Business Analyst v3.0.0

Nanodegree key: nd008

Version: 3.0.0

Locale: en-us

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

Content

Part 01 : Problem Solving with Analytics

Learn a structured framework for solving problems with advanced analytics. Learn to select the most appropriate analytical methodology. Learn linear regression.

Module 01: Welcome to the Nanodegree
- Lesson 01: Orientation
  Learn about how Udacity Nanodegree programs work, as well as get a few tips on how to successfully complete the program. Hear more about what you'll learn and do during this Nanodegree program.
- Lesson 02: Predicting Diamond Prices
  Use a predictive model to predict the prices for a large set of diamonds and provide a recommendation for a bid price.
  
  Project Description - Predicting Diamond Prices
  
  Project Rubric - Predicting Diamond Prices
Module 02: Problem Solving with Analytics
- Lesson 01: The Analytical Problem Solving Framework
  Learn a structured framework for solving problems with advanced analytics.
- Lesson 02: Selecting an Analytical Methodology
  Select the most appropriate analytical methodology based on the context of the business problem.
- Lesson 03: Linear Regression
  Build, validate, and apply linear regression models to solve a business problem
- Lesson 04: Practice Project
  Get hands on practice building a linear regression model.
- Lesson 05: Predicting Catalog Demand
  A home-goods manufacturer wants to predict expected profits from a catalog launch. You will apply a framework to work through the problem and build a linear regression model to provide results and a recommendation.
  
  Project Description - Predicting Catalog Demand
  
  Project Rubric - Predicting Catalog Demand

Part 02 : Data Wrangling

Understand the most common data types. Understand the various sources of data. Make adjustments to dirty data to prepare a dataset. Identify and adjust for outliers. Learn to write queries to extract and analyze data from a relational database.

Module 01: Data Preparation
Module 02: Selecting Predictor Variables
- Lesson 01: Selecting Predictor Variables
  Select predictor variables to be used in a predictive model.
- Lesson 02: Practice Project: Select Location of a New Petstore
  A pet store chain is selecting the location for its next store. Build a predictive model to select the best location.
Module 03: SQL Lessons
- 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.
- Lesson 08: Create Reports from a Database
  Management wants some high level metrics about their organization. You will write SQL queries to extract and analyze data from a transactions database and prepare a set of visualizations.
  
  Project Description - Create Reports from a Database
  
  Project Rubric - Create Reports from a Database

Part 03 : Data Visualization

Understand the importance of data visualization. Know how different data types are encoded in visualizations. Select the most effective chart or graph based on the data being displayed.

Module 01: Lessons

**Part 04 (Career):** Career: Job Search Strategies

Opportunity can come when you least expect it, so when your dream job comes along, you want to be ready.

Module 01: Conduct a Job Search
- Lesson 01: Conduct a Job Search
  Learn how to search for jobs effectively through industry research, and targeting your application to a specific role.
Module 02: Refine Your Resume
- Lesson 01: Refine Your Entry-Level Resume
  Receive a personalized review of your resume. This resume review is best suited for applicants who have 0-3 years of work experience in any industry.
  
  Project Description - Resume Review Project (Entry-level)
  
  Project Rubric - Resume Review Project (Entry-level)
- Lesson 02: Refine Your Career Change Resume
  Receive a personalized review of your resume. This resume review is best suited for applicants who have 3+ years of work experience in an unrelated field.
  
  Project Description - Resume Review Project (Career Change)
  
  Project Rubric - Resume Review Project (Career Change)
- Lesson 03: Refine Your Prior Industry Experience Resume
  Receive a personalized review of your resume. This resume review is best suited for applicants who have 3+ years of work experience in a related field.
  
  Project Description - Resume Review Project (Prior Industry Experience)
  
  Project Rubric - Resume Review Project (Prior Industry Experience)
Module 03: Write an Effective Cover Letter
- Lesson 01: Craft Your Cover Letter
  Get a personalized review of your cover letter. A successful cover letter will convey your enthusiasm, specific technical qualifications, and communication skills applicable to the position.
  
  Project Description - Craft Your Cover Letter
  
  Project Rubric - Craft Your Cover Letter

Part 05 : Classification Models

You will use classification models, such as logistic regression, decision tree, forest, and boosted, to make predictions of binary and non-binary outcomes.

Module 01: Lessons
- Lesson 01: Classification Problems
  Understand the fundamentals of classification modeling and how it differs from modeling numeric data
- Lesson 02: Binary Classification Models
  Build logistic regression and decision tree models. Use stepwise to automate predictor variables selection. Score and compare models and interpret the results.
- Lesson 03: Non-Binary Classification Models
  Build and compare forest and boosted models and interpret their results. Score and compare models and interpret the results.
- Lesson 04: Predicting Default Risk
  A bank recently received an influx of loan applications. You will build and apply a classification model to provide a recommendation on which loan applicants the bank should lend to.
  
