot of other relevant statistics methods.

Master data visualization to uncover patterns, trends, and correlations using libraries like Matplotlib and Seaborn. Learn to create histograms, scatter plots, bar plots, pie charts, and more, along with styling and handling complex JSON files for storytelling.

This module offers a comprehensive journey through data science, machine learning, deep learning, and deployment. Learn key concepts like data preprocessing, EDA, supervised and unsupervised learning, NLP, computer vision, and PyTorch. Wrap up with AWS-based model deployment and real-world projects like fraud detection, customer segmentation, and chatbot development.