Since I posted a link to an accomplished certificate in Data Analysis from freeCodeCamp in my profile and created an alert for Data Analyst, LinkedIn Learning continues to offer me free 3-hour courses on Excel and Power BI. I like working with Python rather than with Excel/Power BI, but some companies require Excel/Power BI. I am currently completing 2 other Python certifications in scientific computing and machine learning and am already out of my planned schedule for them.<p>Are Excel/Power BI courses worth more than Python courses?
> I am currently completing 2 other Python certifications in scientific computing and machine learning and am already out of my planned schedule for them.<p>If you want to focus on scientific computing and machine learning, don't spend time on Excel/Power BI (it might actively harm your employability). If you want to focus on analytics and business intelligence, take the Power BI course and don't spend too much time on ML.<p>They're completely different career tracks, they aren't complementary, one of them isn't a more advanced version of the other - the only thing in common is that "data science" sometimes means one of these tracks and sometimes it means the other depending on the team/company - pick which one you want to do and focus on that.
Python, Excel, PowerBI are tools, not careers. Should an auto mechanic learn how to use a wrench or a screwdriver?<p>I haven’t seen the LinkedIn courses but I suspect they count for very little. Most technical certifications have little value on a resume, but may guide you through learning something.<p>The human mind can learn more than one thing at a time. Schools operate according to that principle. Amazing, I know, but maybe you can learn multiple things at once.
My first piece of advice: don't take advice from random people on the internet :)<p>My second piece of advice: technologies are just tools. Once you are competent in the foundational concepts of why the tool is needed, and how it is used, you are better situated to learn different tools to accomplish similar goals. So, focus on the domain and problem you want to solve. If you like the domain and become good at understanding the problem space and solution paths, then different tools can help you.<p>My third piece of advice: Once you grok the domain, then you can choose roles/jobs that align with the domain. If a company is only looking for 'Excel/PowerBI' folks, it's probably going to be a boring place to work. If the company is looking for someone to help them solve their problems, and are flexible in the tool space, it's probably going to be more interesting for you.
Based on my experience, so not accurate as companies tend to mix skills.<p>Data Analyst: Mostly Excel/Any BI tool + SQL + Python (pandas, numpy)<p>BI Developer: Mostly Any BI tool + SQL + Python (airflow, pyspark)<p>Data Engineer: SQL + Python (airflow, pyspark) + Possible Scala (for spark) + Possible ops<p>Big Data Developer: Python (pyspark) + JVM language (spark/flink/etc.) + Possible ops<p>$$$ usually increases top->bottom<p>For Machine Learning I think it's a completely different pic.
In my view Pandas is a far better additional thing to learn than Excel if you already know Python. It will build up from your existing Python skills and let you accomplish a lot of tabular data work that you can do in spreadsheets.