  Project Description - Predicting Default Risk
  
  Project Rubric - Predicting Default Risk

Part 06 : A/B Testing

Understand the fundamentals of A/B testing, including experimental design, variable selection, and analyzing and interpreting results.

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 (Career):** Career: Networking

Networking is a very important component to a successful job search. In the following lesson, you will learn how tell your unique story to recruiters in a succinct and professional but relatable way.

Module 01: Develop Your Personal Brand
- Lesson 01: Develop Your Personal Brand
  In this lesson, learn how to tell your unique story in a succinct and professional way. Communicate to employers that you know how to solve problems, overcome challenges, and achieve results.
- Lesson 02: LinkedIn Review
  Optimize your LinkedIn profile to show up in recruiter searches, build your network, and attract employers. Learn to read your LinkedIn profile through the lens of a recruiter or hiring manager.
  
  Project Description - LinkedIn Profile Review Project
  
  Project Rubric - LinkedIn Profile Review Project
  - Concept 01: Using LinkedIn
  - Concept 02: Resources in Your Career Portal
- Lesson 03: Udacity Professional Profile
  Update and personalize your Udacity Professional Profile as you complete your Nanodegree program, and make your Profile visible to Udacity hiring partners when you’re ready to start your job search.
  
  Project Description - Udacity Professional Profile Review
  
  Project Rubric - Udacity Professional Profile Review

Part 08 : Time Series Forecasting

Understand trend, seasonal, and cyclical behavior of time series data. Use time series decomposition plots. Build ETS and ARIMA models.

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. You’ll learn how to use time series decomposition plots to visualize each of these components. Then you’ll get hands on practice building out an ETS model in Alteryx.
- 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. You’ll learn how to stationarize data through differencing, a process that prepares data for ARIMA modeling. You’ll learn the different techniques used in seasonal vs. non-seasonal data. Then you’ll get hands on practice building out an ARIMA model in Alteryx.
- Lesson 04: Analyzing and Visualizing Results
  This lesson will demonstrate how to interpret the various results from time series model. You’ll learn how to use holdout samples to compare models and select the best one for a business problem. You’ll also learn how to visualize your forecasts through various plots.
- Lesson 05: 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.
  
  Project Description - Forecast Video Game Demand
  
  Project Rubric - Forecast Video Game Demand

Part 09 : Segmentation and Clustering

Understand the difference between localization, standardization, and segmentation. Scale data to prepare a dataset for cluster modeling. Use principal components analysis (PCA) to reduce the number of variables for cluster model. Build and apply a k-centroid cluster model. Visualize and communicate the results of a cluster model.

Then complete a capstone project combining techniques learned throughout the program.

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: 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 Project
- Lesson 01: Combining Predictive Techniques
  A grocery store chain is planning a significant expansion. You will use multiple analytical techniques to provide recommendations on how to expand. After completing the project, you will feel comfortable combining predictive techniques and delivering results to complex business problems.
  
  Project Description - Combining Predictive Techniques
  
  Project Rubric - Combining Predictive Techniques

**Part 10 (Elective):** Deprecated SQL Lessons

Module 01: SQL Lessons
- Lesson 01: Data and Tables
  Learn how relational databases can structure data into tables and the importance of unique keys and relationships between tables.
- Lesson 02: Problem Set: Data and Tables
  Get warmed up with SQL by installing SQLite and working with a given database.
- Lesson 03: Elements of SQL
  Learn about some basic operations in SQL, including the SELECT, INSERT, and JOIN statements.
- Lesson 04: Problem Set: Elements of SQL
  Practice creating some basic SQL queries to answer questions.
- Lesson 05: Deeper into SQL
  Learn how to design and create new databases, normalized design, self-joins, views. You will also begin accessing databases using Python.
- Lesson 06: Problem Set: Deeper Into SQL
  Dive deeper into SQL with this problem set.

**Part 11 (Elective):** Deprecated Tableau Lessons

Module 01: Viz Lessons
- Lesson 01: Data Visualization Fundamentals
- Lesson 02: Design Principles
  Select the most effective chart or graph based on the data being displayed. Use color, shape, size, and other elements effectively.
- Lesson 03: Creating Visualizations in Tableau
  Become proficient in basic Tableau functionality, including charts, filters, hierarchies, etc. Create calculated fields in Tableau.
- Lesson 04: Telling Stories with Tableau
  Create Tableau dashboards and stories to effectively communicate data.