Data Analysis with Omar

I bet you’ve watched one of those ‘very useful’ YouTube videos, where you’d be expecting to see what a data analyst do on their daily life (AT WORK), but it ends up being ‘the cool life of a data analyst’. The video starts with a bunch of useless visual effects of the “data analyst” waking up in their half-a-million-dollars bedroom. The best part is that they always wake up with 500 cameras capturing their first seconds of the day, from 500 different angles. They wake up very excited about their day. They are so excited, that the first thing they do is a full 30 mins workout and then they have one of the most luxurious breakfasts you’ll ever see in your life. Finally, and most importantly, they take their shower and right away they start their first ‘business meeting’ where they discuss with 200 executives what they will be doing for the rest of the day. Sounds familiar? Yes… The sad story is that none of this is real. This is more fictional than the

Many people approaching the field of data science and data analysis, ask about programming languages. The two most frequent advice are to study either R or Python. However, saying Python without much context can be unobliging and lead the aspiring data analyst or scientist to lose their time on learning unneeded tools. To each field its tools and priorities: the following will explain what is required to start your journey as a data analyst with Python.  Why learn Python in 2021? Python is a general/multi-purpose programming language. You can do everything related to coding on Python: software development (engineering), games development, data science and data analysis, dashboarding (and many more…). In addition, python is relatively simple to learn due to its English-like syntax. In my humble opinion, Python should be learned for the sake of understanding what coding is about, even if you will not use it daily. How to learn Python in 2021? (For beginners)

Data-related jobs have been gaining popularity in the past few years. Many students are aspiring to become data analysts or data scientists; either by taking courses at their universities that relate to those topics (Data mining, machine learning…) or by taking (free or paid) online (coding or theoretical) courses to acquire certifications as proof of their expertise in the domain. Similarly, many professionals are also following the trend.  A career shift into the field of data has become the new normal. However, many do not know how to approach this field after being certified and how to increase their chances of landing their dream job. Therefore, in the following, I will explain what is the easiest way to get a first hands-on experience in the field of data. This will help you highlight your newly acquired skills, from the online coding platforms or theoretical MOOCS that you have taken. What is a portfolio? The term portfolio is highly popular in the arts and creative industry.

Many fresh graduates dream of starting their careers at a Big 4. Their main reasons to join such companies are the title, the money, and the gained experience from working with executives. In addition, the Big 4 companies provide exposure to diverse fields and sectors. Therefore, the work is highly dynamic and less repetitive when compared to the average company.  In the following, I share my personal experience at a Big 4 and what to expect in general when working there. You should read if You are applying or looking to apply for a job at a big 4. Especially if the position is related to data analytics . Who are the Big 4? The Big four refers to the biggest four accounting firms and they are PwC (PricewaterhouseCoopers), KPMG ( Klynveld Peat Marwick Goerdeler ), and EY (Ernest & Young). They are also famous for their consulting departments which are the dream job for many fresh graduates from diverse fields (Business/Econ, STEM, Law...). I