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Data Science with Python Training Hyderabad

Data Science with Python

Introduction

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Python
  • Introduction to Machine Learning

 Statistical Inference

  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution

Python for Datascience

  • Introduction
  • Python Basics
  • Python Lists
  • Python Functions and Packages
  • Numpy
  • Practice Exercises

Data Extraction, Wrangling and Exploration

  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data

Hands-On Practise:

  • Loading different types of dataset in Python
  • Arranging the data
  • Plotting the graphs

 Introduction to Machine Learning

  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithms: Linear Regression and Logistic Regression

      Hands-On:

  • Implementing Linear Regression model in Python
  • Implementing Logistic Regression model in Python

 Classification Techniques

  • What are classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification

      Hands-On:

  • Implementing Decision Tree model in Python
  • Implementing Linear Random Forest in Python
  • Implementing Navies Baye’s model in Python
  • Implementing Support Vector Machine in Python

 Unsupervised Learning

  • What is Clustering & its use cases
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?

      Hands-On:

  • Implementing K-means Clustering in Python
  • Implementing C-means Clustering in Python
  • Implementing Hierarchical Clustering in Python

 Recommender Engines

  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendations
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation use cases

Hands-On:

  • Implementing Association Rules in Python
  • Building a Recommendation Engine in Python

Time Series

  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting

      Hands-On:

  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series Forecasting
  • Forecasting for given Time period

Text Mining

  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text
  • TF-IDF
  • Beyond TF-IDF

Hands-On:

  • Implementing Bag of Words approach in Python
  • Implementing Sentiment Analysis on  Data using  Python

 Deep Learning

  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN’s