Statistical Analysis in Data Science Using Python

( Duration: 3 Days )

Statistics plays an important role in data analysis, machine learning and data science. In Statistical Analysis in Data Science Using Python training course, you will learn the fundamental statistics concepts using Python. You will learn to use third-party Python Packages such as Matplotlib, Seaborn, Pandas and Statsmodels to learn Statistics.

By attending Statistical Analysis in Data Science Using Python workshop, delegates will learn:

  • Visualising statistical relationship using Seaborn
  • Statistical modeling using StatsModel
  • Hypothesis Testing
  • Bayes' inference approach

  • Basic Python
  • Data Scientists
  • Data Analysts
  • Marketeers



Descriptive Statistics

  • Mean & Medium
  • Standard Deviation & Variance
  • Percentiles
  • Summary

Data Visualization with Seaborn

  • What is Seaborn
  • Visualizing Statistical Relationships with Scatter Plot
  • Visualizing Categorical Data with Bar Plot
  • Visualizing Correlation with Pair Plot and Heatmap
  • Visualizing Linear Relationships with Regression

Hypothesis Testing with SciPy

  • What is Hypothesis Testing
  • T Statistics
  • Student's t-test

Statistical Modeling with StatsModel

  • What is Statistical Modeling
  • Statistical Modeling with StatsModel
  • Goodness of Fit

Bayesian Inference with PyMC3

  • Bayesian Inference
  • Using PyMC3 for Bayesian Inference

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.