Tableau Data Analysis Project: Sales Insights - Problem Statement

This is the first video for sales insights data analysis project using tableau. This project will give you a feel of how data analysis projects are executed in big companies. This would be perfect for anyone seeking career as a data analyst. Our case study is based on a computer hardware business which is facing challenges in dynamically changing market. Sales director decides to invest in data analysis project and he would like to build power BI dashboard that can give him real time sales insights.
In this introduction video we will discuss problem statement.

#tableau #data-analysis #developer

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Tableau Data Analysis Project: Sales Insights - Problem Statement
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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Angela  Dickens

Angela Dickens


Tableau Data Analysis Tips and Tricks

Tableau is one of the most powerful and popular Data Analysis Tool. Tableau holds almost 14% market of Business Analysis software industries with highest satisfied customer base.

This is one of Tableau Tips and Trick Series, Where I will tell you some of the timesaving shortcuts for data analysis.

I have a few tricks and tips that every tableau user can apply to make their analysis much faster and efficient. So let’s get started.

Tableau Tricks and Shortcuts

Copy and Paste

It is an amazing tool that allows you to just copy your data and paste it for much faster analysis. (Windows: Ctrl + C for Copy, Ctrl + V for Paste) (Mac: Command + C for Copy, Command + V for Paste)

Drag and Drop

Tableau allows drag and drops feature to flat files for Tableau data extracts connections.

Quick Aggregation

Right-click when dragging the measures/dimensions for analysis into the view for quick aggregation options.


To repeat and reuse a measure that already exists in the display, keep down the CTRL key (Command for Mac) when moving it.


Tableau is a powerful and intelligent tool where instead of typing Zeros in large numbers (Thousands or Millions), we can use denominators K and M(K-Thousands, M-Millions).

Zoom-in Shortcut

Use “Ctrl+Shift+Area Selection” to zoom in quickly and easily while looking into huge time data.

Measures Swapping

For swapping the measures in view follow these steps: “Analysis(Menu Bar)” — “Cycle fields”.

Initial Dashboard

Use the “ESC” key to remove all the filters applied on the Analytics Dashboard to revert it Initial State.

Format Dashboard

Menu bar → “Format” → “Format Dashboard” to Change and Titles and Text in the Dashboard


Steal it like a Pro

Tableau has a huge community where thousands of analysis enthusiasts answer questions and publish their own dashboard to the public for reference and use. Making a dashboard from scratch requires a lot of time and tons of creativity but if we are able to align our data with publicly available dashboards then our work is reduced to half.

I recommend taking look into it. Choose the one dashboard you like and try to align your analytical data in a creative manner.

#data-analysis #data-analytics #business-analysis #tableau #data-science #data analysis

Gerhard  Brink

Gerhard Brink


Top 5 Exciting Data Engineering Projects & Ideas For Beginners [2021]

Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it.

You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience.

Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

That’s because you’ll need to complete the projects correctly. Here are the most important ones:

  • Python and its use in big data
  • Extract Transform Load (ETL) solutions
  • Hadoop and related big data technologies
  • Concept of data pipelines
  • Apache Airflow

#big data #big data projects #data engineer #data engineer project #data engineering projects #data projects

Gerhard  Brink

Gerhard Brink


How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

  • Building/collecting data
  • Cleaning/filtering data
  • Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management