 55 Excel Features You Probably Didn’t Know 


➡️ If you’re already proficient in Excel, these will take your game to the next level. 

➡️ And if you’re still learning your way around, these will show you what’s possible.

Comment to let me know which one is your favorite.

🎯 Here’s a snapshot of those that made the list. What did I forget?

➡️F5: Activates the "Go To" dialog box.
- Enables users to directly jump to specific cells, ranges, or named areas, streamlining navigation in extensive sheets.

➡️Watch Window: Tool for monitoring specific cell values in real-time.
- Ideal for observing crucial values, especially across different worksheets or workbooks.

➡️Named Ranges: Provides a feature to assign names to cells or ranges.
- Improves formula readability and aids navigation, especially in large sheets.

➡️Forecast Sheet: Produces visuals predicting future data based on past data.
- Useful for planning and trend analysis.

➡️Power Query: Enables extraction, transformation, and loading of data from a plethora of sources into Excel.
- Streamlines data integration and cleanup.

➡️Tables (Insert Table): Structures data to enhance readability and analysis.
- Offers better data organization and easier formula implementation.

➡️Flash Fill
- Description: Intuitively fills in values by identifying patterns.
- Reduces manual data entry efforts.

➡️Group/Ungroup Rows and Columns: Organizes rows/columns into structured levels.
- Improves sheet readability, especially in large datasets.

➡️Freeze Panes: useful to scroll up down/left right while continuing to see table headers
- Locks specific rows or columns, making

➡️Speak Cells: This feature reads out the contents of a selected cell or range.
 - Helpful for those with visual impairments or when proofreading numbers.

➡️Hidden Formulas: Protects formulas in cells, making them invisible in the formula bar.
- Secures sensitive or complex formulas from being tampered with.

➡️Text Orientation: Adjusts the angle or direction of cell text.
- Enhances readability and allows for better data presentation.

➡️Data Validation Circle Invalid Entries: Marks cells containing data that violates validation rules.
- Quickly identifies and rectifies data errors.

➡️AutoSave for Cloud File: Auto-saves changes for files stored in cloud locations.
- Protects your against data loss and ensures the most recent work is saved.

➡️➡️➡️If you want a pdf copy please drop a comment, like and share this post so your network can enjoy it too!

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🎯 𝐓𝐨 𝐥𝐞𝐚𝐫𝐧 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐚𝐧𝐚𝐥𝐲𝐳𝐞, 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞, 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧, 𝐚𝐧𝐝 𝐦𝐚𝐬𝐭𝐞𝐫 𝐜𝐚𝐬𝐡 𝐟𝐥𝐨𝐰 check out The Cash Flow Masterclass (link in my Linkedin profile)
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I am a seasoned business financial strategist and Canadian CPA with over 20 years of business experience building 2 companies as an entrepreneur, and financing hundreds of companies as a commercial banker.

Through my company Financiario, I now help business owners, CEOs, and Boards of Directors to scale revenues, improve profitability, and maximize enterprise value through strategic financial planning.

I complement and expand the capacity of Controllers, VPs of Finance and CFOs, to help companies achieve their vision and navigate short-, medium- and long-term financing requirements needed to:

✅grow the business
✅acquire new capital assets
✅buy another business
✅set up a foreign operation
✅exit the business altogether.

Over the past decade, my strategic financing and advisory work helped grow an extensive list of successful companies, many of which are now included in Deloitte’s Fast 50, The Globe and Mail’s Top Growing Companies, or OBJ’s Fastest Growing Companies.

To learn more and work with us, visit www.Financiario.com and send us an email: office@financiario.com

Oana Labes, MBA, CPA

If you spend 5-10 mins on this post, then you can learn a really important topic in pyspark which a 1 hour video also cannot teach you this effectively.

Lets say you have a 20 node spark cluster

Each node is of size - 16 cpu cores / 64 gb RAM

Let's say each node has 3 executors,
with each executor of size - 5 cpu cores / 21 GB RAM

=> 1. What's the total capacity of cluster?

We will have 20 * 3 = 60 executors
Total CPU capacity: 60 * 5 = 300 cpu Cores
Total Memory capacity: 60 * 21 = 1260 GB RAM

=> 2. How many parallel tasks can run on this cluster?

We have 300 CPU cores, we can run 300 parallel tasks on this cluster.

=> 3. Let's say you requested for 4 executors then how many parallel tasks can run?

so the capacity we got is 20 cpu cores
84 GB RAM
so a total of 20 parallel tasks can run.

