The Master Data Science course offers a comprehensive curriculum that covers essential topics in data science, including Python programming, data structures, machine learning, deep learning, and data visualization. Students will gain hands-on experience with tools like Django, TensorFlow, Keras, and PyTorch. The course also focuses on advanced techniques like natural language processing, time series analysis, and model evaluation, preparing graduates for a successful career in data science and machine learning.

1. Case study based discussion and problem solving thought process initiation
2. Different Analytical Domain
3. Industry mentors guest talk

1. Python, Anaconda and relevant packages installations
2. Structure of python program (comments, indentations)
3. Operators
4. Data type, variable
5. User input, string methods

1. Mutable / Immutable
2. List
3. Tuple
4. Sets
5. Dictionary
6. List, sets and dictionary comprehension

1. If & Else and Nested if else
2. While and For loop
3. Break, Continue and Pass
4. Keywords in python
5. Pattern making

1. Basic functions
2. Built-in
3. User-defined functions
4. *args, **kwargs

1. Maps, filter, reduce
2. Iterators and iterables
3. Closures / decorators
4. Generators
5. File Handling and Exception Handling

1. Class & Object
2. Data abstraction, encapsulation, Inheritance, Dunder methods
3. Customized modules

1. Starting your First Web Application
2. Developing Standard Web Template
3. Django Admin
4. Models
5. Views and URLconfs
6. Forms

1. Indexing/slicing
2. Appending /Inserting on axis
3. Mathematical and statistical operations
4. Sort/Condition
5. Transpose Operations
6. Joining/splitting

1. Data Extraction
2. Series Dataframe and Plane
3. Indexing and Slicing
4. Conditions/Grouping/Imputations
5. Append/concat/merge/join
6. Date time functionalities and resampling
7. Excel functions

1. Customization of matplotlib and seaborn
2. Scatterplots/barplot/histogram/density plots
3. Box Plot and outlier detection
4. Visualization Linear relationship
5. Univariate, Bivariate and Multivariate analysis

1. MySQL

1. Linear algebra
2. Matrix operation and properties
3. Introduction to calculus
4. Theory of optimization
5. Probability
6. Conditional Probability
7. Dependent and Independent events
8. Bayes Theorem
9. Descriptive / Inferential
10. Variance / standard deviation
11. Covariance And correlation
12. Central Limit theorem
13. Types of distributions
14. pdf, cdf , pmf
15. Confidence Intervals
16. Hypothesis testing
17. Z Test, t test, chi-2 test
18. F-test/Anova

1. Introduction to Artificial Intelligence (DS, ML & DL)
2. Applications of Machine Learning
3. Categorization of Machine Learning
4. Supervised / Unsupervised / Semi Supervised
5. Parametric vs Non-Parametric

1. Linear Regression
2. Polynomial Regression
3. Lasso Regression
4. Ridge Regression
5. Stepwise Regression
6. Bayesian Regression

1. Logistic Regression
2. KNN
3. SVM (Support Vector Machines)
4. Decision Tree
5. Naive Bayes
6. LDA
7. Classification for Imbalanced Dataset

1. Random forest
2. Adaboost
3. Gradient boosting
4. Decision Tree
5. Xgboost

• Resume building
• Mock interviews
• LinkedIn account creation
• GitHub repository creation
• Migration of projects to GitHub