Data Science2.0 

Day 2 Session

Day 3 - Data Science & Python Fundamentals - Overview

Day-04 Implementing Pandas Library

Day-05 NumPy Library – Case Study

Day-06 Matplotlib - Application

Day-07 - Seaborn and Scikit Library Implementation

Day-08 Logistic & Linear Regression

Day-09 - SVM (Support vector machine) Algorithm

Day 10 - ML Algorithm - Decision Tree

Day 11 - Deep learning techniques - Bagging & Boosting

Day 11 - Bagging & Boosting PPT

Day 12 - deep learning techniques - keras

Day 13 - Neural Network - ANN & CNN

Day 14 - Importing and Manipulation of Data

Day 15 - concepts on excel sheet with data science

day 16 - Concepts of Data science - Data Frame, Function & Data Preparation

day_17 - an intro to QlikView tool, Qlik scale tool

Day 18 - Basic Concepts of QlikView and the usage of this tool in Datascience Domain

Day 19 - NLP in Data Science

Day 20 - Case Study - Detection Models - I - Cyber Money Laundering Detection

Day 21 - Case Study - Detection Models - II - Anamoly Detection

Day 22 - Case Study - Prediction Models I - Heart Disease Prediction

Day 23 - Myers Briggs Personality Prediction Using ML

Day 24 - Classification Models - I - Spam Classification Models

Day 25 - Classification Models -II- Movie Review Classification

Day 26 - Segmentation Models -I- Customer Segmentation Model Creation

Day 27 - Project - 1 - Emotion Detection using Python

Day-28 Project - II - Wine Quality Prediction using Datasets

Day-29 Project - III - Twitter Dataset Classification using DL

Day-30 Data Science - Tools , Languages & Jobs