Course Overview
Schedule of Classes
Course Curriculum
1 Subject
Data Science Batch 304
10 Exercises • 37 Learning Materials
Assignments
Data-Preprocessing
Assignment
Data Visualization
Assignment
Supervised Learning
Assignment
Hyperparameter Tuning
Assignment
Un-Supervised Learning
Assignment
Time-Series Analysis
Assignment
Feature Selection
Assignment
Statistics
Assignment
Deployment Ready Flask with Model Code
Assignment
Final Project
Assignment
Day -1 Data Science Pyramid and Job Roles, No-Code Data Science Tool, AI vs ML vs DS vs DL
session 1
ZIP
Day -3 Statistical Analysis, Feature Engineering
session 3
ZIP
Day -4 Machine Learning Basics, Supervised Machine Learning, Cross-Validation and Model Evaluation
session 4
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Day -5 Feature Importance and Model Interpretation, Unsupervised Machine Learning
session 5
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Day -6 Model Deployment and Maintenance
session 6
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Day -7 Introduction to Python and Virtual Environment
session 7
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Day -8 Python Basics
session 8
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Day -9 Python for Data Science, File Handling using Pandas
session9
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Day -10 Deep Dive into Python Libraries and its usage for Data Science
session10
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Day - 11 Data Pre-Processing Basics
session11
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Day -12Data Wrangling
session12
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Day -14EDA (Exploratory Data Analysis)
session14
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Day -15Deep Dive into Plots and its attributes
session15
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Day -16Statistical Analysis and Outlier Detection
session16
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Day - 17 Data Pre-Processing Advanced - Feature Engineering, Dimensionality Reduction (PCA, t-SNE)
Data Pre-Processing Advanced - Feature Engineering, Dimensionality Reduction (PCA, t-SNE)
ZIP
Day - 18 Pre-Processing Tools: ColumnTransformer and Pipeline
Pre-Processing Tools: ColumnTransformer and Pipeline
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Day - 19 Case Study 1: Program Guidance Implementing all the above processes
session19
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Day -20 Case Study 2: Program Guidance Implementing all the above processes
session20
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Day -21Introduction to Machine Learning and its Types
session21
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Day -22Basics of Models, Model Evaluation, and Cross Validation
session22
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Day - 23 Supervised Learning - Regression
Supervised Learning - Regression
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Day - 24 Supervised Learning - Evaluation Metrics - MSE, F1 score, R2, RMSE, Precision, Recall, Confusion mat
Supervised Learning - Evaluation Metrics - MSE, F1 score, R2, RMSE, Precision, Recall, Confusion mat
ZIP
Day -25Supervised Learning - Classification
session25
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Day -26 Supervised Learning - Decision Tree and Ensemble Model
session26
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Day -27Supervised Learning - Regularization, Model Performance and Optimization - Overfitting, Underfittin
session27
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Day -29Unsupervised Learning - Evaluation Metrics - Cross Validation and Confusion Matrix
session29
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Day - 30Hyperparameter Tuning - Grid Search, Bayesian, Random Search
session30
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Day - 31 Basics of Model Deployment
Basics of Model Deployment
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Day - 32 Case Study 3: Program Guidance Implementing all the above processes
Case Study 3: Program Guidance Implementing all the above processes
ZIP
Day-33Time Series Analysis and Forecasting
session33
ZIP
Day -34Introduction to Deep Learning, Neural Network, and NLP
session34
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Day -35Introduction to Deep Learning, Neural Network, and NLP
session35
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Day -36Final Project - Day 1 - Presentation
session 36
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Day -37 Final Project - Day 2 - Presentation
session37
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Day -38Cloud Introduction
session38
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Day-39 Fundamentals of Power BI and Tableau
session39
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Day - 40 Fundamentals of DB (SQL and No SQL)
Fundamentals of DB (SQL and No SQL)
ZIP
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17 Courses • 1288 Students
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