How to Become Data Analyst in 90 Days With Only $102 Budget?

career online tools $100 budget

Hey! Today we have an experimental article, a roadmap for data analysts. On this site, we often talk about free courses, books, and tutorials, in a word, educational materials that help many to become an expert in their field, prepare for an interview, or change their career path. But today, we will try to figure out what materials you can pick up for yourself with a budget of $102.

At each stage, we will calculate how much time and money we spent on this or that educational material, and our main goal will be three factors - to gain theoretical and practical skills in analyzing data sufficient to get a Junior position, keep within $102 and do it all for 90 days.

Math - Statistics and probability

resource: khanacademy

For some reason, everyone constantly forgets this stage, despite the fact that it is the foundation upon which additional training is constructed. Working with data is difficult without a basic grasp of statistics and probability theory. Trust my expertise. I didn't ask a single question on Python in my first interviews; instead, I focused on mathematics, namely linear algebra. Learn arithmetic before proceeding to the next phase, and Khan Academy may assist you with this. A really popular resource. I don't enjoy all Khan Academy courses (I won't go into detail because they're all so focused on something else), but I truly appreciate the statistics and probability course here. A good and straightforward presentation. This is the most important criterion for me.

Khan Academy - Statistics and Probability (source)

Here you will learn how to read graphs, understand basic terminology (such as median and mathematical mean and how they differ), and model the distribution of data. The latter is especially important because, in fact, this is what you will do in subsequent programming language courses (which I will not spoil yet), as well as at work.

At one time, I did not go through this entire course, but devoted about 32 hours to it, most of which I spent on probability theory. I will say right away that the probability theory section was helpful to me only at an interview and nowhere else since a data analyst works more with existing data, and probability theory is more useful for machine learning specialists. So here's my advice to you - do not worry too much if something doesn't work out for you the first time - everything will be much clearer after practice.

SQL - Full Course

resource: HackerRank

SQL is needed everywhere and in every job. Project managers, developers, marketers, analysts, technical writers, engineers, HR professionals, and many more require it. In a nutshell, everyone. SQL allows you to query databases and extract information from massive data warehouses with hundreds of millions of entries. Consider a large Excel spreadsheet with at least a thousand rows to help you comprehend. Consider how much time it takes to discover the proper record in it; you'll need a person or a certain amount of money.

Hacker Rank Interface (source)

Someone will say, "OK, smart guy, I'll use ctrl + f to find the entry I need." And someone will be correct, but what if you need to discover numerous records with different names, ages, or other criteria? So, what should you do? SQL comes to the rescue here, allowing you to discover such records in a matter of seconds. Furthermore, Excel tables use a lot of space and are too hefty to contain hundreds of millions of rows. Experiment with opening a file containing at least one hundred thousand lines. No, don't even attempt; spend a year of your life on something more useful.

You can learn SQL - its syntax and logic in a week of hard work, in just a week. It is intuitive and essentially represents an English accent. SQL is your next tool, along with mathematics, which will allow you to do with the data what you want, get what you need, and read what you need. But this is not enough, and we need a tool that will perform the calculations you have planned.

Python - Core Python 3: Getting Started

resource: Pluralsight

It's time to pay a little. This course and the next course are worth nothing on their own - the price is for a monthly subscription to this online learning platform, so we took the monthly subscription cost for calculation. You can easily complete this course and the previous one in one month, but you will calculate according to the worst-case scenario, so it will be more objective and honest.

Core Python 3. Getting Started - Pluralsight Course (source)

If for a miner the main tool is a pick, then for you, as for future data analysts, this Python tool. Many people argue about which is better Python or R, a language specially created for data analysis and processing, but I still recommend that you stay with Python for one very simple reason - its versatility. Knowing Python opens up many more career paths for you than with knowledge of R, and these paths are not only in the field of data analytics, but also in the field of development, machine learning, or, for example, Data Science. In a word, always strive for universality.

This course will introduce you to the basics of this programming language, dive into the terminology and explain the logic behind how Python works. The site says that the course lasts only 4 hours, but don't get too excited. Firstly, these are just the basics, your starting point, your sharpening before chopping meat, and secondly, the duration does not take into account additional tasks from mentors and homework, which is also worth singing about, in no case should it be skipped and analyzed every line of code written by the mentor on the screen. This is the only way to learn programming languages.

