1/3 Start from scratch : choosing a personal project 🎯
Hi everyone,
My name is Guilhem, and I am passionate about Data Analysis and its numerous opportunities for innovation, making data relevant but also impactful for the ones who will have to use it to meet strategic decisions. Along this newsletter, I will tackle subjects you will be interested in for working in this area from a business perspective.
⏱ Reading time: 2 min
1. How to choose a good portfolio project? 💡
I will tell you first, there is no good or bad portfolio project as long as it is consistent and relevant for bringing your skills to the next level. Here are some questions you better answer when leading your project :
1.1 Ask you (an interesting question) ❓
In which topic are you interested? You will invest your time and all the freest.
Which aspect would you want to learn? In which technology do you want to improve your skills?
It can be technical or literacy or both. Being a good analyst makes relevant data easily understandable and interpretable for your end user. Define your learning goals and select tools and technologies that align with those goals.
Ex: I want to know how to get data from a public API and build a dataset from it to perform a statistical analysis and some visualization to explore the data and draw patterns.
1.2 Which tool do I have to choose to perform my analysis 🛠
Depending on what the goal of your project is, you have to take into account :
Which technologies are you comfortable with?
What do you want to deploy for implementing your end-to-end project?
Each technology has its own specificities and framework. As you are willing to become a future data nerd, specific tools and programming languages are required for performing well in your future position.
Overview of Technologies listed in Data Jobs
Depending on your development ambition, the skills required will be different. However, there is a starter kit to master in order to gain competence and ease in understanding projects and their management in a professional context.
As a Data Analyst, you want to make the bridge between business and technical fields, so learn the basics for understanding the basics in both.
👉 SQL and Excel are your foundations, don’t skip them 🏠
👉 Tableau and Power BI are the most used data visualization software products in companies, learning how to use them will give you data visualization and literacy sensitivity. 👁
👉 Python and R are good to explore and visualize data in notebooks. Both will help you to make your homemade projects and implement tons of automation in your company 💪
2. Learn the basics of your project 🏠
2.1 Mindset: Quick and dirty 🧹
Are you new to python, R, or any programming language you wish to learn? Good! Being project-oriented will definitely boost your in-depth learning as you are solving real business issues. That is why, after learning the basics to boot camps, try to implement your recently acquired knowledge in guided projects available in open platforms such as Kaggle or Data camp with already provided datasets.
Your projects are here to demonstrate specific types of skills.
By limiting the scope of your projects, you will clearly cover the different aspects of your technical skill set.
The more you will acquire knowledge in the topic you want to specialize in, the more you will have enough confidence to tackle cross-domain projects you never thought about before.
Yes, indeed, self-projects require as much learning as real projects you will face in enterprise and even more because you are free to experiment with as many topics as you want to cover.
2.2 Get data (the simplest way you can start) ⚡️
Once you have determined the topic and goals of your project, you will need data to work with. The simplest way to get started is to find publicly available datasets online. The second option is to make your own homemade Database. This option is a bit tricky to implement if you are a data novice, nevertheless, you will understand what an end-to-end project implies: 90% of your time aggregating and cleaning data and 10% percent the funny part.
The resources you can begin with and save you time and effort :
👉 Kaggle (Please Make you a favor don’t go for Titanic either Iris)
3. Create your personal branding ®
Now that you have built your portfolio project and acquired new skills, it is important to showcase your work to potential employers. Create a personal website that highlights your projects, skills, and achievements. Make sure to include your contact information and a professional-looking photo. Additionally, consider creating a LinkedIn profile and sharing your work on social media platforms to increase your visibility in the industry.
3.1 Communicate about your successes 🗣
Don't be shy about sharing your successes with your network. Share your portfolio projects, certifications, and any other achievements on LinkedIn and other social media platforms. Networking is an essential part of building a successful career in data and analytics as much as other topics, and sharing your successes can help you connect with potential employers and collaborators.
3.2 Certify yourself 👩🎓
To further enhance your credentials, consider obtaining certifications in the technologies and skills you have acquired as the first way to speak about your achievements in public. Tons of MOOCs, online certifications, and training for them are available on the web at costless prices or even free. Investing in yourself is not a must-have but a requirement for achieving your goals of tomorrow, so do you a favor and buy this course at $9.9.
Here for starting with good basics, a list of reliable self-learning websites :
Best tracks and resources to start with
👉 DataCamp
YouTube Channels I personally recommend for keeping you up to date :
By the way, I am thinking about providing you with additional content like step-by-step tutorial books for Data Analysis with all the resources you need for boosting your start of a career as a junior free. Will you be interested?
In a brief ⚡️
Start with the basics but don’t put too much effort into covering the whole subjects
Apply directly your skills with a side project you will work on with an interesting question
Don’t be ashamed to develop dirty code, practice makes perfect
Perform your first analysis on an already prepared dataset, then diversify your data sources
The more you will speak about a subject you are passionate about, the more you will gain confidence about it: good news, it also applies to data.
Stay in touch next email will give you all the resources you need to actually achieve your first project 😄