Certificate Course on: Data Science 5000

  • Duration: 45 hours

  • Per Day: 1-2 Hours
Data Science

The Training session will be live *Recorded lectures will be available

Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.In data science, one deals with both structured and unstructured data.

Week Duration Topic Contents
Week 1 1-2 Hours Introduction
  • Data Science and Business Buzzwords
  • Difference between Analysis and Analytics
  • Data Science Info graphic
  • Tools in Data Science
Week 1 1-2 Hours Data Science Techniques
  • Techniques for working with Traditional Data
  • Techniques for working with Big Data
  • Business Intelligence Techniques
  • Machine Learning Techniques
  • Examples
Week 1 1-2 Hours Data Science Techniques
  • Techniques for working with Traditional Data
  • Techniques for working with Big Data
  • Business Intelligence Techniques
  • Machine Learning Techniques
  • Examples
Week 1 1-2 Hours Linear Regression
  • Linear Regression Model
  • Correlation vs. Regression
  • First Regression using Python
  • Interpret Regression Table
  • OLS Model
Week 1 1-2 Hours Multiple Linear Regression
  • Multiple Linear Regression Model
  • Adjusted R-Square
  • Test of Significance
  • p-value
  • Linearity
Week 1 1-2 Hours Logistic Regression
  • Introduction to logistic regression
  • Logistic vs. Logit Function
  • Building logistic regression
  • Understanding logistic regression table
  • Over fit and under fit
Week 2 1-2 Hours Cluster Analysis
  • Cluster Analysis
  • Examples of clusters
  • Difference between classification and clustering
  • Math Pre-requisites
  • K-means clustering
Week 2 1-2 Hours Clustering
  • Clustering using python
  • Clustering categorical data
  • Pros and cons of k-means clustering
  • Relationship between clustering and regression
Week 2 1-2 Hours Clustering Case study
  • Market segmentation with Cluster Analysis
  • Species segmentation with cluster analysis
Week 2 1-2 Hours Preliminary mathematics for deep learning
  • Matrix
  • Scalars ad vectors
  • Linear algebra and geometry
  • Tensor
  • Matrix operation
Week 2 1-2 Hours Deep Learning
  • Introduction to neural networks
  • Training the model
  • Types of machine learning
  • Linear Model
Week 3 1-2 Hours Deep Learning
  • Objective function
  • Optimization Algorithms
  • Basic NN examples using Python
Week 2 1-2 Hours Deep Learning
  • TensorFlow 2.0
  • Outlining the model with TensorFlow
  • Basic NN with TensorFlow
  • What is Layer?
  • Deep net
  • Activation Functions
  • Backpropagation
Week 2 1-2 Hours Classifying on the MNIST Dataset
  • How to tackle MNIST
  • Preprocessing the Data
  • Outline the model
  • Learning
  • Testing the model
Week 2 1-2 Hours Business Case Example
  • Exploring the Dataset
  • Identifying predictors
  • Outline the solution
  • Preprocessing Data
  • Learning and result interpretation
  • Testing the model
Week 2 1-2 Hours Tableau
  • Tableau for Data Visualization
  • Creating first viz. on Tableau
  • Graphs and Scatter Plots
  • Publishing the viz
  • Embedding viz on website