Predictive Analytics for Business Nanodegree v2.0.0

Nanodegree key: nd008t

Version: 2.0.0

Locale: en-us

Learn to clearly define business issues, prepare and clean data, and implement a variety of predictive modeling techniques.

Content

Part 05 : Career Support

LinkedIn is widely used by tech recruiters to find and evaluate job candidates. It's also where you can meet interesting people in your field. Get personalized feedback on how to improve your profile! (You may elect to opt out of this project.)

Module 01: Career Support
- Lesson 01: Take 30 Min to Improve your LinkedIn
  Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile

Part 06 : A/B Testing

Module 01: A/B Testing
- Lesson 01: A/B Testing Fundamentals
  Understand the fundamentals of A/B testing, including selecting target and control units and variables and the duration of a test.
- Lesson 02: Randomized Design Tests
  Select test and control variables and understand the importance of sample size. Design a randomized design A/B test and analyze the results.
- Lesson 03: Matched Pair Design Tests
  Match test units to control units. Design a matched pair design A/B test and analyze the results.
- Lesson 04: Matched Pair Practice
  Use trend and seasonality as control variables for a matched pair design A/B test.
- Lesson 05: A/B Test a New Menu Launch
  A chain of coffee shops is considering launching a new menu. You will design and analyze an A/B test and write up a recommendation on whether the chain should introduce the new menu.
  
  Project Description - A/B Test a New Menu Launch
  
  Project Rubric - A/B Test a New Menu Launch

Part 07 : Time Series Forecasting

Module 01: Time Series Forecasting
- Lesson 01: Fundamentals of Time Series Forecasting
  In this lesson you’ll learn what attributes make data a time series. You’ll also learn the key components used in time series forecasting, such as seasonality, trends, and cyclical patterns.
- Lesson 02: ETS Models
  In this lesson you’ll learn how to build and use ETS models. ETS stands for error, trend, and seasonality, and are the three inputs in ETS models.
- Lesson 03: ARIMA Models
  In this lesson you’ll learn how to build and use ARIMA models. ARIMA stands for autoregressive, integrated, moving average, which are the inputs for ARIMA models.
- Lesson 04: Analyzing and Visualizing Results
  This lesson will demonstrate how to interpret the various results from time series model.
- Lesson 05: Practice Project Forecast Video Game Sales
  A video game producer is planning production levels. You will use time series forecasting models to forecast monthly demand and provide a recommendation to help match supply to demand.

Part 08 : Segmentation and Clustering

Module 01: Lessons
- Lesson 01: Segmentation Fundamentals
  Understand the difference between localization, standardization, and segmentation
- Lesson 02: Preparing Data for Clustering
  Scale data to prepare a dataset for cluster modeling. Select variables to include based on the business context.
- Lesson 03: Variable Reduction
  Use principal components analysis (PCA) to reduce the number of variables for cluster model
- Lesson 04: Clustering Models
  Select the appropriate number of clusters. Build and apply a k-centroid cluster model.
- Lesson 05: Validating and Applying Clusters
  Validate the results of a cluster model. Visualize and communicate the results of a cluster model.
- Lesson 06: Data Visualizations in Tableau
  In this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!
- Lesson 07: Segmentation Practice Project
  A retail store chain wants to expand to other countries. You will build a clustering model to segment countries to determine which countries are most similar to the U.S. in order to determine the best
Module 02: Capstone
- Lesson 01: Combining Predictive Techniques
  After completing the project, you will feel comfortable combining predictive techniques and delivering results
  
  Project Description - Combining Predictive Techniques
  
  Project Rubric - Combining Predictive Techniques

**Part 09 (Elective):** SQL Lessons

Module 01: SQL
- Lesson 01: Basic SQL
  In this section, you will gain knowledge about SQL basics for working with a single table. You will learn the key commands to filter a table in many different ways.
- Lesson 02: SQL Joins
  In this lesson, you will learn how to combine data from multiple tables together.
- Lesson 03: SQL Aggregations
  In this lesson, you will learn how to aggregate data using SQL functions like SUM, AVG, and COUNT. Additionally, CASE, HAVING, and DATE functions provide you an incredible problem solving toolkit.
- Lesson 04: SQL Subqueries & Temporary Tables
  In this lesson, you will be learning to answer much more complex business questions using nested querying methods - also known as subqueries.
- Lesson 05: SQL Data Cleaning
  Cleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.
- Lesson 06: [Advanced] SQL Window Functions
  Compare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.
- Lesson 07: [Advanced] SQL Advanced JOINs & Performance Tuning
  Learn advanced joins and how to make queries that run quickly across giant datasets. Most of the examples in the lesson involve edge cases, some of which come up in interviews.

**Part 10 (Elective):** Tableau Lessons

Module 01: Data Visualization

Content

Part 01 : Welcome to the program

Part 02 : Problem Solving with Analytics

Part 03 : Data Wrangling

Part 04 : Classification Models