#Tableau Tutorial

[cavillionlearning]

Industry-Leading Visualization with Tableau Data Visualization

1. User-Friendly Interface: The intuitive design allows anyone to easily start visualizing data.

2. Seamless Integration: Connects easily with various data sources for efficient analysis.

3. Quick Insights: Generate actionable insights within minutes.

4. Integrated Collaboration Tools: Share dashboards and insights with your team effortlessly.

5. Regular Updates: Tableau continuously evolves with new features and improvements.

Want to take your skills to the next level? Join our 1-day Tableau Bootcamp on 2nd November (Online)!

[digitalmarketing6669]

Master Data Preparation in Tableau: Beginner’s Guide for 2024

[tutorialwithexample]

Unleashing the Power of Tableau: Advanced Tips & Tricks for Stunning Visuals

If you’re new to data visualization, Tableau is an excellent tool to start with. This powerful software allows you to create interactive and shareable dashboards that present data in a visually appealing way. In this Tableau tutorial, we’ll walk you through the basics to help you get started.

What is Tableau?

Tableau is a leading data visualization tool used by organizations worldwide. It helps transform raw data into understandable and actionable insights through charts, graphs, and maps. Whether you’re dealing with small datasets or large, complex data, Tableau can handle it all.

Getting Started with Tableau

Download and Install Tableau: First, download Tableau from the official website and install it on your computer. You can start with the free Tableau Public version, which offers basic features.

Connect to Data: Once installed, open Tableau and connect to your data source. Tableau supports various data formats, including Excel, SQL databases, and cloud-based data.

Create Your First Visualization: Select the data you want to visualize, choose the appropriate chart type, and drag your data fields into the workspace. Customize your visualization with colors, labels, and filters.

Build a Dashboard: Combine your visualizations into a single dashboard, making it easier to present your data story.

For more detailed tutorials on data tools, visit Tableau Tutorial.

[jtpoint]

This "Tableau Tutorial" provides a step-by-step guide to mastering data visualization, offering insights on creating dashboards, using advanced features, and transforming raw data into impactful visual stories.

[zora28]

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[avaquava250]

Hi, I just got into tech (actually 1 year in but I still feel like I don't know anything).

I learnt python, R, SQL, Tableau and concepts in Data Structures, Algorithms.

[Just the Basic level of the listed.]

But I can't write my own programs on python or create anything.

I was hella impressed by your microprocessor project, and when I took up tech (ai and data science) I assumed I would do something like that or at least code.

Now, I am just venting but where do I start, how do I make projects what are some interesting projects, what should I learn? A lot of my seniors tell me to have a wide range of knowledge like focus on one thing by depth but add other things. For example: you can study data analytics in depth but have some knowledge in ethical hacking and web development etc. Like an all rounder.

Wow ok a lot to unpack here, I'll get started

Lots of places recommend that you start with Arduino, as there are a lot of good tutorials out there. I somewhat disagree

I think that raspberry pi is better because it's usually a lot cheaper to buy the stuff you want to play around with

But an even cooler way to do it, go to wokwi.com and start a project on a Raspberry Pi Pico (make sure it's the micropython version) and then you can get started there

Google something like "Blink led with Pi Pico micropython" and there will be a tutorial or two

Then, once you've played around for a while you can buy a Pi Pico for very little money and see your stuff work irl!

If you need any more help or have any questions, feel free to DM me and I'll see what I can do

[multimystica]

Welcome!

I'm a Brazilian Tarot reader and Oraculist just trying to make a living to pay for college and help with house expenses, I work with the following divination methods:

Tarot (Rider Waite; Osho's Zen Tarot; Tarot of the Holy Grail)

Lenormand a.k.a. Petit Lenormand ( French Cartomancy, or Gypsy Deck if you're Brazilian)

Vera Sibilla Italiana (a.k.a. Italian Cartomancy)

Elder Futhark (a.k.a. Runes)

Very sporadically I use the pendulum and radiestesy & radionic graphics as well.

