Week 5 is broken down into 5 sections

• Data analyst job opportunities
• The importance of fair business decisions
• Optional: Exploring your next job
• Weekly Challenge 5
• Course challenge

Data analyst job opportunities

The 1^st section of data analyst job opportunities starts out with motivational incentives to move forward in the program as well as a few testimonials from individuals that contribute data analytics to their success.  For any individual that is self-motivated, this section provides very little insight and is more of a feel-good motivator.  Some of the examples used in the videos seem to be recycled tropes, such as a business overstaffing on rainy days when less visitor traffic occurs. Instead of watching weather forecasts and making adjustments to the schedule, the problem was solved by the insights provided by the data analyst. 

The importance of fair business decisions

This section follows the train of thought that even statistical numbers based on facts can be biased.   An example used here is a company comprising of men who hired a woman and then uses statistics to show how men outperform women in their company.  Of course, this is going to be very biased because the single woman in this data set is the statistical outlier of the sample that differs significantly from the rest of the observation. “Sometimes we encounter datasets where outliers are extreme that they completely bias the results of an analysis. If the outliers are not removed on such occasions, it could lead to a false and misleading analysis” (S. Manthani, 2020). Insensitivity to sample size, which in this case is the single woman, is a cognitive bias that occurs when people judge the probability of obtaining a sample statistic without respect to the sample size (Wikipedia).

This reminds me of how to lie with statistics

Optional Exploring your next job

This section could have been fleshed out a little more but what I did like was searchable job titles. Just go onto your favorite job posting site and look up any of the below jobs to find what current employers are looking for in their next candidate.

• Business analyst — analyzes data to help businesses improve processes, products, or services
• Data analytics consultant — analyzes the systems and models for using data
• Data engineer — prepares and integrates data from different sources for analytical use
• Data scientist — uses expert skills in technology and social science to find trends through data analysis
• Data specialist — organizes or converts data for use in databases or software systems
• Operations analyst — analyzes data to assess the performance of business operations and workflows
• Marketing analyst — analyzes market conditions to assess the potential sales of products and services 
• HR/payroll analyst — analyzes payroll data for inefficiencies and errors
• Financial analyst — analyzes financial status by collecting, monitoring, and reviewing data
• Risk analyst — analyzes financial documents, economic conditions, and client data to help companies determine the level of risk involved in making a particular business decision
• Healthcare analyst — analyzes medical data to improve the business aspect of hospitals and medical facilities

In this section, a Google recruiter, Samah, gives advice to potential candidates.

• Have in mind and ability to converse about a time when you’ve used data to solve a problem, whether it’s in your professional or personal projects. 
• Increase your professional network. One way is to increase your online footprint, by reaching out to other analysts on LinkedIn or joining local meet-ups with other data scientists. 
• “Sometimes when we’re looking for a unique skill set, recruiters are going on websites like LinkedIn, and GitHub, and trying to find that talent themselves. It’s really important to have your LinkedIn updated along with websites like GitHub, where you can showcase a lot of the data analysts projects you’ve done” 
• Prepare questions for the interviewer. 
• You might be given a case study in an interview.  Expect to be given a business problem along with the sample data set. Take that sample data set, analyze it, and come up with a solution. Ensure you are analyzing the data and coming up with a solution that relates back to that data. 
  • Sometimes there is no right answer, and a lot of times interviewers are looking to see your thought process and the way you get to your solution.
• After applying for a role you’re interested in look for the hiring manager online. See if you can reach out to them and set up a coffee chat or send them your resume directly. Online applications could be a really big black hole where you never hear back from the recruiter or the team. When you reach out directly to a hiring manager or recruiter, it really shows your eagerness for the role and your interest in the role. 

Course challenge

The test consisted of 10 questions which are a lot harder than any other weekly challenge, but you are given 50min to complete. Though not overly tough if you paid attention and read all the required learning material.  Upon completion of this first course, you are congratulated and given a link to sign up for the next course Ask Questions to make Data-Driven Decisions.

Week 5 Glossary of Terms

A

Analytical skills: Qualities and characteristics associated with using facts to solve problems

Analytical thinking: The process of identifying and defining a problem, then solving it by using data in an organized, step-by-step manner

Attribute: A characteristic or quality of data used to label a column in a table

B

Business task: The question or problem data analysis resolves for a business

C

Context: The condition in which something exists or happens

D

Data: A collection of facts

Data analysis: The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making

Data analyst: Someone who collects, transforms, and organizes data in order to draw conclusions, make predictions and drive informed decision-making

Data analytics: The science of data

Data design: How information is organized

Data-driven decision-making: Using facts to guide business strategy

Data ecosystem: The various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data

Data science: A field of study that uses raw data to create new ways of modeling and understanding the unknown

Data strategy: The management of the people, processes, and tools used in data analysis

Data visualization: The graphical representation of data

Database: A collection of data stored in a computer system

Dataset: A collection of data that can be manipulated or analyzed as one unit

F

Fairness: A quality of data analysis that does not create or reinforce bias

Formula: A set of instructions used to perform a calculation using the data in a spreadsheet

Function: A preset command that automatically performs a process or task using the data in a spreadsheet

G

Gap analysis: A method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future

O

Observation: The attributes that describe a piece of data contained in a row of a table

Q

Query: A request for data or information from a database

Query language: A computer programming language used to communicate with a database

R

Root cause: The reason why a problem occurs

S

Stakeholders: People who invest time and resources into a project and are interested in its outcome

T

Technical mindset: The ability to break things down into smaller steps or pieces and work with them in an orderly and logical way

V

Visualization: (Refer to data visualization)

References

S. Manthani. 2020. Bias is not always intentional. https://sathish-manthani.medium.com/bias-is-not-always-intentional-fd6fcc8610c0#:~:text=Sometimes%20we%20encounter%20dataset%20where,it%20is%20to%20find%20outliers).

Wikipedia. 2022 Jan 7. Insensitivity to sample size. https://en.wikipedia.org/wiki/Insensitivity_to_sample_size