Solve Any Data Analysis Problem MEAP V02 1st Edition by David Asboth ISBN 9781633437531

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Product details:
ISBN 13: 9781633437531
Author: David Asboth

Solve Any Data Analysis Problem MEAP V02 1st Edition Table of contents:

1 Bridging the gap between data science training and the real world

1.1 The data analyst’s toolkit

1.2 A results-driven approach

1.2.1 Understand the problem

1.2.2 Start at the end

1.2.3 Identify additional resources

1.2.4 Obtain the data

1.2.5 Do the work

1.2.6 Present the minimum viable answer

1.2.7 Iterate if necessary

1.3 Project structure

Summary

2 Encoding geographies

2.1 Project 1: Identifying customer geographies

2.1.1 Data dictionary

2.2 An example solution: Finding London

2.2.1 Setting ourselves up for success

2.2.2 Creating the first iteration of a solution

2.2.3 Review and future steps

2.3 How to use the rest of the book

Summary

3 Data modeling

3.1 The importance of data modeling

3.1.1 Common data modeling tasks

3.2 Project 2: Who are your customers?

3.2.1 Problem statement

3.2.2 Data dictionary

3.2.3 Desired outcomes

3.2.4 Required tools

3.3 Planning our approach to customer data modeling

3.3.1 Applying the results-driven process to data modeling

3.3.2 Questions to consider

3.4 An example solution: Identifying customers from transactional data

3.4.1 Developing an action plan

3.4.2 Exploring, extracting, and combining multiple sources of data

3.4.3 Applying entity resolution to deduplicate records

3.4.4 Conclusions and recommendations

3.5 Closing thoughts on data modeling

3.5.1 Data modeling skills for any project

Summary

4 Metrics

4.1 The importance of well-defined metrics

4.2 Project 3: Defining precise metrics for better decision making

4.2.1 Problem statement

4.2.2 Data dictionary

4.2.3 Desired outcomes

4.2.4 Required tools

4.3 Applying the results-driven method to different metric definitions

4.3.1 Questions to consider

4.4 An example solution: Finding the best performing products

4.4.1 Combining and exploring product data

4.4.2 Calculating product-level metrics

4.4.3 Finding the best products using our defined metrics

4.5 Closing thoughts on metrics

4.5.1 Skills for defining better metrics for any project

Summary

5 Unusual data sources

5.1 Identifying novel data sources

5.1.1 Considerations for using new datasets

5.2 Project 4: Analyzing film industry trends using PDF data

5.2.1 Problem statement

5.2.2 Data dictionary

5.2.3 Desired outcomes

5.2.4 Required tools

5.3 Applying the results-driven method to extracting data from PDFs

5.4 An example solution: Effects of the COVID-19 lockdown periods on the film industry

5.4.1 Inspecting the available data

5.4.2 Extracting data from PDFs

5.4.3 Analyzing the data extracted from PDFs

5.4.4 Project conclusions and recommendations

5.5 Closing thoughts on exploring novel data sources

5.5.1 Skills for exploring unusual data sources for any project

Summary

6 Categorical data

6.1 Working with categorical data

6.1.1 Methods for handling categorical data

6.1.2 Working with survey data

6.2 Project 5: Analyzing a survey to understand developer attitudes toward AI tools

6.2.1 Problem statement

6.2.2 Data dictionary

6.2.3 Desired outcomes

6.2.4 Required tools

6.3 Applying the results-driven method to analyzing the developer survey

6.4 An example solution: How do developers use AI?

6.4.1 Exploring categorical data

6.4.2 Analyzing categorical survey data

6.4.3 Project progress so far

Summary

7 Categorical data: Advanced methods

7.1 Project 5 revisited: Analyzing survey data to determine developer attitudes to AI tools

7.1.1 Data dictionary

7.1.2 Desired outcomes

7.1.3 Summary of the project so far

7.2 Using advanced methods to answer further questions about categorical data

7.2.1 Binning continuous values to discrete categories

7.2.2 Using statistical tests for categorical data

7.2.3 Answering a new question from start to finish

7.2.4 Project results

7.3 Closing thoughts on categorical data

7.3.1 Skills for working with categorical data for any project

Summary

8 Time series data: Data preparation

8.1 Working with time series data

8.1.1 The hidden depth of time series data

8.1.2 How to work with time series data

8.2 Project 6: Analyzing time series to improve cycling infrastructure

8.2.1 Problem statement

8.2.2 Data dictionary

8.2.3 Desired outcomes

8.2.4 Required tools

8.3 Applying the results-driven method to analyzing road traffic data

8.4 An example solution: Where should cycling infrastructure improvements be focused?

8.4.1 Investigating available data and extracting time series

8.4.2 Project progress so far

Summary

9 Time series data: Analysis

9.1 Project 6 revisited: Analyzing time series to improve cycling infrastructure

9.1.1 Problem statement

9.1.2 Data dictionary

9.1.3 Desired outcomes

9.2 Where should cycling infrastructure improvements be focused?

9.2.1 Analysis of time series data

9.2.2 Project conclusions and recommendations

9.3 Closing thoughts: Time series

9.3.1 Skills for working with time series data for any project

Summary

10 Rapid prototyping: Data analysis

10.1 The rapid prototyping process

10.1.1 Rapid prototyping example

10.2 Project 7: Build a proof of concept to investigate Welsh property prices

10.2.1 Problem statement

10.2.2 Data dictionary

10.2.3 Desired outcomes

10.2.4 Required tools

10.3 Applying the results-driven method to investigating Welsh property data

10.4 An example solution: Building a prototype to explore using house price data

10.4.1 Analyzing data before prototyping

10.4.2 Investigating geographic aspects of a dataset

10.4.3 Identifying how to present data in the prototype

10.4.4 Project progress so far

Summary

11 Rapid prototyping: Creating the proof of concept

11.1 Project 7 revisited: Building a proof of concept to investigate Welsh property prices

11.1.1 Data dictionary

11.1.2 Desired outcomes

11.1.3 Project summary so far

11.2 Building a proof of concept

11.2.1 Preparing to build a proof of concept

11.2.2 Using streamlit to build a proof of concept

11.2.3 Project outcomes and next steps

11.3 Closing thoughts on the rapid prototyping of ideas

11.3.1 Skills for rapid prototyping for any project

Summary

12 Iterating on someone else’s work: Data preparation

12.1 Finding similar entities

12.2 Continuing someone else’s work

12.3 Project 8: Finding customer segments from mobile activity

12.3.1 Problem statement

12.3.2 Data dictionary

12.3.3 Desired outcomes

12.3.4 Required tools

12.4 Applying the results-driven method to creating the second iteration of a customer segmentation

12.5 An example solution: Creating customer segments

12.5.1 Recreating someone else’s analysis

12.5.2 Analyzing event data to learn about customer behavior

12.5.3 Project progress so far

Summary

13 Iterating on someone else’s work: Customer segmentation

13.1 Project 8 revisited: Finding customer segments from mobile activity

13.1.1 Data dictionary

13.1.2 Desired outcomes

13.1.3 Project summary so far

13.1.4 Segmentation of mobile users using clustering

13.1.5 Conclusions and next steps

13.2 Closing thoughts: Segmentation and clustering

13.2.1 Skills learned to use for any project

Summary

appendix Python installation instructions

A.1 Installing Python

A.2 Installing poetry

A.3 Creating your virtual environment

index

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Tags: David Asboth, Data Analysis Problem, MEAP V02