Description
QUESTION 1 [55 Marks]
Climate change stands as one of the most urgent challenges confronting our planet
today. To effectively comprehend and address this critical issue, access to precise and
comprehensive data regarding global temperatures and other climate-related factors is
indispensable.
In this regard, you serve as a data analyst at the National Aeronautics & Space
Administration (NASA) and are engaged in researching Earth’s climate and
temperature. Your work involves utilizing datasets sourced from satellites and groundbased sensors.
You have been entrusted with a dataset “earth_surface_temperatures .csv”
encompassing surface temperature data for various countries worldwide, spanning from
Dec 1743 to December 2020. Your mission is to conduct an in-depth Exploratory Data
Analysis (EDA) on this dataset. This analysis aims to extract insights and answer crucial
questions about the data by delving into trends and patterns.
To achieve this, you are expected to:
a. Identify and rectify any missing values in the data using appropriate techniques.
[5 Marks]
b. Transform the Years and Month columns into a single column labeled “Date” in
the MM-YYYY format, with a datetime64[ns] data type. For example, the year
1848 and month 5 should be unified as a single value, such as 5-1848. [5 Marks]
c. Detect and investigate extreme temperature values that might be regarded as
outliers. [5 Marks]
d. Compute summary statistics for temperature, monthly variation, and anomaly
values, including mean, median, standard deviation, and range. [5 Marks]
e. Identify the countries included in the dataset and calculate their average
temperature values. [5 Marks]
f. Determine the overall trend in global temperatures over the years and visualize
this trend using a suitable chart. [5 Marks]
g. Identify the months with the highest and lowest temperatures for each country
and find out whether there are noticeable seasonal patterns in the temperature
data. [5 Marks]
h. Explore the variation in temperature anomalies on a monthly basis and identify
any months with consistently high or low anomalies across the years. [5 Marks]
i. Choose five countries and compare the trends in their temperatures over the
years, seeking any similar temperature patterns. [5 Marks]
j. Explore the potential correlation between temperature and monthly variation or
anomaly values. Calculate correlation coefficients and create scatterplots to
investigate this relationship. [5 Marks]
k. Provide an intriguing insight from the dataset by utilizing data visualization
techniques such as histograms, box plots, or heatmaps to represent the data’s
distribution, trends, and relationships. [5 Marks]
QUESTION 2 [45 Marks]
As a member of the retail analytics team, you have been contacted by the Category
Manager at a retail store, who desires to gain a deeper understanding of the customers who
buy chips and their purchasing habits within the region through valuable insights that will
eventually be used to inform the store’s strategic plan for the chip category in the upcoming
six months.
You have received the following e-mail from your manager.
Greetings!
I am following up on our earlier conversation with a few pointers to help you succeed in this
task. Here are the key areas you will be working on and what we’re looking for in each one:
Firstly, examine the transaction data (“transaction_data“ file) and look for inconsistencies,
missing data, outliers, correctly identify category items, and numeric data across all tables.
If you notice any anomalies, please make the necessary changes in the dataset and save it
for further analysis. Having clean data will make it easier for us to conduct an effective
analysis.
Secondly, examine the customer data (“purchase_behaviour” file) for similar issues and
check for null values. Once you’re satisfied with the data, merge the transaction and
customer data together for analysis, ensuring that you save your files along the way.
Thirdly, conduct data analysis and identify customer segments. Define the metrics, such as
total sales, drivers of sales, and the source of the highest sales. Explore the data, create
charts and graphs, and note any interesting trends and insights you find.
Finally, deep dive into customer segments and recommend which segments we should
target. Determine if packet sizes are relative and form an overall conclusion based on your
analysis.
Here is the task:
Your task is to provide a data-driven strategic recommendation for the upcoming category
review. To achieve this, you must first analyze the current purchasing trends and behaviors
to understand the customer segments and their chip purchasing behavior. To describe the
customers’ purchasing behavior, you need to identify relevant metrics. The client has a
specific interest in understanding the chip purchasing behavior of different customer
segments.
To begin the task, download the comma-separated values (CSV) data files provided to you
and conduct preliminary data checks, including:
• Generating and interpreting high-level data summaries.
• Identifying any outliers and, if necessary, removing them (if applicable).
• Verifying the data formats and correcting them, if needed (if applicable).
In addition to the preliminary data checks, it is essential to extract additional features, such
as pack size and brand name, from the data. Defining relevant metrics of interest is also
crucial to gaining insights into the chip purchasing behavior of different customer segments.
Your ultimate goal is to formulate a strategic recommendation for the Category Manager,
based on your findings. Therefore, it is essential that your insights have a commercial
application and can be used to inform decision-making.
Lastly, a detailed report on your analysis findings, no longer than 3-4 pages, is required.
The report should include any relevant visualizations you have created, as well as your
recommendation to the Category Manager, to inform the store’s strategic plan for the chip
category. Do not include any technical aspects of your analysis, such as coding, in the
report.
Note: This is an open-ended case study and can be approached in various ways, allowing
for flexibility and creativity in the analysis process.
Additional Pointers (column description of purchase behavior):
LIFESTAGE: Customer attribute that determines if they have a family or not, and at what
stage of life they are in. For instance, it considers whether their children are in preschool,
primary or secondary school.
PREMIUM_CUSTOMER: Customer segmentation approach that distinguishes shoppers
based on the price point and product types they purchase. Its purpose is to determine
whether customers are willing to pay more for brand or quality or prefer to purchase the
most economical options.
