Principle’s Post

Want to land that "entry-level" data analyst gig??? This is all you will need: 👉 5 or more years of SQL 👉 10 or more years of Excel 👉PhD in Machine Learning... because why stop at a bachelors??? 👉 3 or more years managing AI Robots or herding cats 👉 Experience in deciphering hieroglyphics-some of our documentation is old... 👉 Proficiency in at least 8 languages- including binary and dolphin ( they are the future) 👉Preferred Experience in Time Travel in order to gather future datasets and help forecast data trends Honestly just a simple list of requirements... 😉

"Real data analysts don't use Excel." "You're not a data analyst if you don't use Python or R." "You need to have 3 years of experience before you can call yourself a real data analyst." "Your title says you aren't a real data analyst." The gatekeepers don't want to hear this, but data analyst roles vary across companies and industries. You're not any less of a data analyst if you clean your data in Excel or if your company doesn't have a SQL server. There are data analysts whose roles are entirely comprised of writing SQL queries and creating dashboards. There are data analysts who take on more of a data scientist role by implementing product testing and machine learning. Why are we policing others for their contributions as data analysts? And who has the authority to say whether or not someone is a "real" data analyst? If you're working with data to solve problems and provide recommendations, you are a data analyst. #dataanalyst

"Real data analysts don't use Excel." "You're not a data analyst if you don't use Python or R." "You need to have 5 years of experience before you can call yourself a real data analyst." "Your title says you aren't a real data analyst." The gatekeepers don't want to hear this, but data analyst roles vary across companies and industries. You're not any less of a data analyst if you clean your data in Excel or if your company doesn't have a SQL server. There are data analysts whose roles are entirely comprised of writing SQL queries and creating dashboards. There are data analysts who take on more of a data scientist role by implementing product testing and machine learning. Why are we policing others for their contributions as data analysts? And who has the authority to say whether or not someone is a "real" data analyst? If you're working with data to solve problems and provide recommendations, you are a data analyst. #dataanalyst

Thanks so much Mary Knoeferl for this. I was a supply chain planner before and because of this gatekeeping mental model, I was hesitant to call myself a data analyst. I used Excel primarily, I did not know SQL (at that time) and merely applied statistics not the cooler data science stuff. I was actually a #dataanalyst after all. 😁 💙 😁 I am now a #technicalprogrammanager in the data space, there is also gatekeeping in this discipline, referring to these #agilistas #agile consultants/scammers/bozos who think they understand how to actually run technical initiatives. Fortunately, in true technology companies (another gatekeeping haha), no one really pays attention to these folks ---> https://lnkd.in/gJfYniV2. 😀

There has been a lot of changes & advancements in recent years across the Data & Business Intelligence landscape 📊 Job titles are one that seem to have changed in recent years, albeit the fundamentals & principals remain the same 👨💻 Here are some changes in titles I have seen: ✅ DBA = Database Engineer / Developer ✅ ETL/DW/Database Dev = Data Engineer ✅ BI Developer = Data Visualization Engineer/ Analyst ✅ Data Analyst = Analytics Engineer / Data Scientist ✅ Data Scientist = AI / Machine Learning Engineer What would you add? 👨💻 #Data #DataEngineer #BusinessIntelligence #AnalyticsEngineer #DataScience

Data Scientist vs. Data Analyst: Unraveling the Path Best Suited for You 🚀🔍 In the vast ocean of data careers, the choice between becoming a Data Scientist and a Data Analyst can seem overwhelming. Thankfully, Hari Prasad Renganathan and Venkata Naga Sai Kumar Bysani have provided a beacon of clarity in their recent dialogue, which I found both enlightening and invaluable. 📌 Detailed Key Takeaways: ✅ Job Roles & Tasks: Data Scientists focus on building predictive models and require a deep dive into machine learning, whereas Data Analysts concentrate on interpreting data to provide actionable insights. ✅ Skill Requirements: Mastery in programming languages like Python and tools for machine learning is crucial for Data Scientists. In contrast, Data Analysts thrive with SQL, data visualization tools like Tableau or Power BI, and a knack for storytelling. ✅ Educational Background: Neither role strictly requires a computer science degree. Passion, skill and the willingness to learn are key. ✅ Industry Applications: Both roles are versatile, finding a place in industries ranging from healthcare to finance, each with unique contributions. ✅ Career Growth: Paths in both domains offer progression from entry-level positions to senior roles, emphasizing the importance of continuous learning and adaptability. ✅ Work-Life Balance: Generally favorable in both fields, with slight variations based on company culture and project demands. ✅ Salary: While Data Scientists often command higher salaries due to their technical expertise, Data Analysts can also achieve competitive compensation, especially with experience and specialization. Below is the link to the video 🔗 Link: https://lnkd.in/gCF7fjqs Huge appreciation to Hari Prasad Renganathan and Venkata Naga Sai Kumar Bysani for demystifying these paths. Whether you’re drawn to the analytical storytelling of Data Analysis or the predictive modeling at the heart of Data Science, there's a fulfilling career ahead for you. #DataScience #DataAnalytics #CareerAdvice #TechCareers