=> 4. Let's say we read a csv file of 10.1 GB stored in datalake and have to do some filtering of data, how many tasks will run?

if we create a dataframe out of 10.1 GB file we will get 81 partitions in our dataframe. (will cover in my next post on how many partitions are created)

so we have 81 partitions each of size 128 mb, the last partition will be a bit smaller.

so our job will have 81 total tasks.
but we have 20 cpu cores

lets say each task takes around 10 second to process 128 mb data.
so first 20 tasks run in parallel,
once these 20 tasks are done the other 20 tasks are executed and so on...

so totally 5 cycles, if we think the most ideal scenario.

10 sec + 10 sec + 10 sec + 10 sec + 8 sec

first 4 cycles is to process 80 tasks all of 128 mb,
last 8 sec is to process just one task of around 100 mb, so it takes little lesser but 19 cpu cores were free during this time.

=> 5. is there a possibility of, out of memory error in the above scenario?

Each executor has 5 cpu cores and 21 gb ram.
This 21 gb RAM is divided in various parts -
300 mb reserved memory,
40% user memory to store user defined variables/data. example hashmap
60% spark memory - this is divided 50:50 between storage memory and execution memory.

so basically we are looking at execution memory and it will be around 28% roughly of the total memory allotted.

so consider around 6 GB of 21 GB memory is meant for execution memory.
per cpu core we have (6 GB / 5 cores) = 1.2 GB execution memory.
That means our task can roughly handle around 1.2 GB of data.
however, we are handling 128 mb so we are well under this range.

I hope you liked the explanation :)

if you want to experience a learning like never before & want to make a career in big data then visit my website https://lnkd.in/gt_jpCyE to know more about the program. New batch starting this Saturday.

#bigdata #career #dataengineering #apachespark

Key Concepts to Understand Database Sharding. 
. 
. 
In this concise and visually engaging resource, we break down the key concepts of database partitioning, explaining both vertical and horizontal strategies. 
 
1. Range-Based Sharding: Splitting your data into distinct ranges. Think of it as organizing your books by genre on separate shelves. 
 
2. Key-Based Sharding (with a dash of %3 hash): Imagine each piece of data having a unique key, and we distribute them based on a specific rule. It's like sorting your playing cards by suit and number. 
 
3. Directory-Based Sharding: A directory, like a phone book, helps you quickly find the information you need. Similarly, this technique uses a directory to route data efficiently. 
 
Over to you: What are some other ways to scale a database? 
 
– 
Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq 
 
#systemdesign #coding #interviewtips 
.

Follow me for system design & book-writing tips | Author of System Design Interview Series

Elevate your data game with SQL Charts!

The ability to harness the full potential of your information is a game-changer. SQL charts works like the dynamic bridge that transforms raw data into compelling visual stories.

But to truly harness the power of data, you need to master Database Control: DQL, DML, DCL, and DDL.

➡ DQL (Data Query Language): Learn the art of crafting SQL queries that not only fetch data but also transform it into meaningful narratives

➡ DML (Data Manipulation Language): Dive deeper into data manipulation, where you can shape, modify, and organize your data. With DML, you can craft the perfect data canvas for your visual storytelling

➡ DCL (Data Control Language): Data security is a must. Discover how to control access, permissions, and privileges, ensuring that your data remains safe and secure throughout its journey

➡ DDL (Data Definition Language): Lay the foundations for your data infrastructure. DDL allows you to define the structure, constraints, and relationships within your database, setting the stage for powerful data visualization.

Credits: Saurabh Moody and Citizen Data Scientist

Data is the lifeblood of any modern business. But with so much data available, it can be difficult to know how to store, manage, and analyze it effectively. 

That's where data warehouse, data lake, lakehouse, and data mesh come in.

1. **Data Warehouse:**
- 📂 Structured Data: Designed primarily for structured data storage.
- 📊 Analytical Focus: Optimized for query performance, typically used for business intelligence tasks.
- 🛠 ETL Process: Data is cleansed and transformed (ETL) before it’s loaded.
- Example: Teradata, introduced in the late 1970s, is a pioneering example of a data warehouse solution.
- Historical Note: Became popular in the 1980s and 1990s as businesses needed more analytical power.

2. **Data Lake:**
- 🌊 Raw Data: Can store massive amounts of raw, structured, semi-structured, or unstructured data.
- ⏱ Schema-on-Read: Data structure is defined at the read time.
- 🛠 ELT Process: Store first, transform later.
- Example: Amazon S3, launched in 2006, is a popular choice for building data lakes.
- Historical Note: Gained traction in the 2010s with the rise of big data and diverse data sources.

