30 Days Data Science Webinar

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

Please find the Registration Link ,Seats are Limited