𝗛𝗼𝘄 𝘁𝗼 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿 -- [1] Do you have strong analytic skills? ↳ Yes: Proceed to the next step. ↳ No: Data science might not be the right career for you. [2] What data do you enjoy working with? ↳ Structured & Small: Data Analyst might suit you. ↳ Unstructured & Large: Data Engineer could be your calling. [3] Do you enjoy solving problems? ↳ Yes: Data Science awaits. ↳ No: Data science might not align with your interests. [4] Do you prefer solving business problems? ↳ Yes: Consider a path as a Business Analyst. ↳ No: Proceed to the next step. [5] Interested in Machine Learning? ↳ Yes: Explore opportunities as a Machine Learning Engineer or Data Scientist. -- 𝗙𝗮𝗰𝘁𝗼𝗿𝘀 𝘁𝗼 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 -- Consider these factors: ↳ Interests: What problems ignite your passion? ↳ Skills: Assess your existing abilities and willingness to learn. ↳ Education: Evaluate your educational background and readiness for further studies. ↳ Job Market: Research job prospects across different data science careers.

𝗛𝗼𝘄 𝘁𝗼 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿 -- [1] Do you have strong analytic skills? ↳ Yes: Proceed to the next step. ↳ No: Data science might not be the right career for you. [2] What data do you enjoy working with? ↳ Structured & Small: Data Analyst might suit you. ↳ Unstructured & Large: Data Engineer could be your calling. [3] Do you enjoy solving problems? ↳ Yes: Data Science awaits. ↳ No: Data science might not align with your interests. [4] Do you prefer solving business problems? ↳ Yes: Consider a path as a Business Analyst. ↳ No: Proceed to the next step. [5] Interested in Machine Learning? ↳ Yes: Explore opportunities as a Machine Learning Engineer or Data Scientist. -- 𝗙𝗮𝗰𝘁𝗼𝗿𝘀 𝘁𝗼 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 -- Consider these factors: ↳ Interests: What problems ignite your passion? ↳ Skills: Assess your existing abilities and willingness to learn. ↳ Education: Evaluate your educational background and readiness for further studies. ↳ Job Market: Research job prospects across different data science careers. ---- Source: Preksha Kaparwan 🙌 Check out for more: https://lnkd.in/gFPT4bXF

🚀 Hey Data Science Rockstars! Big News! 🚀 Exciting times ahead! I'm thrilled to share that I've just rolled out my very own Data Science Jobs Salaries Dashboard using Tableau! 📊 It's packed with insights tailored just for you - whether you're in NYC, London, or anywhere in between. Dive in to discover how your experience level, job role, and even employment type impact salaries in this dynamic field. 💰 🔍 Explore salary trends in your city or dream destination. 📈 Level up your career game by comparing salaries across experience levels. 💼 Get the lowdown on compensation for different job types - full-time, part-time, or contract. 👩💻 Uncover the secrets behind the numbers for various data science roles, from analysts to machine learning gurus. Ready to take the plunge? Click https://lnkd.in/ecDaQq3G to access the dashboard and turbocharge your data science journey! Don't forget to spread the word and tag your fellow data enthusiasts. Let's conquer the data world together! 💡 #DataScience #SalaryInsights #CareerGrowth #TableauMagic

Information about the Dataset (AFTER CLEANING) Job Title: The title of job, eg. Data scientist, junior data scientist, senior data scientist etc. Salary Estimate: Range of salary and the source. Job Description: Tells us what is expected out of the job title. Rating: It gives the rating of the company Company Name: Name of the company Location: Location of the job Headquarters: location of headquarter of the company Size: Range of number of employee working in the company Founded: Company founded in Year Type of ownership: Tells us if the company is private, public or government owned. Industry: Industry of the company Sector: Sector in which company works Revenue: Total revennue of the company per year Competitors: Current competitor of the company in the same sector Hourly - Tells us if the salary reported was hourly or yearly. 1: Hourly, 0: not hourly. Employer provided: 1: If the salary was provided by the employee of the company, 0: otherwise. Lower Salary: Lower salary reported for the job in a particular company. Uppr Salary: Upper salary reported for the job in a particular company. Avg Salary(K): Average of Lower and Upper salary yearly. K is the unit of the column, it means 1000. Also, the slaary is in ($) U.S. dollars. company_txt: It contains the name of the company. Multiple skill columns (python, spark, aws, excel etc): 1: Skill is required by the company, 0: It is not required. Jobtitle_sim: It contains the title of the job like Data scientist, ML engineer etc. seniority_by_title: Senority of the position, it is extracted from the Job Title. Degree: If the job description mention that the company gives experience credit for a master(M) or Ph.D degree(P).