My main objective with this profile is to work professionally with divination and spirituality online. If you want to know more about myself before you book a reading or service feel free to ask, I'm an open book.

As this is a professional page and not only a hobby, I plan to charge for my readings, I take payments via Paypal or PicPay (if you're in Brazil, I take payments via pix as well).

However, I also intend to serve spirituality itself, so once a week I'll answer simple questions with 6 card Lenormand readings free of charge via Tumblr's ask function.

Readings:

1 card reading using Osho's Zen Tarot Deck

3 runes reading using Elder Futhark Runes

6 card reading using the Lenormand Deck

3 card reading using Rider Waite's Tarot Deck

Pack of three 6 card readings using Lenormand Deck

Pack of three 3 card readings using Rider Waite's Tarot Deck

Lenormand Grand Tableau reading + 3 clarifying questions.

1 hour of unlimited questions using your oracles of choice (from those currently avaliable) + 1 Osho's Zen Tarot card advice.

Readings can be made online (texts or videocalls if the app of choice supports it, you choose) via Tumblr Chat, Discord, WhatsApp, or delivered on a PDF via e-mail. I'll always send pictures of the cards drawn and explain everything.

Other services I offer:

Spiritual Guidance and Advice Sessions ( 1 hour sessions via Discord or Whatsapp Messenger)

Tutorial on Energetically Cleansing Spellwork (delivered via Discord, E-mail or PDF)

30 days of Spiritual Guidance and Advice + three 6 card readings with Lenormand (via Discord or Whatsapp Messenger)

On Spellwork:

I can perform certain types of spellwork, such as:

Abundance, prosperity & wealth rituals

Self-love, self-esteem & self-respect rituals

Peace and positivity rituals

Those don't have a fixed pricing, the price is to be discussed depending on the severity of the situation, on the pricing of the required materials to perform the ritual, and on the financial situation of the client.

I may as well prescribe baths and rituals which you'd have to do yourself. If you come to me just for the prescription of baths or rituals I just charge the symbolic value of $5 for said prescriptions; however, if you booked a reading or another service with me and I find it to be useful or necessary in your situation, I'll do it free of charge.

Onto the much necessary disclaimer:

I DON'T do binding or karmic return rituals. Do not even ask about it. It goes against my values as a light-worker. Divine justice is there for a reason. I'm not here to rip off anyone so if you really need a certain service and can't afford it just message me about it and I'll see what I can do to help for FREE.

Most importantly: I am not a scammer, I'd rather be scammed than be seen as a scammer so you don't even need to pay upfront, I don't care to work for free if it means I'll get to help people with my cards.

[deletedg1rl]

24 November 2023

ok so here's list of tasks that'll take only a few minutes:

talk about interview with that guy.

emails.

talk about relocation plans and offer letter with HR.

choose a book to start.

text that person on reddit.

message that guy.

here's a list of things that takes time:

project report of tableau.

complete the chatbot tutorial.

start a book.

start my file on at least one person.

write journal.

i am going to schedule all of this like this.

8 am to 9 am -> chatbot/bath.

9 am to 10:30 -> chatbot.

10:30 am to 11 am -> emails, talk about relocation and offer letter with HR, talk about interview with that one guy, message that guy.

11 am to 12 pm -> choose a book, look through all my resources, text that person on reddit.

1:30 pm to 2:30 -> chatbot.

2:30 pm to 4 pm -> tableau report.

5 pm to 6 pm -> read whatever book i've chosen.

7 pm to 8 pm -> write in my journal, start a file on one person.

10 pm to 11 pm -> plan tommorow.

[vivekavicky12]

Cracking the Code: A Beginner's Roadmap to Mastering Data Science

Embarking on the journey into data science as a complete novice is an exciting venture. While the world of data science may seem daunting at first, breaking down the learning process into manageable steps can make the endeavor both enjoyable and rewarding. Choosing the best Data Science Institute can further accelerate your journey into this thriving industry.