3. **Lakehouse:**
- 🏠 Hybrid: Combines aspects of Data Warehouses and Data Lakes.
- 📊 Unified Platform: Facilitates both BI and machine learning.
- 🛠 Data Quality: Maintains reliable data standards.
- Example: Databricks Delta Lake, introduced in the late 2010s.
- Historical Note: Emerged recently, addressing the gaps between data lakes and warehouses.

4. **Data Mesh:**
- 🌐 Decentralized: Promotes domain-oriented decentralized data ownership.
- 🚀 Scalability: Built for modern distributed systems and microservices.
- 🤝 Collaborative: Focuses on cross-team collaboration.
- Example: It's more of a paradigm than a product. Think of it as a decentralized approach akin to how microservices decentralized traditional app architecture.
- Historical Note: Started gaining attention in the early 2020s, building on the lessons of past architectures.


In essence:
- Warehouse: Structured, analytical powerhouses.
- Lake: Massive, diverse data reservoirs.
- Lakehouse: The union of both worlds.
- Mesh: Decentralized, scalable futures.

Remember, the ideal choice aligns with your business objectives, needs, and infrastructure.

Would love insights from fellow data enthusiasts navigating this terrain!

#DataManagement #DataWarehouse #DataLake #Lakehouse #DataMesh #BigData #dataengineering #softwareengineering 

🚀 Exciting News! 📊 

Tableau has just launched its first-ever Business Intelligence course in partnership with Coursera! 🌟

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Looking to transition to data analytics but not sure where to start? 

Reach out to me for personalized learning plans. 

Let's shape your data-driven future together! 🔗 [Book a consultation here: https://lnkd.in/gs65FKyU]

#dataanalytics #tableau #coursera #careerdevelopment #dataachievers

I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

Data serves as the lifeblood powering today's businesses. Yet, amidst the overwhelming abundance of data, the challenge lies in effectively storing, managing, and analyzing it.


👉 Follow Rajesh Kumar for more amazing content ❤️
👉 Follow Rajesh Kumar for more amazing content ❤️

This is where concepts such as the Data Warehouse, Data Lake, Lakehouse, and Data Mesh come into play.

1️⃣ Data Warehouse:

-📂 Focus on Structured Data: Primarily designed for the storage of structured data.
-📊 Geared for Analysis: Optimized to deliver top-notch query performance, typically employed for business intelligence tasks.
-🛠 ETL Process: Data undergoes cleansing and transformation (ETL) before being loaded.
-Example: Teradata, which made its debut in the late 1970s, stands as a pioneering example of a data warehousing solution.

=> Historical Note: Gained prominence in the 1980s and 1990s, meeting the growing analytical needs of businesses.

2️⃣ Data Lake:

-🌊 Home to Raw Data: Has the capacity to house vast amounts of raw data, whether structured, semi-structured, or unstructured.
-⏱ Schema-on-Read: Data structure is defined at the time of reading or querying.
-🛠 ELT Process: Data is first stored and then transformed.
Example: Amazon S3, launched in 2006, serves as a popular choice for constructing data lakes.

=> Historical Note: Rose to prominence in the 2010s, coinciding with the surge in big data and diverse data sources.

3️⃣ Lakehouse:

-🏠 Hybrid Approach: Combines elements from both Data Warehouses and Data Lakes.
-📊 Unified Platform: Supports both business intelligence and machine learning functions.
-🛠 Data Quality Assurance: Maintains consistent data quality standards.
-Example: Databricks Delta Lake, introduced in the late 2010s.

=> Historical Note: A relatively recent concept, it addresses the bridging of gaps between data lakes and warehouses.

4️⃣ Data Mesh:

-🌐 Decentralized Model: Advocates for decentralized data ownership, focusing on domain-oriented data control.
-🚀 Built for Scalability: Designed to thrive within modern distributed systems and microservices architectures.
-🤝 Encourages Collaboration: Emphasizes cross-team cooperation.
-Example: Think of it more as a paradigm than a specific product, akin to how 
microservices transformed traditional application architecture through decentralization.

=> Historical Note: Gained attention in the early 2020s, evolving from lessons learned from prior data architecture paradigms.

