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Datascience Master Class-30 Days Challenge

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FREE DATASCIENCE Master Class -30 Day Hackathon with Full Hands-on

What you will Learn?

⭐DATA SCIENCE

✅Day-1: Introduction to Artificial Intelligence, Data Analytics & Road Map to become a Data Scientist

⭐EXCEL

✅Day-2: Data Preparation – Power Query & Tables
✅Day-3: Data analytics- Formula & Pivot Table
✅Day-4: Story Telling – Charts & Dashboard

⭐PYTHON

✅Day-5: Introduction to Python & Installing Python and its Libraries
✅Day-6: Basic Python Programming for Data Analytics

⭐STATISTICS & PROBABILITY

✅Day-7: Introduction to Statistics & Use Case of Statistics on Data
✅Day-8: Population and Sampling

⭐BI TOOLS – TABLEU

✅Day-9: Connect Tableau to a Variety of Datasets
✅Day-10: Visualize Data in the Form of Various Charts, Plots, and Maps

⭐BI TOOLS – POWERBI

✅Day-11: Connect Tableau to a Variety of Datasets
✅Day-12: Visualize Data in the Form of Various Charts, Plots, and Maps and Calculate Data
NUMPY
✅Day-13: Python Numpy functions

⭐PANDAS

✅Day-14: Pandas for Data analytics in Python

⭐MATPLOTLIB – Data Visualization

✅Day-15: Matplotlib for data visualization

⭐SEABORN- Data Visualization

✅Day-16: Seaborn for data visualization

⭐DATABASE – SQL

✅Day-17: SQL basics for Data analytics

⭐DATABASE – MONGODB

✅Day-18: MongoDB basics for Data analytics

⭐MACHINE LEARNING

✅Day-19: Introduction to Machine Learning & its libraries

⭐Supervised Learning – Classification

✅Day-20: Salary Estimation using K-NEAREST NEIGHBOR – SUPERVISED LEARNING

⭐Supervised Learning – Regression

✅Day-21: House Price Prediction using LINEAR REGRESSION – SUPERVISED LEARNING

⭐UnSupervised Learning – Clustering

✅Day-22: Identifying the Pattern of the Customer spent using K-MEANS CLUSTERING

⭐UnSupervised Learning – Association

✅Day-23: Market Basket Analysis using APIRIORI

⭐Reinforcement Learning

✅Day-24: Web Ads. Click through Rate optimization using UPPER BOUND CONFIDENCE

⭐Natural Language Processing

✅Day-25: Sentimental Analysis using Natural Language Processing

⭐DEEP LEARNING

✅Day-26: Introduction to Deep Learning & its libraries

⭐Multi-Layer Perceptron

✅Day-27: Diabetes detection using Artificial Neural Network (MLP)

⭐Convolutional Neural Network

✅Day-28: Object Recognition using Pre Trained Model – Caffe
✅Day-29: Brain Tumor Detection using CNN

⭐Recurrent Neural Network

✅Day-30: Stock Price prediction using LSTM

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