#Data Visualisation With Tableau

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[zora28]

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[techsoulculture]

Using Tableau for Data Visualisation - A Guide 2023

Data visualization is a powerful tool that enables us to visually depict complex data, making it easier to grasp and interpret Tableau

[mplus-weekly]

Dragonflight Season 2 Week 16

At this point in the season, the team meta is largely fixed. That is, m+ teams are largely made of the same player specs and are unlikely to change. So, instead of exploring on the distribution of player specializations this week, I wanted to try a KPI-style dashboard for the different dungeons, comparing the top 4000 dungeons run from week 15 to week 16.

This design choice is largely inspired by the numerous KPI dashboards I have seen on LinkedIn over the past week. Further, KPI dashboards are some of the most staple dashboards in data analytics, so what better place to start?

The style of this dashboard aims to be simple and minimalistic, featuring only numbers comparisons between the number of dungeons run. The area chart represents the number of dungeons in week 15, and the line chart represents week 16. A more detailed comparison is made at the bottom of the dashboard for each dungeon, and the percent change compared to the previous week.

We see that The Vortex Pinnacle (VP), which is a very unpopular dungeon, is even more unpopular during Fortified week. This is expected, since the Fortified affix buffs trash mobs, and the trash mobs in VP are already notoriously hard and difficult to gather.

Another interesting trend is the decrease in popularity of The Underrot, which has many trash mobs that require kicks and stops. Mistakes on Underrot trash mobs can therefore snowball into a full team wipe very quickly. Dungeons with more difficult trash can be correlated with a decrease in popularity on Fortified weeks.

Other than the dungeons mentioned above, dungeons are generally more popular during Fortified weeks compared to Tyrannical weeks.

I am quite happy with how this dashboard turned out. If I had to remake this, I would probably use a bar chart with bars for each individual dungeon for the main chart. A bar chart can summarize more information and would reduce the sheer amount of unused space of the area chart. I would also like to include filters for player class and spec.

[yourdreamilymassivebouquetthings]

Hi Friends,

Tableau is a trending BI tool in current job market which allows us to convert data into pictorial representation. To know more about it, please check out this tutorial. It helps you to understand what Tableau is , how and where it can be used. How easy it is to plug in and use this tool for your analysis and get business insights in few minutes.

Don't forget to like , share and subscribe. Thanks for watching. Have a nice day :)

[analyticspursuit]

Data Visualization: Key to Making Data Relatable

The last time you looked at a table of data and thought "I'm just not sure how to make sense of this," you weren't alone. It's not that the information was unimportant, or even that the topic was particularly complicated—it's just that you weren't sure how to present it in a way that would help you understand what it meant and how it could be used.

That's where data visualization comes in. Data visualizations are charts and graphs that take data from a variety of sources and turn them into something you can see, understand, and act on. They can be simple bar charts or complex scatterplots, but they all have one thing in common: they make information more accessible than ever before.

In this short video, we're going to explore why data visualizations are so important for businesses today, how they work, and where you can find them online!

[buffydataviz]

BTVS speaking time (racing bar chart)

Finally get to post something I created! Eventually I will make a Tableau dashboard to dig into this data, but for now here is a very rough racing bar chart, showing the top 11 BTVS characters by speaking time, per episode. This is built on the work done by J. Freedland in GitHub, which I linked to previously. Details below the cut.

When I googled how to create a racing bar chart for free, I found something called Flourish. It was very easy to create, so I decided to go ahead and make a rough version of this. (No coding required if you want to create your own. This post sadly not sponsored.) The colors on this aren't great, to keep it legible I limited it to the top 11 characters, it lacks captions, and it lacks images (faces) for the top characters. I can actually fix all that in Flourish but don't have the time at the moment. BUT you get the point.

Notes:

This is cumulative speaking time per episode for each character, in seconds. (The original J. Freedland data did the work of compiling this for each character for each episode.)

Angel and Angelus are treated separately in the original J. Freedland data, but I chose to combine them into one character labeled "Angel." This is consistent with how Spike's speaking time is treated, with only one entry. (I also deleted the original Flourish bars for Angel and Angelus, you can only see the combined bar here.)

For whatever reason the original J. Freedland data combines E01 and E02 for S06 into one entry.