In this comprehensive guide, we'll outline a roadmap for beginners to get started with data science, from understanding the basics to building a portfolio of projects.

1. Understanding the Basics: Laying the Foundation

The journey begins with a solid understanding of the fundamentals of data science. Start by familiarizing yourself with key concepts such as data types, variables, and basic statistics. Platforms like Khan Academy, Coursera, and edX offer introductory courses in statistics and data science, providing a solid foundation for your learning journey.

2. Learn Programming Languages: The Language of Data Science

Programming is a crucial skill in data science, and Python is one of the most widely used languages in the field. Platforms like Codecademy, DataCamp, and freeCodeCamp offer interactive lessons and projects to help beginners get hands-on experience with Python. Additionally, learning R, another popular language in data science, can broaden your skill set.

3. Explore Data Visualization: Bringing Data to Life

Data visualization is a powerful tool for understanding and communicating data. Explore tools like Tableau for creating interactive visualizations or dive into Python libraries like Matplotlib and Seaborn. Understanding how to present data visually enhances your ability to derive insights and convey information effectively.

4. Master Data Manipulation: Unlocking Data's Potential

Data manipulation is a fundamental aspect of data science. Learn how to manipulate and analyze data using libraries like Pandas in Python. The official Pandas website provides tutorials and documentation to guide you through the basics of data manipulation, a skill that is essential for any data scientist.

5. Delve into Machine Learning Basics: The Heart of Data Science

Machine learning is a core component of data science. Start exploring the fundamentals of machine learning on platforms like Kaggle, which offers beginner-friendly datasets and competitions. Participating in Kaggle competitions allows you to apply your knowledge, learn from others, and gain practical experience in machine learning.

6. Take Online Courses: Structured Learning Paths

Enroll in online courses that provide structured learning paths in data science. Platforms like Coursera (e.g., "Data Science and Machine Learning Bootcamp with R" or "Applied Data Science with Python") and edX (e.g., "Harvard's Data Science Professional Certificate") offer comprehensive courses taught by experts in the field.

7. Read Books and Blogs: Supplementing Your Knowledge

Books and blogs can provide additional insights and practical tips. "Python for Data Analysis" by Wes McKinney is a highly recommended book, and blogs like Towards Data Science on Medium offer a wealth of articles covering various data science topics. These resources can deepen your understanding and offer different perspectives on the subject.

8. Join Online Communities: Learning Through Connection

Engage with the data science community by joining online platforms like Stack Overflow, Reddit (e.g., r/datascience), and LinkedIn. Participate in discussions, ask questions, and learn from the experiences of others. Being part of a community provides valuable support and insights.

9. Work on Real Projects: Applying Your Skills

Apply your skills by working on real-world projects. Identify a problem or area of interest, find a dataset, and start working on analysis and predictions. Whether it's predicting housing prices, analyzing social media sentiment, or exploring healthcare data, hands-on projects are crucial for developing practical skills.

10. Attend Webinars and Conferences: Staying Updated

Stay updated on the latest trends and advancements in data science by attending webinars and conferences. Platforms like Data Science Central and conferences like the Data Science Conference provide opportunities to learn from experts, discover new technologies, and connect with the wider data science community.

11. Build a Portfolio: Showcasing Your Journey

Create a portfolio showcasing your projects and skills. This can be a GitHub repository or a personal website where you document and present your work. A portfolio is a powerful tool for demonstrating your capabilities to potential employers and collaborators.

12. Practice Regularly: The Path to Mastery

Consistent practice is key to mastering data science. Dedicate regular time to coding, explore new datasets, and challenge yourself with increasingly complex projects. As you progress, you'll find that your skills evolve, and you become more confident in tackling advanced data science challenges.

