Data Science Training

Teaching mode: Online/Offline              Duration: 45 Days      Course Fee: ₹ 12,000/-

For Regular and Weekend Batches  Call us +91 9032208424, +91 9642323272  +91 9652522399 


Data Science Training - Course Overview


Introduction

  • Introduction to Data Science.
  • Skills required for Data Scientist.
  • Introduction to Analytics.
  • Types of Analytics.
  • Introduction to Machine Learning.
  • Applications of Machine Learning.
  • Future scope of Machine Learning.
  • Introduction to Artificial Intelligence.

Introduction of R and Python

  • Introduction and Installation of R studio.
  • Introduction of Data types in R

Vectors

Lists

Matrices

Arrays

Factors

Series

Data Frames

  • Installation of Packages in R.
  • Installation of Jupyter Notebook and Spyder for Python.
  • Introduction of Data types in Python

Numbers

String

List

Tuple

Dictionary

  • Introduction of Libraries in Python

Numpy

Pandas

Matplotlib

Scipy

Introduction to Machine Learning Algorithms

Data Preprocessing

  • Handling Missing Values with Python and R.
  • Handling categorical data.
  • Scaling Techniques.

Regression

  • Introduction to Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Evaluation of Regression models

Classification

  • Introduction to Classification
  • Logistic Regression
  • KNN
  • SVM
  • Decision Tree
  • Random Forest
  • Evaluation of Classification models.

Clustering

  • Introduction to Clustering
  • K-Means clustering

Association Rule Mining

  • Introduction to Association Rule Mining.
  • Apriori algorithm

Text Mining

  • Introduction to Text Mining
  • Introduction to Sentiment Analysis
  • Data preprocessing

Removal of Punctuation

Removal of Numbers

Removal of Stop words

Stemming

Tokenization

Document term Matrix

Bag of Words

          Forecasting

  • Introduction to Time Series
  • Introduction to Trend and Seasonality
  • Introduction to ACF and PACF
  • ARIMA

Deep Learning

  • Introduction to Deep Learning
  • Introduction to ANN

Activation Function

Gradient Boosting

  • Introduction to CNN

Introduction to PCA

Introduction to K-fold cross validation

Introduction to Boosting

Introduction to Big Data

Tools:

  • R studio for R
  • Jupyter Notebook for Python
  • Introduction to Knime
  • Introduction to Weka
  • Spyder for Python