I have not spot checked the J. Freedland data with a stopwatch, but from reviewing it at a high level it seems accurate.

ADDED 09/01: Joyce has speaking time in S7, but this is the First using her body. So note that this data source credits First's speaking time to the character the First is using. (You can also think of it as this data using the actors and not the characters.)

Happy to share the source data (currently in excel) upon request, however it is really just the raw csv from J. Freedland's GitHub in excel, and then basic math making it cumulative to put into Flourish.

If you have any requests for this type of chart for BTVS let me know! It would be relatively easy to re-create this for specific characters and / or focus on specific seasons.

[tyrannosaurus-maxy]

Heyy :) I love your ao3 f1 data analysis <3 I was wondering what kind of programs/languages you use for the data scraping, analysis and visualization?

Thank you! I used ParseHub to do the data scrapping, and visualisation on Flourish. I usually would use Tableau but it's on my work computer and no way am I uploading this dataset onto it 😭

[ilikedata]

22/7/22

A much needed blog to keep myself accountable

Goals to achieve by the end of August:

Complete SQL fundamentals track w/DataCamp

Complete, and post, at least two projects (SQL)

Complete PowerBI fundamentals track w/DataCamp

Start Tableau fundamentals track w/DataCamp

Do one MakeoverMonday data visualisation challenge

Confidence & progress so far:

Excel 8/10, just need to learn some basic VBA

SQL 3/10, finished Intro to SQL & Joining Data in SQL. My DataCamp result was intermediate, I don't think I'm anywhere near intermediate yet. I did manage a guided project this week, with some difficulty; this is mostly forgetting silly things like making sure spelling is correct.

PowerBI 0/10, never used

Tableau 0/10, can't remember a thing but I remember it was cute soooo...

R 1/10, can't remember much but not completely scared of it

Python 0/10, send help

[excelrsolutionshyderabad]

Data Analyst Course in Mumbai

Mastering Essential Skills: What to Expect from a Data Analytics Course in Pune

Pune, known as the Oxford of the East and a burgeoning tech hub in India, is witnessing a growth in demand for skilled professionals in data analytics. As industries increasingly depend on data-driven insights to make strategic decisions, the need for proficient data analysts has never been greater. To meet this demand, many educational institutions in Pune offer specialised Data Analyst Courses to equip professionals with the skills and knowledge needed to thrive in this dynamic field. Let's explore what one can expect from a Data Analyst Course in Pune and how it can pave the way for a perfect career in data analytics.

1. Comprehensive Curriculum Tailored to Industry Needs:

A Data Analyst Course in Pune typically offers a detailed curriculum that covers a wide range of topics relevant to data analytics. From foundational concepts in statistics and probability to advanced data visualisation and machine learning techniques, students gain a deep understanding of the data analysis process. Moreover, these courses often incorporate real-world case studies and practical projects, allowing students to implement their knowledge to solve complex business problems. By focusing on industry-relevant skills and emerging trends, the Data Analyst Course in Pune ensures that graduates are well-prepared to meet the rising demands of the job market.

2. Hands-On Learning Experience:

One of the key highlights of a Data Analyst Course in Pune is the emphasis on hands-on learning. Students can work with industry-standard tools and technologies commonly used in data analytics. Whether conducting data analysis using Python, R, or SQL or creating interactive visualisations with Tableau or Power BI, students gain practical experience directly applicable to their future careers. Moreover, many courses offer internship opportunities or capstone projects where students can collaborate with industry partners to tackle real-world challenges, further enhancing their practical skills and industry exposure.

3. Expert Faculty Guidance and Mentorship:

In Pune's vibrant educational landscape, a Data Analyst Course is led by experienced faculty members who are experts in their respective fields. These instructors bring industry experience and academic expertise to the classroom, providing students invaluable guidance throughout their learning journey. Moreover, many courses offer mentorship programs where students receive one-on-one advice from industry professionals, helping them navigate their career paths and build valuable networks within the data analytics community. By learning from seasoned professionals, students gain a deeper understanding of industry best practices and rising trends, preparing them for success in their future careers.