Python - Exploratory Data Analysis with Python

resource: Pluralsight

Approximately 20% of the Junior Data Analyst's work (I'll talk about the other 80 later) is the compilation of EDA (Exploratory Data Analysis) dashboards. In fact, this is a whole art, about which I even wanted to write a separate article. Maybe I’ll even write, everything will depend on how much this article will help readers.

Exploratory Data Analysis with Python - Pluralsight Course (source)

In fact, EDA is a dashboard that includes a first impression of a dataset, a first look at the data, identifying any patterns that immediately catch your eye, or, for example, building basic and simple graphs in order to learn more about the dataset, with which you work.

I didn’t want to share my EDAs with you until the last, because then I was studying, and they look terrible, but I thought that for the sake of completeness, you should see my infamous EDA dashboards. Some (most recent) can be very good. You should definitely learn how to compose such dashboards because this is the basis, this is base that you will be taught in this course.

Python - Data Cleaning and Analysis in Python

resource: DataQuesst

Dataquest is a very popular resource not only for data analysts but also for anyone who plans and wants to work in the data field. Here you will find everything related to data professions and, if you wish, you can take a whole course for obtaining the profession of a data analyst. I don’t really like this method, simply because the more different sources you use, the more horizons you have, and the more information you get from different experts. But here there is such an opportunity.

Data Cleaning and Analysis in Python - DataQuest Course (source)

On the Dataquest website, you will find two courses on Data Cleaning for beginners and for more advanced ones. I highly recommend you take both courses. Firstly, data cleaning is 80% of the work of any data analyst - preparing the dataset for work, cleaning up incorrect data, and filling in gaps using Python. Secondly, this is what you will be doing as a Junior Data Analyst all the time. Your working day will begin and end with data cleaning. I tell you this from my own experience.

Just like with the previous resource, Dataquest has a subscription system, that is, you pay not for the course, but for the month of use (so it’s in your interest to be as productive as possible). A monthly subscription costs $49. On our website, we do not comment on prices, and we also do not say whether it is a lot or a little - for some, these are ridiculous prices, but for someone, this money lives for a week, so we add on our own that compared to competitors, this is a very high price.

Python - Data Manipulation

resource: Kaggle

If you are planning to become a data scientist and have not heard of Kaggle, then this is a big oversight on your part, but I will fix it. Kaggle is a huge sandbox of users who level, learn, and compete in the data realm. This is a unique place, about which we write very often and in detail on our website. I highly recommend reading ours and mine, including articles about Kaggle (for example, an article about what it is all about or an article about why this is the best place to get hard skills).

Kaggle Courses (source)

On Kaggle you will find a huge number of free courses, which, if I were you, I would take all of them, even though you have already taken some of them (see above). The fact is that at this stage you have a general idea about each of these skills, but you can hardly imagine how to use these skills at the same time.

Kaggle will solve this problem and in simple and short courses will show you how to work with all the acquired skills at once, and not separately. The courses are simple and rather superficial, but they will perfectly complement your current knowledge and immerse you a little in the data analyst process so that you have an idea of ​​what kind of profession you are applying for.

SQL - Interview Preparation

resource: Data Lemur (Premium)

The last resource for today will be Data Lemur. This is a resource that will help you prepare for your data analyst interview (which is why this resource is the last one). It contains a huge number of tasks and tests from real interviews with the world's top SQL companies. When I was interviewed, I was asked more about SQL and mathematical statistics, and I demonstrated Python skills through my Kaggle profile.

DataLemur Start Page (source)

I will say right away - you can use the free version of this site, but I have indicated here the premium version. The fact is that the premium version opens up more tasks for us, as well as hints in case something is not clear and you cannot cope with something. It is very valuable - not to look for answers on the net yourself, such a search has a very low educational value, but to use tips and advice from experts. At the initial stage of training, this is especially valuable.

If at the time of the job search, I would have met such a site, I would have gone through every task, and I would have read every hint because, in fact, all SQL test tasks are the same and check basic knowledge, only the data itself differs. You will understand this too when you go through these exercises.

Conclusion

I am very glad that I wrote this article and shared with you the best, in my opinion, educational resources available that can make you a Junior Data Analyst. These resources have helped me in the past and I hope they will help you too. I hope you learned something new today or at least got inspired to learn. If today's list is not enough for you, you can always visit our search page for finding useful educational resources - we have accumulated a fairly large database of learning sources, 99% of which are absolutely free. Good luck with your learning!