Interpreting Data with Python

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Interpreting Data with Python

Author: Janani Ravi

Interpreting Data with Python is a skill that will teach learners how to apply disciplines such as Statistics and Probability to understand data and to prep future models.

What You Will Learn

This skill will convey the most common techniques for using Python to help interpret data, both with and without statistical models.

Pre-requisites

  • Data Literacy
  • Data Analytics Literacy
  • Python for Data Analysts
  • Statistics and Probability
  • Data Visualization with Python

Beginner

Find relationships in a data set and interpret data with simple statistical models using Python.

Finding Relationships in Data with Python

by Janani Ravi

Oct 29, 2019 / 2h 3m

2h 3m

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Description

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well.

In this course, Finding Relationships in Data with Python you will gain the ability to find relationships within your data that you can exploit to construct more complex models.

First, you will learn to summarize your data using univariate, bivariate and multivariate statistics. Next, you will discover how specific forms of visualization have evolved to identify and capture specific types of relationships. You will then learn some advanced tools such as the use of autocorrelation plots and KDE plots that help model probability distributions.

Finally, you will round out your knowledge by using four of these libraries - Matplotlib, Seaborn, Altair and Plotly to find relationships.

When you’re finished with this course, you will have the skills and knowledge to identify and exploit relationships that exist within your data, by efficiently exploring and visualizing that data.

Table of contents
  1. Course Overview
  2. Identifying and Visualizing Common Relationships in Data
  3. New ModuleIdentifying and Visualizing Probabilistic and Statistical Relationships
  4. Using Interactive Visualizations to Explore Relationships in Data

Interpreting Data Using Statistical Models with Python

by Janani Ravi

Oct 29, 2019 / 2h 45m

2h 45m

Start Course
Description

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well.

In this course, Interpreting Data using Statistical Models with Python you will gain the ability to go one step beyond visualizations and basic descriptive statistics, by harnessing the power of inferential statistics.

First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, you will discover how the classic t-test can be used in a variety of common scenarios around estimating means. You will also learn about related tests such as the Z-test, Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances.

Finally, you will round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from hypothesis testing, including t-tests, ANOVA and regression tests in order to measure the strength of statistical relationships within your data.

Table of contents
  1. Course Overview
  2. Understanding Inferential Statistics
  3. Performing Hypothesis Testing in Python
  4. Implementing Predictive Models for Continuous Data
  5. Implementing Predictive Models for Categorical Data

Intermediate

Interpret data without models using Python by calculating common descriptive statistics for central tendency and variability.

Interpreting Data Using Descriptive Statistics with Python

by Janani Ravi

Nov 8, 2019 / 2h 20m

2h 20m

Start Course
Description

The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data.

In this course, Interpreting Data using Descriptive Statistics with Python, you will gain the ability to identify the important statistical properties of your dataset and understand their implications.

First, you will explore how important measures of central tendency, the arithmetic mean, the mode, and the median, each summarize our data in different ways. Next, you will discover how measures of dispersion such as standard deviation provide clues about variation in a single variable.

Later, you will learn how your data is distributed using skewness and kurtosis and understand bivariate measures of dispersion and co-movement like correlation and covariance.

Finally, you will round out your knowledge by implementing these measures using different libraries available in Python, like Pandas, SciPy, and StatsModels.

When you are finished with this course, you will have the skills and knowledge to summarize key statistical properties of your dataset using Python.

Table of contents
  1. Course Overview
  2. Understanding Descriptive Statistics
  3. Working with Descriptive Statistics Using Pandas
  4. Working with Descriptive Statistics Using SciPy and Statsmodels
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