PROJECT: TIME SERIES ANALYSIS OF NAICS

A Data Analyst is someone who munges information using data analysis tools. The meaningful
results they pull from raw data help their employers or clients make important decisions by
identifying various facts and trends. The North American Industry Classification System
(NAICS) is an industry classification system developed by the statistical agencies of Canada,
Mexico and the United States. NAICS is designed to provide common definitions of the
industrial structure of the three countries and a common statistical framework to facilitate the
analysis of the three economies.

For this task, consider yourself a newly employed Data Analyst. The head of your department at
your company has given you the following instructions:


1. Prepare a data set using the following files:

	a. NAICS 2017 – Statistics Canada: Description of the North American Industry
Classification System (NAICS). All you would need to understand for this task is, how the
NAICS works as a hierarchical structure for defining industries at different levels of
aggregation. For example (see page 22), a 2-digit NAICS industry (e.g., 23 - Construction) is
composed of some 3-digit NAICS industries (236 - Construction of buildings, 237 - Heavy
and civil engineering construction, and a few more 3-digit NAICS industries). Similarly, a
3-digit NAICS industry (e.g., 236 - Construction of buildings), is composed of 4-digit
NAICS industries (2361 - Residential building construction and 2362 - Non-residential
building construction).
	b. Raw data: 15 CSV files beginning with RTRA. These files contain employment data by
industry at different level of aggregation; 2-digit NAICS, 3-digit NAICS, and 4-digit NAICS.
Columns mean as follows:
(i) SYEAR: Survey Year
(ii) SMTH: Survey Month
(iii) NAICS: Industry name and associated NAICS code in the bracket
(iv) _EMPLOYMENT_: Employment
	c. LMO Detailed Industries by NAICS: An excel file for mapping the RTRA data to the
desired data. The first column of this file has a list of 7 industries that are frequently used.
The second column has their NAICS definitions. Using these NAICS definitions and RTRA
data, you would create a monthly employment data series from 1997 to 2018 for these 7
industries.
	d. Data Output Template: An excel file with an empty column for employment. You should
fill the empty column with the data you prepared from your analysis.
	e. Take note of the following: (i) The industry names in the ‘LMO Detailed Industries by
NAICS’ match with the industry names in the ‘Data Output Template’. The RTRA data
should be used based on the NAICS codes, not by industry names.
(ii) Try to create each series from the highest possible level of aggregation in the raw data files.
For example, if an LMO Detailed Industry is defined with a 2-digit NAICS only, do not use a
lower level of aggregation (i.e., 3-digit or 4-digit level NAICS files in the RTRA). Similarly,
if an LMO Detailed Industry is defined with a 3-digit NAICS only, do not use the 4-digit
NAICS files for that industry.
(iii) All steps, including merging or appending the data, that would generate the requested data
should be done using python codes.
(iv) The source for the data is: Real Time Remote Access (RTRA) data from the Labour Force
Survey (LFS) by Statistics Canada.

2. Highlight at least 5 important questions that you would like to answer in order to provide
valuable information to your company so that they can take good business decisions. Your
questions should include these:
i) How employment in Construction evolved over time, compared to the total
employment across all industries? and at least 4 more questions.

3. Use visualizations to illustrate how data driven decisions can be made for unseen data such as 2022 or 2023 data.