Predictive Data Analytics with Python

In this course trainees learn how to read, clean, visualize and analyze data effectively using Python and its powerful free libraries Pandas, Seaborn, Scipy, Numpy, Matplotlib, and Statsmodels. They also learn how to interpret the results and use them to make predictions.

Note: This course is only available as part of NCLab’s Data Analyst Career Training program.

Course Overview

The course focuses on using Python libraries to solve practical applications rather than on the underlying math concepts. Trainees learn enough statistical and analytical concepts and procedures in the tutorials to use these libraries effectively. This foundation is invaluable, whether trainees continue to use free Python libraries for analysis and visualization in their own work, or move on to a commercial analytics/visualization product specific to their industry. Trainees learn how to use Python and its powerful free libraries including Pandas, Numpy, Scipy, Matplotlib, Seaborn, and Statsmodels to read data from files, clean data, present data in visual form, perform qualitative and quantitative analysis of data, interpret data, and make predictions.

Prerequisites

This course has Python Fundamentals as a prerequisite.

Student Learning Outcomes (SLO)

Students will be able to:

  • Import data from CSV files and clean data.
  • Enter data into Pandas data frames, manipulate data frames.
  • Calculate variance and standard deviation.
  • Visualize data using scatterplots.
  • Use Seaborn to display the regression and residual plots.
  • Discuss the residual plot as part of regression analysis.
  • Make predictions based on the results of simple linear regression analysis.
  • Display boxplots with Seaborn and interpret them.
  • Plot histograms with Pandas.
  • Calculate the Pearson coefficient of correlation R (R-value).
  • Visualize correlation graphically via heatmaps.
  • Describe the purpose of using the R-squared value, and its advantages over the R-value.
  • Distinguish between continuous, discrete and categorical random variables.
  • Calculate the P-value and the standard error of the estimate using Scipy and interpret the results.
  • Interpret data using the statistical hypothesis testing and the null hypothesis.
  • Use training and testing datasets to make predictions.
  • Perform multiple linear regression using Statsmodels.
  • Make predictions based on multiple linear regression.
  • Evaluate variable independence in multiple linear regression based on multicollinearity.
  • Perform logistic regression with Seaborn and Statsmodels.
  • Recognize when the results of logistic regression are wrong.

Equipment Requirements

Computer, laptop or tablet with Internet access, email, and one of the following browsers:

  • Google Chrome
  • Mozilla Firefox
  • Microsoft Edge
  • Safari

Course Structure and Length

The course is divided into four Units. Each Unit consists of seven instructional/ practice levels, a quiz, and a master (proficiency) level. Trainees can return to any level or quiz for review. The course is self-paced, and trainees will practice each skill or concept as they go. Automatic feedback is built into the course for both practices and quizzes.

While learning the skills in Predictive Data Analytics with Python, trainees can practice skills and create portfolio artifacts with NCLab’s Python apps. They can use a project idea from NCLab or create their own. This independent practice develops their fluency and confidence as data analysts and programmers.

The time to complete this course is approximately 80 hours. Since the course is self-paced, the amount of time required to complete the course will vary from person to person. Trainees are responsible for learning both the tutorial content and the skills acquired through practice.

At the end of this course, trainees will complete a Capstone Project under the supervision of an NCLab senior Data Analytics instructor in order to graduate and obtain a career certificate.