Embarking on the path of data science as a beginner may seem like a formidable task, but with the right resources and a structured approach, it becomes an exciting and achievable endeavor. From understanding the basics to building a portfolio of real-world projects, each step contributes to your growth as a data scientist. Embrace the learning process, stay curious, and celebrate the milestones along the way. The world of data science is vast and dynamic, and your journey is just beginning.  Choosing the best Data Science courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

[paulgadzikowski]

[Image description: Preview panel for the comic strip at the link. Tom Thumb, Princess Huncamunca, Lord Grizzle, Glumdalca the giantess, King Arthur, Queen Dollalolla, and Merlin of The Opera Of Operas stand in the tableau of the final scene. The Doctor, fugitive incarnation, of Doctor Who stands next to Merlin. Merlin is saying, “Ah yes, there’s nothing like bringing a roomful of spontaneous murder-suicides back to life for convincing them all to look on the bright side.” Unfortunately there are not image descriptions at the main Hero of Three Faces site. End description.]

The Hero of Three Faces is fanfiction crossovers, but it’s comic strips with stick figures, but they’re triangles. Preview panel only. Click here for full cartoon. Or see the on-site navigation tutorial. Or see this blog’s FAQ, or my archive tumblog’s FAQ. Cartoons may contain unmarked spoilers. Cartoons linked from Tumblr 10:00 (Central US time) daily are the previous day’s new update and the posts are pinned to the top of this blog. Cartoons linked from Tumblr 22:00 daily are from the archive and the posts are pinned only during annual summer hiatus of new updates.

[innovationalofficesolution]

How to Handle Calculated Fields and DAX Conversions in Power BI

Power BI, Microsoft's flagship business intelligence platform, has become a go-to tool for organizations transitioning from other BI systems like Tableau. One of the most critical components of this transition is learning how to effectively handle calculated fields and DAX (Data Analysis Expressions) – Power BI's powerful formula language.

At OfficeSolution, we specialize in seamless Tableau to Power BI migrations, and in this guide, we’ll help you understand the best practices for working with calculated fields and DAX conversions.

What Are Calculated Fields in Power BI?

Calculated fields in Power BI are custom columns or measures you create to enhance your data model. These fields are often derived from existing data but represent new insights—like profit margins, rankings, or growth percentages.

There are two main types:

Calculated Columns: Added to tables in your data model, these are calculated row-by-row and stored in the model.

Measures: Dynamic calculations performed on aggregation; they do not occupy memory space per row, making them more efficient.

Understanding when to use each is crucial—calculated columns are great for static row-level logic, while measures are optimal for performance-heavy dashboards.

Introduction to DAX (Data Analysis Expressions)

DAX is the formula engine behind Power BI. It combines Excel-like functions with relational database logic to allow deep data exploration. Whether you're creating time intelligence reports or filtering datasets with complex conditions, DAX is your best friend.

Here are a few foundational DAX functions:

CALCULATE(): Modifies the filter context for a calculation.

FILTER(): Returns a table that meets a specific condition.

RELATED(): Accesses data from related tables.

IF(), SWITCH(): Perform conditional logic.

If you're coming from Tableau, where calculated fields are often simpler expressions, DAX can feel more complex—but it’s also more flexible once mastered.

Tips for DAX Conversions from Tableau

Migrating calculated fields from Tableau to Power BI can be tricky. Here are a few best practices:

Map the Logic Start by clearly outlining what your Tableau calculated field does. Identify if it’s row-level or aggregate, then find the DAX equivalent.

Leverage Power BI’s Data Model Power BI's star schema encourages dimensional modeling. Use relationships wisely to replicate Tableau's LOD (Level of Detail) expressions via DAX functions like SUMX(), ALL(), and KEEPFILTERS().

Use Variables In DAX, VAR statements can simplify complex logic and improve readability. This is a best practice when translating deeply nested Tableau calculations.

Test Incrementally Break down complex Tableau fields into smaller DAX components. Validate each step inside Power BI to ensure accuracy.

Conclusion

Transitioning from Tableau to Power BI requires more than just data migration—it demands a mindset shift in how calculations are handled. With a structured approach and understanding of DAX, calculated fields in Power BI can unlock powerful insights.