Please share your thoughts!

cc: Brij kishore Pandey ❤️

#DataManagement #DataWarehouse #DataLake #Lakehouse #DataMesh #BigData #dataengineering #softwareengineering

Coding is the one thing that I started in college life to increase my problem-solving skills and get a good job and I achieved that.

I am currently working as a Developer at DXC Technology, and working on Mainframes technologies like COBOL, JCL, and VSAM. I love to solve Data Structures and Algorithms problems so for this I am learning new concepts in DSA.

Also know some other skills like PYTHON, CSS, HTML, and MYSQL.

Recently I have started following my hobby which is Blogging, where I  am proving updates or notifications on drives and helping students to get their dream jobs in MNC's companies.

Rajesh Kumar

STOP delivering dashboards that explore the dataset

START delivering dashboards that answer business problems

Look, my first dashboard was me just throwing stuff into charts

Without a goal in mind

I was just looking for trends

In the real world, this is not how it works

You will most likely have a question to solve before gathering your data

Do this before you create your next project and it will be more refined

You got this

I'll help you land a career in Data Analytics | Datadad.io | Data Analyst

𝗦𝗤𝗟 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗚𝘂𝗶𝗱𝗲✅

Comprehensive Notes for SQL Enthusiasts! 

FREE PDF- telegram.me/notesgallery1
Download PDF- telegram.me/codingbugs

Are you ready to level up your SQL skills?

Swipe the post to grab amazing notes and embark on a transformative SQL learning journey! 

Follow me Amit Pandey for more  amazing content.
Follow me Amit Pandey for more  amazing content.

#SQLMastery #DatabaseManagement #LearnSQL #DataGeeksUnite #ContinuousLearning

8+ millions view 🥇10k linkden family ❣️ software engineer || Front-end Developer || HTML || CSS JavaScript|| Python || SQL || UX/UI ll Content Creatorll

SQL Complete ✨🌟❣️

Master Your Data Game with these Free Resources!

Thanks to Nollie Maoto for compiling this invaluable list. If you're looking to upskill in areas like STATISTICS, EXCEL, PYTHON, R, SQL, POWER BI, and TABLEAU, dive into these resources:

STATISTICS
Read: https://lnkd.in/dfczXFe8
Watch: https://lnkd.in/dPF4RKCk

MICROSOFT EXCEL
Read: https://lnkd.in/d-9FnuMw
Watch: youtu.be/Vl0H-qTclOg

MICROSOFT POWER BI
Read: https://lnkd.in/dBJBfgT9
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TABLEAU
Read: https://lnkd.in/dZ6cS7nB
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SQL
Read: https://lnkd.in/dSvunG-r
Watch: youtu.be/HXV3zeQKqGY
youtu.be/9URM1_2S0ho
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PYTHON
Read: https://lnkd.in/dwHNWMDG
Watch: https://lnkd.in/dF-BCxJa
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R
Read: https://lnkd.in/dCjdmSaB
Watch: youtu.be/_V8eKsto3Ug

Nollie also gave these tips: Absorb these materials at your own pace. Avoid rushing through; depth over breadth always wins. And remember, consistent practice is the key. You'll hone your skills not just by watching or reading, but by doing!

Feel free to share this wealth of knowledge with others. 🌟

#DataScience #TechResources #Learning #Statistics #Excel #Python #Rprogramming #SQL #PowerBI #Tableau #Upskill #PracticeMakesPerfect

Data Analyst: Analyzing data with a passion to drive informed business and economic decisions for forward-thinking companies.

In a world increasingly powered by data, the manufacturing industry stands as a colossal data powerhouse generating an average of 𝟏.𝟗 𝐩𝐞𝐭𝐚𝐛𝐲𝐭𝐞𝐬 of data every year (nearly double the next highest industry according to McKinsey Global Institute). The lion's share of data comes from supply chains, the strategic sourcing processes, the multifaceted operations within factories, and the stringent stages of compliance and quality management. (Source: https://lnkd.in/eHZyTCBx)

The question is – Are you converting that data into information?

Unfortunately, the Industrial Internet Consortium estimates that companies have discarded 𝟗𝟗% of that information without any attempt to make use of it. (Source: https://lnkd.in/ewreZSHB).

Why does this matter? Because there is gold in that data! The University of Texas-Austin found that a 𝟏𝟎% 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in data usability could result in the average Fortune 1000 company's revenue increasing by more than $2 billion. (Source: https://lnkd.in/euWZxw8h)

What can you do?

𝐃𝐨-𝐈𝐭-𝐘𝐨𝐮𝐫𝐬𝐞𝐥𝐟 (𝐃𝐈𝐘) 𝐌𝐞𝐭𝐡𝐨𝐝:
Open-source, end-to-end reference implementation leveraging open industrial standards and digital twin technology created by Erich Barnstedt
https://lnkd.in/eAf95Wyi

𝐏𝐫𝐞𝐛𝐮𝐢𝐥𝐭 𝐌𝐞𝐭𝐡𝐨𝐝:
The Smart Manufacturing Innovation Platform by CESMII provides secure connectivity and intelligent data management using Profiles, streamlining middleware needs and offering capabilities like data ingestion, workflow orchestration, and an integrated marketplace.
https://lnkd.in/e58DtJni

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How to add PDFs data into Excel without Copying?

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#microsoftexcel #exceltips #exceltricks #microsoft #google #students #jobseekers #india #linkedin 

Profile summary:-
Thanks for Visiting My Profile.
I have 4+ Years of experience in QA department, Currently designated Test Engineer in Doxa holding international Singapore. 
- Good knowledge of the Software   Development Life Cycle (SDLC). 
- Excellent understanding of the Software Testing Life Cycle (STLC). 
- Excellent Understanding of the  Defect Life Cycle 
- Knowledge of the defect management tool JIRA.
- Familiar with Agile methodology.

- Performed Manual Testing including:
 Functional, Sanity, Smoke, Positive, Negative, Integration, Regression, & Retesting

- Involved in Test Scenario Design, Test Case Design, and Test Case Execution.

- Participated in DSM, Sprint planning, review & retrospective meetings.

- Used Tools such as Postman, & IRIS

Kiran Pitambar Bharambe

How to start a career in data? 
 
 
Jumping into the data world might seem intimidating, but let's decode the biggest hurdles together and make it easier! 
 
1. Mastering the Learning Curve:  
Data isn't just numbers - it's a language of its own. The initial steep learning curve might seem like a mountain, but remember - the view is best from the top! 
 
2. Handling Data Overload: 
In a world where 328.77 million terabytes of data are produced every day, it's easy to feel overwhelmed. But remember, it's not about quantity, but the quality of insights you can extract. 
 
3. Embracing the Uncertainty: 
Data can be messy, and it might not always tell you what you want to hear. Embrace the uncertainty, and let it guide you towards innovative solutions. 
 
4. Constantly Evolving Tech: 
The pace of change in data-related tech can be dizzying. But every new tool is an opportunity to fine-tune your skill set, and stay ahead of the curve. 
 
5. Bridging the Gap Between Data and Decisions: 
Transforming raw data into actionable insights is a challenging but rewarding journey. Remember, your analysis can drive impactful decisions. 
 
6. Communication is Key: 
Expressing complex data insights in a simple, understandable way can be a tough nut to crack. But your ability to do so can make you a data superstar! 
 
Starting a career in data might be a challenge, but it's definitely not an insurmountable one, even if like me you do not have a university degree.  
 
Each hurdle is a stepping stone to a rewarding career full of endless possibilities. 
 
Let's not just overcome the hurdles, let's leap over them. 
 
How did you get started in data?

No alternative text description for this image

I'm often asked how to stop others from using the numbers you publish to tell a different story than the one you intended. 

The short answer is, you can't. What every Data Analyst must realise is data can tell different stories based on the context it's examined. For example, a number can appear low or high depending on what it's compared to. This is the subjectivity of data interpretation. 

But to have more control of the data story you tell (or the context you frame your numbers), ask yourself the following question:

🤔  What's the counter data story–is this also true? 

By recognising multiple (true) stories can be found in the same dataset, you take a more critical and open-minded approach to data analysis. Addressing this alternative context when you communicate, can help create a more balanced data story. 

#datastory #datastorytelling #databias #dataanalysis 

-----------------
❤️ Like this post?

It's inspired by a book I'm writing called 'The Data Storyteller's Handbook' 📖
You can find out more about the book here: https://lnkd.in/gWs2kTP

Data Storyteller / Speaker / Trainer / Founder of Rogue Penguin

I am glad to announce that Cisco Nexus Dashboard 3.0 with the new Insights service is now available for download! This is just the first step towards further simplification, faster troubleshooting and improved analytics to make Nexus Dashboard the simplest way to manage and monitor Data Center Networks.
Thanks to the whole team that made it possible!
Cisco #sustainability #datacenter #Nexus #uidesign 

Director, Product Management - Cisco Data Center Infrastructure & Cloud Networking

Taplio

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