DataScience with Python

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Course Overview

Xpertised Offers Advanced and Personalized Instructor Led Online Classroom training on DataScience with Python which gives you the opportunity to interact with a DataScience with Python instructor and help you enhance yourself to meet the demands of the industry.

Learn from our instructors from the convenience of your home or office. Interact and learn live with trainers and other participants. The course assumes a working knowledge of key data science topics (statistics, machine learning, and general data analytic methods). Programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, or FORTRAN) is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions. Experience with Python is helpful (but not required).

Course Content

Introduction to Datascience

  • What is Datascience
  • What is machine learning
  • What is deep learning
  • What is AI
  • Data Analytics and its types

Introduction to Python

  • What is Python
  • Why Python
  • Installing Python
  • Python IDE's
  • Spyder Overview
  • Jupyter Notebook Overview

Python Packages

  • Pandas
  • Numpy
  • Sci-kit learn
  • Mat-plot library

Importing data

  • Reading csv files
  • loading and storing data with clipboard
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file

Manipulating data

  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation

Statistics Basics
Central Tendency
    * Mean
    * Median
    * Mode
    * Skweness
    * Normal distribution
Probability Basics
    * What does mean by probability
    * Types of probability
    * ODDS Ratio
Standard Deviation
    * Data deviation & distribution
    * Variance
Bias variance Trade off
    * Underfitting
    * Overfitting
Distance metrics
    * Eucliclean Distance
    * Manhattan Distance
Outlier analysis
    * What is an Outlier
    * Inter Quartile Range
    * Box and Whisker Plot
    * Upper Whisker
    * lower Whisker
    * Scatter Plot
    * Cook's Distance
Missing Value treatments
    * What is NA
    * Central Imputation
    * KNN imputation
    * Dummification
    * Pearsons Correlation 
    * Positive and Negative Correlation

Error metrics
    * Confusion matrix
    * Precision
    * Recall
    * Specificity
    * F1 Score
    * MSE
    * RMSE
    * MAPE

Introduction to Machine learning

  • Link for ISLR
  • Introduction to Machine Learning
  • Machine Learning with Python

Supervised learning
Linear Regression
    * Linear Equation
    * Slope
    * Intercept
    * R Square value
Logistics regression
    * ODDS ratio
    * Probability of success
    * Probability of failure
    * ROC curve

Unsupervised learning

  • K-Means
  • K- Means ++
  • Hierarchical Clustering

Other Machine Learning Algorithms

  • K.Nearest Neighbour
  • Naive Bays Classifier
  • Decision Tree - CART
  • Decision tree - C50
  • Random Forest

Customer Reviews

Thanks to Xpertised and the tutor who walked me through all the topics with Practical exposure which is helping me in my current project.

Course was quite helpful in terms of understanding of concepts and practicality. Its really a very friendly environment to learn. The timing were mutually chosen, as we both are working professional. I am quite satisfied with the course.


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