Welcome to CodeClan and the introduction to your pre-course work! The aim of the pre-course work is to prepare you for the intensive 14-week Data Analysis course.
This means by the end of the next two weeks, you should:
We’ll check-in with you a few times during the two weeks, but if you have a big blocker stopping you from making progress or any questions, don’t hesitate to get in touch.
The pre-coursework resources are not the course materials that you will use during the 14 weeks. Instead, we have compiled resources we feel will give you exposure to topics and technologies that we will cover on the course in more detail. Learn as much of the terminology as you can before you start the course, as class is very immersive and it will help you in the first few weeks. When completing the pre-coursework, the following is the order that you will tackle the content in:
Practicing your typing is very important. An ideal minimum typing speed by the end of the two weeks would be 40-50 words per minute as a normal typing speed. Class can be quite fast paced at times, so to ensure you can keep up practicing your typing is essential. After all, practice makes perfect! You should test your typing speed and send the results to learning support before beginning the pre-coursework (see info below). If you are having any problems please let an instructor know: we will provide help and resources so you can improve your typing speed.
Your computer is going to be the tool of your trade, so it’s essential that you start becoming comfortable using it. Familiarise yourself with the software you will use on the course (if you are not already), and challenge yourself to learn a keyboard shortcut each day.
There are a number of languages used for data science and data analysis. During the course we will introduce you to three languages: R, SQL and Python. Most of the programming you do in the course will be in R, so we will introduce you to it in the pre-coursework. R is an open source language, which means it is free to use and you can take it with you into any role at the end of the course. We will use Swirl, a tutorial package available within R, to help you get familiar with both the syntax of the language and RStudio (the tool you will be using to write your code).
All the work you do as an analyst requires careful management to ensure reproducibility. We use tools and utilities to help us manage these files. This gives us a safety net of backups, makes sharing with colleagues easier and helps us speed up analyses. There are many options, but we will use a program called Git. Your laptop will have it installed.
The level of maths occurring on the course is roughly equivalent to Standard Grade, GCSE, O-level, Intermediate 2 or National 5. Ideally, you should be OK with:
However, don’t worry though if some of the topics are unfamiliar: you’ll pick them up as you go along, and we can help you build these skills. Also, you don’t need to memorise how to do these: you will always have access to reference material. General numeracy, interest and a willingness to learn are the most important skills!
While you don’t need a degree in statistics to become a data analyst, understanding statistics is an important part of many roles. We will study statistics, and the theory of probability underpins statistics, so we would like you to have a reasonable understanding of probability by the end of the course. Don’t worry, we will guide you through everything you need to know, but we include some links to material in the pre-coursework to give you an introduction.
The ability to critically assess numerical data and plots is important, and healthy skepticism is the most powerful tool you can develop as a data analyst. Some of the pre-coursework includes links to get you thinking about data visualisation, interpreting statistics in a meaningful way, and how to skeptically interpret data and plots.
Data science is a vast and varied field, but it is still relatively new and the terminology is still growing and changing. The pre-coursework provides a summary of commonly used terms and some views on the breadth and importance of data and statistics.
The pre-coursework needs to be completed and we will check-in with you a few times during the two weeks to see how you are doing and to provide help with any queries or problems. At the end of each week, you need to complete the relevant checklists below so that we can ensure you’re on track:
If you have any issues, responsibilities or commitments that mean you might struggle to complete the pre-coursework, you need to contact us to let us know and we can offer extra support and guidance.
You will use Slack during the course: this is a messaging service we use at CodeClan for communication between students and staff. Whilst completing the pre-coursework, we encourage you to ask your fellow classmates questions on Slack. The benefits are:
We will send you a link to Slack just before Meet Your Cohort and give you an intro on the day. You can then use this throughout your pre-coursework if you have any problems.
Below is a list of resources that you need to use to complete the pre-coursework: what you need to do, and when you should do it by. The pre-coursework is designed to take up the two weeks before the main course starts. However, we understand that everyone works at different speeds and may have different home/work responsibilities. Please let the learning support staff and instructors know if you have any problems.
A moderate typing speed is important to follow along with the codealongs in class. Please send a screenshot of your normal typing speed as soon as possible in week 1 pre-coursework to email@example.com. The latest you can send this is Friday of week 1 (pre-coursework).
If you are using Windows:
R is the core language we teach during the course (although we will also cover SQL and Python at points). In order to get you up to speed with R, we ask that you complete the following:
Understand the basics of source code version control and why it is used. Don’t worry if you still feel uncertain about git after completing this section, we have lessons covering this on the course.
Learning to code is hard – we know this, and we appreciate that your first introduction to R might seem overwhelming. We also know that returning to a classroom environment is hard, as is deciding to change careers. In terms of supporting your own learning, we suggest that you download this free e-book which steps through the learning cycle you’ll go through as you learn to code:
We also suggest that you keep a note of any of the content you found hard: first, to track topics you might need to focus on more during the course; and second, so that we can gauge areas in which you might need additional help.
If you haven’t done so already, create a login for yourself on Khan Academy.
The maths on the course is not something to worry about. We understand most people will not have a background in maths or statistics, and that you may not have been in a maths classroom in a long time! We will walk you through statistical concepts that are relevant to the course in more detail. However, the following links will provide you with a good refresher (or starting point, if it’s all new to you).
You don’t have to be a statistician to be a data analyst, but you do have to understand how to use and interpret statistics. This pre-coursework will guide you through an intro to statistics, and the core concept they are grounded on: probability.
Don’t worry if you find any of this learning tough – we will revisit the concepts introduced here throughout the course! Keep your focus more on understanding broad concepts than on following all of the mathematical detail. A detailed understanding will come when you start manipulating and analysing data for yourself.
At the end of each video, try to summarise the contents in a series of mental or written ‘bullet points’. Remember to take frequent breaks!
If you finish the mandatory pre-coursework you may want to have a look at these extension exercises to prepare yourself further.
If you have time, you’ll find these resources helpful: