14,000.00

This deeply involved course is a practitioner’s approach to developing Data Science models using Python. Python is the most widely used language for Data Science with backing from Google, Facebook and others.

You will learn the following

  1. Real Life view into how data science models are built with learnings from real world projects.
  2. A practical approach to understanding data science which will help you in enhancing your knowledge and clear data science interviews.

Enable you to begin your journey into the world of Data Science, Machine Learning and Advanced Analytics

Course Title Data Science and Advanced Analytics Using Python
Duration 40 hours of involved learning
What will you learn This deeply involved course is a practitioner’s approach to developing Data Science models using Python. Python is the most widely used language for Data Science with backing from Google, Facebook and others.

 

You will learn the following

A.      Real Life view into how data science models are built with learnings from real world projects.

B.      A practical approach to understanding data science which will help you in enhancing your knowledge and clear data science interviews.

C.      Enable you to begin your journey into the world of Data Science, Machine Learning and Advanced Analytics

Course Mode

Instructor Led Live Sessions

Course Content

Week # Saturday
(8AM to 12 PM)
Sunday
(8AM to 12 PM)
1 Manipulating Data in Python
Introduction to Data ScienceNumerical, Categorical, Ordinal data
Managing Data Frames
Feature Engineering
Dimensionality Reduction
Visualizing Data
Creating visuals from Data
Analyzing data through charts (Line charts, Pie Charts, Bar charts etc)
Box Plots
Q-Q plots
2 Regression Models
Understanding Simple Regression
Multiple Regression
Gradient Descent
Exercise: Boston Housing Pricing
Classification Models: Logistic Regression
Understanding Logistic Regression
Confusion Matrix
Accuracy, Precision, Recall
ROC Curve
Exercise: Identifying Donors
3 Decision Trees and Random Forests
Decision Trees
Ensemble Models
Random Forests
XGBoost
Exercise: Identifying donors
Support Vector Machines, Naïve Bayes
Concept behind Support Vector Machines
Kernels
Naïve Bayes and Bayes Theorem
Exercise: Classification problem through SVM and Naïve Bayes
4 Cluster Analysis
Hierachical Clustering
K-means Cluster
Exercise: Customer Segmentation
Natural Language Processing through Text Mining
Text Processing
Bag of Words
Named Entity Resolution
Exercise: Classifying emails as Spam
5 Introduction to Deep Learning
Introduction to Neural Networks
Back-propagation
Convolutional Networks
Sequence to Sequence Models
Exercise: Build a neural network to classify donors
Advanced Topics
Introduction to Time Series Analysis
Reinforcement Learning
Recommendation Systems
Introduction to Big Data