At OfficeSolution, we’re here to support your entire migration journey—from data modeling to DAX mastery. For expert help, tutorials, and services, visit us at https://tableautopowerbimigration.com/.

[subb01]

How to Start a Career in Data Science with No Technical Background

If you’ve ever thought, “Data science sounds fascinating, but I don’t have a tech background,” you’re not alone — and you’re definitely not out of luck.

Here’s the truth: you don’t need to be a coder, a statistician, or a data engineer to start a career in data science. What you need is curiosity, consistency, and the right approach.

This blog will walk you through exactly how someone from a non-technical field — like marketing, finance, operations, education, or even arts — can break into the world of data science.

Step 1: Understand What Data Science Actually Is

Start by learning the basics of data science — what it means, how it's used, and the kind of problems it solves.

Think of data science as a combination of three core elements:

Math and Statistics – to make sense of data

Programming – to work with and process that data

Business Understanding – to know which problems are worth solving

The best part? You can learn all of this at your own pace, even if you’re starting from zero.

Step 2: Start with Tools You’re Familiar With

If you’ve used Excel or Google Sheets, you’ve already worked with data.

From there, you can gradually move to tools like:

SQL – to pull data from databases

Python – to manipulate, analyze, and visualize data

Power BI or Tableau – to create dashboards and visual stories

There are beginner-friendly platforms and tutorials available to help you learn these tools step-by-step.

Step 3: Focus on Real-World Applications

Don’t try to memorize formulas or force yourself to master every algorithm. Instead, focus on how data science is used in the real world:

In marketing to measure campaign performance

In HR to predict employee attrition

In finance to detect fraud

In supply chain to optimize delivery routes

Relating concepts to your current domain makes learning not only easier but more enjoyable.

Step 4: Work on Projects, Not Just Theory

Even if you’re still learning, try to work on mini-projects using publicly available datasets from Kaggle or government portals.

For example:

Analyze sales data and build a forecast model

Explore customer churn patterns for a telecom company

Create a dashboard showing COVID-19 trends

These projects will become part of your portfolio, making you stand out when applying for jobs.

Step 5: Keep Learning, Keep Growing

The field of data science evolves fast. Stay updated by:

Following data science communities on LinkedIn

Watching free courses and tutorials

Reading blogs and case studies

Connecting with mentors or peers online

Ready to Get Started?

If you're serious about breaking into data science, there's no better time than now — and no better way than starting with a free beginner-friendly course.

🎥 Check out this free YouTube course on Data Science that explains core concepts, tools, and techniques — all in simple, easy-to-follow language:

👉 Click here to watch the full course

You don’t need a tech degree — just a desire to learn and take the first step. Your data science journey starts today!

[futuremultimedia01]

From Data Dreams to Data Deals: My Path to Guaranteed Placement in Indore's Top Analyst Course

Ever feel like you're drowning in data, but thirsty for insights? That was me. I knew I wanted a career that was both intellectually stimulating and in-demand. Data analytics seemed like the perfect fit, but where to start? Living in Indore, I began my search, and one name kept popping up: a Data Analyst Course with guaranteed job placement.

Let me tell you, "guaranteed placement" caught my attention. But it wasn't just the promise of a job that sold me. It was the promise of a real career, built on solid skills and expert guidance.

Why This Course Felt Different

I've seen plenty of online tutorials and boot camps, but this course stood out. Here's why:

IIT Experts, Not Just Talking Heads: Imagine learning from people who’ve actually built data solutions for real companies. That's what this course offers. The instructors aren't just teaching concepts; they're sharing hard-earned wisdom.

Real Projects, Not Just Textbook Examples: I'm a hands-on learner. The idea of tackling capstone projects across 11 different domains? That's how you actually build confidence and a portfolio that gets noticed.

Case Studies That Matter: No abstract theories here. We're diving into real-world scenarios, understanding how data drives decisions in various industries. This is about learning to think like a data analyst.