4. Career Development and Placement Support:

Recognising the importance of career development, a Data Analyst Course offers dedicated placement assistance and support services to help students kickstart their careers in data analytics. From resume writing workshops to mock interviews and networking events, these programs provide students with the tools and resources to secure internships and job opportunities. Moreover, many courses have strong industry partnerships with leading organisations in Pune's thriving tech sector, facilitating internship and job placements for their graduates. By leveraging these resources, students can confidently transition from the classroom to the workplace and embark on a rewarding career in data analytics.

5. Lifelong Learning and Professional Growth:

In the fast-paced field of data analytics, continuous learning is crucial for staying competitive. Data Analyst Course instils a lifelong learning and professional growth culture, equipping students with the skills and mindset needed to adapt to changing technologies and trends. Many courses offer alum networks, continuing education programs, and access to online resources and communities, allowing graduates to stay connected with their peasants and continue expanding their knowledge beyond the classroom. By leveraging a culture of curiosity and innovation, the Data Analyst Course in Pune empowers students to pursue satisfying careers and make meaningful contributions to data analytics throughout their professional lives.

In conclusion, a Data Analyst Course in Pune offers a profound learning experience that prepares students for success in data analytics. With a focus on industry-relevant skills, hands-on learning, expert guidance, career development, and lifelong learning, these courses provide aspiring data analysts with the essential tools and resources needed to thrive in Pune's dynamic tech landscape. Whether you're a recent graduate looking to enter the field or a seasoned professional seeking to upskill, a s can provide you with the knowledge and expertise needed to excel in the exciting world of data analytics.

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Professional Data Analytics Course in-depth in Rohini

The significance of data analytics in today's data-driven environment cannot be emphasised. Businesses in a variety of sectors use data analytics to improve operations, make wise decisions, and obtain a competitive edge. Consequently, being skilled in data analytics has become very desirable. Aspiring professionals who want to work in the field of data analytics can use this article as a thorough guide.

Comprehending Data Analytics Analysing datasets to make inferences about the information they hold is known as data analytics. Data analysis, which is a crucial component of many corporate operations, including supply chain management, marketing, and finance, requires the use of specialised systems and software.

Data analytics encompasses several key components:

Data Collection: Gathering relevant data from various sources.

Data Cleaning: Ensuring the data is accurate and usable by removing errors and inconsistencies.

Data Analysis: Applying statistical and computational techniques to extract insights.

Data Interpretation: Understanding and communicating the significance of the analyzed data.

Data Analytics Types Four main categories of data analytics exist: Using historical data, descriptive analytics describes what has happened in the past. The simplest type of analytics includes summarising data in order to identify trends or patterns. Investigates the causes of historical occurrences using diagnostic analytics. In order to comprehend cause-and-effect relationships and the reasons behind specific outcomes, it digs deeper into the data. Predictive analytics forecasts future events by utilising statistical algorithms and past data. This kind uses methods like data mining and machine learning to forecast patterns and actions. Prescriptive analytics: Makes suggestions for actions based on information. It not only forecasts future events but also recommends the optimal path of action to take in order to reach the intended results.

Join our comprehensive Data Analytics course in Rohini Gain hands-on experience with tools like Python, R, and Tableau. Learn data collection, cleaning, analysis, and visualization techniques. Ideal for beginners and professionals seeking to enhance their skills. Unlock career opportunities in a data-driven world with expert guidance and practical projects. Enroll now to transform your data proficiency!

The Importance of Data Analytics

Data analytics is crucial for several reasons:

Informed Decision-Making: By analyzing data, organizations can make more informed decisions, minimizing risks and uncertainties.

Operational Efficiency: Analytics can identify inefficiencies and areas for improvement, leading to optimized operations and reduced costs.

Customer Insights: Understanding customer behavior and preferences allows for better-targeted marketing and improved customer satisfaction.

Competitive Advantage: Companies that effectively use data analytics can gain a significant edge over competitors who do not.

Essential Knowledge for Data Analysts Aspiring professionals in data analytics require a blend of hard and soft skills to thrive. Among the most crucial abilities are the following:

Statistical Analysis: Analysing data and coming to relevant findings requires a basic understanding of statistical procedures. Programming: To manipulate and analyse data, one must be proficient in languages like Python, R, and SQL. Data visualisation: Effective communication of insights requires the capacity to show data in an aesthetically pleasing and comprehensible manner. Commonly used tools include Tableau, Power BI, and D3.js. Machine Learning: Prescriptive and predictive analytics require an understanding of machine learning techniques and algorithms. Critical Thinking: Data interpretation and strategic decision-making depend heavily on one's capacity for critical thought and problem-solving.

