Drew Zeimetz on LinkedIn: This is the biggest mistake I see sports bettors make: Avoid this mistake… (2024)

I was wrong about using YouTube to learnI avoided YouTube when learning data analytics prior to breaking inI preferred structured paths with a clear trackOften I opted to purchase these structured courses/curriculums and work through themBut now I am reaching a point where I am curious about certain niche topics or subtopicsIt doesn't make sense to purchase full courses when I am only interested in a small section of the courseThis is where YouTube has impressed me lately as I can search the sub topic of interest and follow a videoAlso for people breaking in or wanting to dip their toes into data analytics and see if they like it, watching YouTube is a great free resourceFollowing along doing projects with a split screen has been very productive for me lately

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One of the most interesting things about data analytics, is the diversity of skills and tech stacks.I may specialize in tool X and not use tool Y, while another data analyst will specialize in tool Y and not use tool X.And same goes for specific business knowledge since analytics is used across many industries.When you have years of experience in data, you have the control to seek out the problems you want to solve, with the tools you want to use, and in an industry that you enjoy.The possibilities are endless!

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I am going to learn Data Analytics 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲 in 2024In 2023 I had the bad habit of:- jumping course to course- not finishing the last chapters- hyper focus on one technical skillIn 2024 I will do the following:- stick the courses/books that I am in progress of- go through all of the material- balance learning more than one technical skill at any given timeHere is how I am going to make that happen:- Finishing what I am already in the middle of (No switching)- Writing down what I am going to complete by the end of the month- Creating my own learning roadmap- Writing and publishing this post for accountability#dataanalytics

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Then vs Now:#sql #sqlsunday

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Here's the most overlooked tool when learning data analysis:Microsoft ExcelWhy?It is the MOST WIDELY used data analysis tool. End of debate!It makes no sense to start with lesser used tools.Also in case that doesn't convince you, the stakeholders mostly want the final report to be in Excel.The stakeholder wants:-The result set of a query to be in excel -Your pandas data frame to be in excel -got tabular data? Through it into excelYou are not missing out by starting with excelALSOExcel has very transferable skills:- handing tabular data- filtering data- slicing data- cleaning data- connecting tables- data visualization- ..... (I could write a list 100 items long)If you start with Excel and learn to process and visualize data. You will learn SQL, Pandas, tableau, and power bi at a faster paceStart with ExcelDon't skip itI know other tools are vastly more powerful. But for someone first getting into data analysis, Excel is the clear choice to start with.

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How to learn statistics and probability (for data people):You need the right resources.If statistics scares you, start with the book "Naked Statistics". Charles Wheelan does a great job at making complex topics seem simple.Need something that is extensive and FREE?Take Khan Academy's Statistics and Probability course. Literally sign up and its free and very high quality material. And there is many more free courses available on the site. Looking to get a college quality education in statistics and probability?Check out MIT OpenCourseWare and watch college lectures for FREE.What about practicing over 800 problems for under $20?Get Schaum's Outlines: Probability and Statistics (4th edition). I personally used Schaum's post college to keep my stats skills fresh and ready.For the YouTube stans, check out these channels:-Michel van Biezen-Khan Academy -StatQuest with Josh Starmer-3Blue1BrownOr take the longer and more expensive route like I did and get a bachelor's degree in Mathematics with an emphasis in statistics.📸 Credit: DALL-EP.S. The more you look at the picture the worse it gets. What is your favorite mistake or oddity in the photo?

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Getting your first role in analytics can be challengingCandidates often incorrectly value different factorsHere are the overrated and underrated factors:𝐎𝐯𝐞𝐫𝐫𝐚𝐭𝐞𝐝:- Certificates of Completion - Expensive College Degrees- Cover Letters- Comparing yourself to the average/typical applicant𝘐 𝘸𝘪𝘭𝘭 𝘦𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘦 𝘮𝘰𝘳𝘦 𝘰𝘯 𝘵𝘩𝘦𝘴𝘦 𝘱𝘰𝘪𝘯𝘵𝘴 𝘣𝘦𝘭𝘰𝘸𝐔𝐧𝐝𝐞𝐫𝐫𝐚𝐭𝐞𝐝:- Certification of Competency- Personal Brand and Network- Portfolio of personal projects - Cultural fit/communication skills- Business Acumen/Industry specific knowledge𝐄𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧:Certificates of Completion: These certificates, which are usually earned by taking online courses, are great for learning but might not have a big effect on hiring decisions. Employers care more about how you use the skills you've learned than about the fact that you finished the course.Expensive College Degrees: The name of the college you attend seems much less relevant today. If you choose to get a degree choose a reasonably priced option that fits your projected salary.Cover Letters: These are becoming less critical in the analytics field. Many recruiters focus more on your resume, portfolio, and practical skills demonstrated through projects or technical assessments.Comparing yourself to the average/typical applicant: Think about it, you see on a website that 200+ people already applied. If there is only one spot available its a competition between the top 1-2% of people who got interviews. But many people compare themselves to the other average/typical person in a job hunt they see on LinkedIn. If your mind set or standard is the average applicant, you will miss interviews like the average applicant.Certification of Competency: Certification of Competency judges based on your understanding of the material in a proctored environment thus is more reliable in demonstrating your ability.Personal Brand and Network: Building a strong personal brand through networking, social media presence, and community participation can open up more opportunities. Your network can provide job leads, recommendations, and advice that are invaluable in job hunting.Portfolio of Personal Projects: A portfolio showcasing your work is crucial. It provides concrete evidence of your skills and is a great way to demonstrate your approach to solving real-world problems with data.Cultural Fit/Communication Skills: Being able to blend into a company’s culture and effectively communicate with both technical and non-technical stakeholders is highly valued. Employers look for candidates who can collaborate well and contribute positively to the workplace environment.Business Acumen/Industry-Specific Knowledge: Understanding the industry you're working in and being able to apply analytics to solve industry-specific problems is incredibly valuable. #data #dataanalyst #career #job

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An exciting milestone!20,000+ followers on #linkedin

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Both queries will get the same result setThe first query is 𝐥𝐞𝐬𝐬 clear on what table and field combination is being used to combine recordsThe JOIN clause will 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 state the table being used and followed by an 'ON" or "USING' clause to specify the field being usedJust an absurd example on how a query statement's readability is important Think about how the query can be read and understood by someone else#sql #data #sqlsunday

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