The Placement Promise (and the Reality Behind It): Look, "guaranteed placement" is a bold claim. But after looking into the curriculum and the instructors, it felt less like a gimmick and more like a commitment. They're investing in your success, because their reputation depends on it.

The Skills That Will Shape My Future

This course isn't just about learning tools; it's about building a foundation:

From Excel to Python and R: I'm excited to move beyond basic spreadsheets and dive into the power of programming for data analysis.

Visualizing the Story: Turning raw data into compelling visuals with Tableau and Power BI? That's where the magic happens.

SQL: The Language of Data: Learning to extract and manipulate data from databases is a crucial skill, and I'm ready to master it.

Data Wrangling and Ethical Considerations: Understanding how to clean data and the ethical implications of data analysis is vital.

My Journey Starts Now

Whether you're an aspiring data analyst, a business professional looking to upskill, or simply someone who loves data, this course offers a pathway to success.

I'm taking the leap. I'm ready to transform my data dreams into data deals. And I'm excited to see where this journey takes me.

Key Takeaways (For You and Me):

Expert instruction makes all the difference.

Practical experience is non-negotiable.

A commitment to placement speaks volumes.

The right skills open doors.

If you're ready to take control of your data career, join me. Let's learn, grow, and succeed together. Contact the course providers today.

For More Information Visit: https://www.futuremultimedia.in/Data-analytics-course-class-coaching-institute-indore.html

Original Source: https://www.futuremultimedia.in/

[visual-sculptors]

The Art of McKinsey Slides: Strategies for Clear and Persuasive Communication

McKinsey Slide Structure: Q & A Tutorial

1. What tools can I use to create McKinsey-style slides?

To create McKinsey-style slides, you can use tools like Microsoft PowerPoint, Google Slides, or Keynote for design. For data visualization, consider Excel, Tableau, or Google Data Studio. Additionally, platforms like Canva or Slide bean offer templates that can help emulate McKinsey's clean, professional aesthetic. Maintaining a consistent color palette and clear typography is also essential.

2. Is there a template available for McKinsey slide structures?

Yes, there are templates available for McKinsey slide structures, often referred to as "McKinsey-style" or "MECE" (Mutually Exclusive, Collectively Exhaustive) frameworks. These templates can be found in various business presentation resources or design software. However, it's important to create original content rather than just filling in templates to maintain the quality and effectiveness of your presentation.

3. How does a McKinsey slide structure compare to Bain or BCG slide structures?

McKinsey slides typically emphasize storytelling with a clear, logical flow and a focus on insights. Bain slides often prioritize client implications and actionable recommendations, while BCG slides may incorporate more data visuals and frameworks. All three firms aim for clarity, but McKinsey leans towards narrative, Bain on client impact, and BCG on analytical depth.

4. What are the common mistakes to avoid when following McKinsey slide principles?

Common mistakes to avoid when following McKinsey slide principles include overcrowding slides with text, using unclear visuals, neglecting a logical flow, failing to prioritize key messages, and overlooking audience needs. Additionally, avoid inconsistent formatting and using jargon that may confuse viewers. Always aim for clarity, simplicity, and a strong narrative to effectively convey your message.

5. Are there any case studies demonstrating McKinsey slide structures in action?

Yes, there are case studies available that showcase McKinsey slide structures in action. These often highlight how organizations have effectively used the MECE (Mutually Exclusive, Collectively Exhaustive) principle and pyramid structure to present data clearly and persuasively. Various business strategy resources and consulting literature may provide examples and insights into these techniques.

Visit: VS Website See: VS Portfolio

[codezup]

Use SQL to Create Dynamic Reports with Tableau

Introduction Using SQL to Create Dynamic Reports with Tableau is a powerful technique that allows you to extract insights from complex data sets and create interactive, dynamic reports. In this comprehensive tutorial, we will explore the technical aspects of Using SQL to Create Dynamic Reports with Tableau and provide hands-on examples to help you master this skill. Prerequisites Basic…