Tools and Technologies in Data Analytics

A variety of tools and technologies are available to assist data analysts in their work. Some of the most commonly used include:

Excel: A fundamental tool for data analysis and manipulation.

SQL: Essential for querying and managing databases.

Python and R: Popular programming languages for data analysis and machine learning.

Tableau and Power BI: Leading tools for data visualization.

Hadoop and Spark: Frameworks for handling big data.

TensorFlow and PyTorch: Libraries for machine learning and deep learning.

The Data Analytics Process

The data analytics process typically involves several key steps:

Define the Objective: Clearly articulate the problem or question to be addressed through data analysis.

Data Collection: Gather relevant data from various sources, ensuring it is accurate and comprehensive.

Data Cleaning: Clean the data to remove any errors, inconsistencies, or missing values.

Data Exploration: Explore the data to understand its structure, identify patterns, and determine which techniques to apply.

Data Modeling: Build models to analyze the data and extract insights. This may involve statistical analysis, machine learning, or other techniques.

Interpretation: Interpret the results to draw meaningful conclusions and provide actionable recommendations.

Communication: Communicate findings to stakeholders in a clear and compelling manner, often through visualizations and reports.

Case Studies in Data Analytics

Case Study 1: Netflix

Netflix is a prime example of a company that has successfully leveraged data analytics to drive its business. By analyzing viewing habits and preferences, Netflix can recommend personalized content to its users. This has led to increased viewer engagement and customer retention. Additionally, data analytics helps Netflix decide which content to produce, ensuring high audience satisfaction and return on investment.

Case Study 2: Walmart

Walmart uses data analytics to optimize its supply chain and inventory management. By analyzing sales data and customer purchasing patterns, Walmart can predict demand and manage stock levels more efficiently. This reduces costs and improves customer satisfaction by ensuring products are available when and where customers need them.

Case Study 3: Healthcare

In the healthcare sector, data analytics is used to improve patient outcomes and operational efficiency. For example, predictive analytics can identify patients at risk of developing certain conditions, allowing for early intervention and better management of chronic diseases. Hospitals also use data analytics to streamline operations, reduce wait times, and allocate resources more effectively.

Future Trends in Data Analytics

The field of data analytics is continuously evolving, with several emerging trends shaping its future:

Artificial Intelligence and Machine Learning: AI and machine learning are becoming increasingly integrated into data analytics, enabling more advanced and accurate predictive models.

Big Data: The growing volume of data generated by digital devices and IoT presents both challenges and opportunities for data analytics. Advanced tools and techniques are being developed to handle and extract insights from big data.

Real-Time Analytics: Real-time data analytics is gaining prominence, allowing organizations to make immediate, data-driven decisions.

Data Privacy and Ethics: With the increasing focus on data privacy and ethical use of data, organizations must ensure compliance with regulations and adopt best practices for data governance.

Self-Service Analytics: Tools that enable non-technical users to perform their own data analysis are becoming more popular, democratizing access to data insights.

Conclusion

Data analytics is a powerful tool that drives decision-making and innovation across industries. As the demand for data-driven insights continues to grow, the need for skilled data analysts becomes increasingly critical. By understanding the fundamentals of data analytics, developing key skills, and staying abreast of emerging trends, aspiring professionals can position themselves for success in this dynamic and rewarding field.

The journey to becoming a proficient data analyst involves continuous learning and adaptation. With the right mindset and resources, anyone can harness the transformative power of data analytics to make a significant impact in their organization and beyond.

[mplus-weekly]

Dragonflight Season 2 Week 15

This week, I updated the api call to include runs from US and EU servers, increasing the total number of runs scraped from 2000 to 4000.

For the dashboard design, I wanted to try a more illustrative style that featured bright colours and bold black outlines. Unfortunately, I don't think it worked out. The plain black background was chosen to make the colours pop but feels too overwhelming, and there is little cohesion in the colour palette.

I would like to attempt this style again in the future, perhaps with more planning.