Day 2 — Foundations Continued…
Did the details of the case study help to change the way you think about data analysis? Why or why not?
- Initially, my thoughts on data analysis wasn’t very detailed anyway — therefore the details of the case study expanded my scope if anything. I discovered the thought process of a data analysis — I drew comparisons to the ‘scientific method’. The details gave me some samples of some effective questions to ask during the process of figuring out a problem, in this case, why retention is difficult for this company — which is quite common globally.
Did you find anything surprising about the way the data analysts approached their task?
- Surprising, no — as I know analytics do require tedious work. However I did learn how inclusive the data has to be and how being ethically plays a huge part in analysis. Manipulation will happen, therefore its imperative we provide ‘clean’ data, I’m assuming.
What else would you like to learn about data analysis?
- Unfortunately, I don’t know what I don’t know. I am excited and curious to learn more about how to acquire clean data and the processes data analyst go through daily.
Video — Cassie: Dimensions of Data Analytics
Decision Intelligence is a combination of applied data science and the social and managerial sciences.
Advice: Pick specialization best suits your personality.
Data science encompasses 3 disciplines:
Machine learning, statistics, and analytics
Which one most fits me according to Cassie: Statistics.
The excellence of statistics is rigor. Statisticians are essentially philosophers, epistemologists.They are very, very careful about protecting decision-makers from coming to the wrong conclusion.If that care and rigor is what you are passionate about,I would recommend statistics.
Video — What is the data ecosystem?
Ecosystem — group of elements that interact with one another (think Apple).
Data ecosystems are made up of various elements that interact with one another in order to produce, manage, store, organize, analyze and share data.
The cloud plays a big part in the data ecosystem, and as a data analyst, it’s your job to harness the power of that data ecosystem, find the right information, and provide the team with analysis that helps them make smart decisions.
Data science is defined as creating new ways of modeling and understanding the unknown by using raw data. Data scientists create new questions using data, while analysts find answers to existing questions by creating insights from data sources.
Video: How data informs better decisions
Data-driven decision-making is defined as using facts to guide business strategy.
First step: Figuring out the business need.
Whatever the problem is, once it’s defined, a data analyst finds data, analyzes it and uses it to uncover trends, patterns and relationships.
Whats worked in the past?
Data alone will never be as powerful as data combined with human experience, observation, and sometimes even intuition.
READING: Data and gut instinct
Analysts use data-driven decision-making and follow a step-by-step process. You have learned that there are six steps to this process:
Ask questions and define the problem.
Prepare data by collecting and storing the information.
Process data by cleaning and checking the information.
Analyze data to find patterns, relationships, and trends.
Share data with your audience.
Act on the data and use the analysis results.
Why gut instinct can be a problem
Gut instinct may be biased
Blending data with business knowledge, plus maybe a touch of gut instinct, will be a common part of your process as a junior data analyst.
Try asking yourself these questions about a project to help find the perfect balance:
What kind of results are needed?
Who will be informed?
Am I answering the question being asked?
How quickly does a decision need to be made?
Rush projects — may need to rely on own knowledge and experience. When time is on your side, decision can be more data driven.
Origins of the data analysis process
Data analysis life cycle — the process of going form data to decision. Created, consumed, tested, processed, and reused.
Ask: Business Challenge/Objective/Question
Prepare: Data generation, collection, storage, and data management
Process: Data cleaning/data integrity
Analyze: Data exploration, visualization, and analysis
Share: Communicating and interpreting results
Act: Putting your insights to work to solve the problem
How we analyze data will vary from company to company and will continue to evolve.
