Why learn data analysis?
Irrespective of the industry or the role you are in, being able to analyse data and present your findings will make you far more productive.
Even if your role doesn’t need you to work with data teams, having some data skills will help you make more informed decisions.
Digital skills are the shared language of the modern economy. Not every worker needs to learn how to code, but every worker needs to be literate in digital skills. Three-quarters of workers in a recent survey said that they felt unprepared for jobs in the digital-first economy.
These recommendations are for people who don’t write code yet. But have used Excel or Google Sheets. They are also open to learning advanced Excel functions e.g formulas, Macros and dabbling a bit in SQL, Python and R.
What courses do I need to take?
Any data analytics project life cycle generally has the following steps.
In this post, we will recommend one (or two) courses for each step. You should take them in the recommended order.
1. Understand - Ask the right business question
You might have seen the following Venn diagram by Drew Conway.
While this diagram was intended for Data Science (the cooler cousin of Data Analytics), the idea is not too different.
Arguably, the most important part of any data project is asking the right questions. This requires you to be aware of the domain you are in.
Whether you work for an organization in the Oil & Gas industry, Retail or Public Sector, etc. - learn about the industry.
Recommendation #1: Google’s Foundations: Data, Data, Everywhere
Learning Objectives
Gain an understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job.
Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox.
Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process.
Recommendation #2: Ask Questions to Make Data-Driven Decisions
Learning Objectives
Learn about effective questioning techniques that can help guide analysis.
Gain an understanding of data-driven decision-making and how data analysts present findings.
Explore a variety of real-world business scenarios to support an understanding of questioning and decision-making.
2. Data - Gather and Clean
One issue with online tutorials/courses is that they use a clean dataset.
Data cleaning can be deeply frustrating. Why are some of your text fields not being displayed and read correctly? Why are there non-ASCII characters in the dataset? What should you do about the missing values? Is this date column formatted incorrectly? Are there data entries inconsistent?
Real-world data is (almost) never clean. There would always be issues with the acquisition process.
Recommendation #3: Master Course in Tableau Prep - Prepare & Clean Data
Learning Objectives
Getting the must-know basics out the way quickly
Learn the functionalities of Tableau Prep, enabling you to use all of Tableau Preps functionalities
This course is taught using Tableau Prep.
Tableau Prep is a no-code tool that helps with data preparation. You can download the 14 days without restrictions trial version. If you are a student you can get it bundled with Tableau Desktop for a year.
Irrespective of the tool, the goal should be to learn about the general steps involved in data cleaning.
3. Analyse
Once you have the dataset, you need to analyse it. Data analysis is the practice of working with data to extract insights, which can then be used to make informed decisions.
Recommendation #4: Introduction to Data Analysis Using Excel
This is a project-based course i.e you learn about Excel functions while working on a use case related to a store’s sales data.
4. Present - Convey findings
Recommendation #5: Data Visualization with Advanced Excel
About this course
This course is all about presenting the story of the data, using PowerPoint. You'll learn how to structure a presentation, to include insights and supporting data. You'll also learn some design principles for effective visuals and slides. You'll gain skills for client-facing communication - including public speaking, executive presence and compelling storytelling. Finally, you'll be given a client profile, a business problem, and a set of basic Excel charts, which you'll need to turn into a presentation - which you'll deliver with iterative peer feedback.
Being able to succinctly present your ideas is one of the superpowers in the workplace. You should know how to write well and create presentations that convey your ideas.
Here’s some advice on writing by Computer Scientist Paul Graham Co-Founder & Partner @ Y Combinator
I think it's far more important to write well than most people realize. Writing doesn't just communicate ideas; it generates them. If you're bad at writing and don't like to do it, you'll miss out on most of the ideas writing would have generated.
Source: Writing, Briefly
5. Document: Make Projects Reproducible
Reproducibility is important to reduce the bus factor. It is a (dark) humourous way of saying that if a certain number of people on a project were hit by a bus, would the project still survive?
You want to document every step of the project.
Recommendation #6: Excel Modeling for Professionals: Best Practices & Pitfalls
About this course
What sets this Excel course apart is that we don’t focus on quick fixes or specific tips and tricks. This course is for those who already understand Excel. The aim of the course is to elevate Excel models, to improve clarity, longevity, and transferability of these models, and to reduce mistakes and to encourage consistency in businesses as they work with Excel.
Through this Excel course, learners will gain the tools to decide whether or not to use Excel to solve their problem. They will learn how to set up good input data, format correctly, and the importance of good documentation.
Would you like to discuss this learning pathway or create a customized learning pathway for yourself?