#data analytics

[code-es]

Coding resource!

exercism.org

A free website where you get specific problems to methodically learn small concepts of a programming language. Do 10 minutes to 1 hour every day, and then you will keep practicing every day, and you will be able to use the skills you learn in your real projects. They walk you through the problem all the way, and it's a super good way to learn!

#coding #programming #web development #resources #codeblr #progblr #learn to code #software development #python #javascript #js #c++ #c #css #html #resource #data analytics #computer science #cs #compsci

[thalia-pages]

Some useful YouTube channels for studying coding, statistics and math

#data science #data analytics #studblr #codeblr

[turns-out-its-adhd]

AI exists and there's nothing any of us can do to change that.

If you have concerns about how AI is being/will be used the solution is not to abstain - it's to get involved.

Learn about it, practice utilising AI tools, understand it. Ignorance will not protect you, and putting your fingers in your ears going 'lalalala AI doesn't exist I don't acknowledge it' won't stop it from affecting your life.

The more the general population fears and misunderstands this technology, the less equipped they will be to resist its influence.

#ai #artificial intelligence #ai technology #tech #technology #singularity #futurism #datascience #data analytics #data harvesting #manipulation #civil rights #civil disobedience #ai discourse

[lorcanaloser]

I got curious about it and decided to graph out the word counts of fics for the top 5 homestuck pairings, some comments from what I've seen:

I apologize for any butchered pairing names but please understand if Karkat is not involved I do not read it.

All data was taken from AO3 posts in december of 2023, from fics with 4 or less grey/pairing tags.

A single Jake-Dirk fic nearly matched Rose-Kanaya's entire word count, oof.

Like for real I'm shocked Rose-Kanaya didn't get more, like from what I've seen that's a very common source of art?

I'll die happy if these charts inspire anyone to write more content for under-repped pairings.

It's kind of cool that all 5 pairings have (almost) no overlapping characters?

#homestuck #shitpost #data analytics #karkat vantas #dave strider #dirk strider #jake english #john egbert #rose lalonde #kanaya maryam #sollux captor #eridan ampora

[scipunk]

Alien: Covenant (2017) - UIs

#alien covenant #graphic design #user interface #user interaction #graphic art #cyberpunk aesthetic #data analytics #data visualization #scifi #computers #computing

[hmfaysal99]

New Business Marketing Tips And Tricks for Success

Starting a new business can be an exciting endeavor, but it also comes with its fair share of challenges, especially in the competitive landscape of today's market. Effective marketing is crucial for the success of any new venture. Here are four essential marketing tips and tricks to help your new business thrive.

Define Your Target Audience: Before diving into marketing efforts, it's essential to identify and understand your target audience. Define your ideal customer persona by considering demographics, interests, pain points, and buying behaviors. Conduct market research to gather valuable insights that will guide your marketing strategies. Tailoring your messages and campaigns to resonate with your target audience will significantly increase your chances of success.

Once you have a clear picture of your audience, choose the most suitable marketing channels to reach them effectively. Social media, email marketing, content marketing, and pay-per-click advertising are just a few options to consider. Your choice of channels should align with where your audience spends their time online.

Create Compelling Content: Content marketing is a powerful tool for new businesses to establish their brand and build credibility. Develop high-quality, informative, and engaging content that addresses the needs and interests of your target audience. This content can take various forms, including blog posts, videos, infographics, and podcasts.

Consistency is key when it comes to content creation. Develop a content calendar to plan and schedule regular updates. Providing valuable content not only helps you connect with your audience but also boosts your search engine rankings, making it easier for potential customers to find you.

Leverage Social Media: Social media platforms have become indispensable for marketing in today's digital age. Create profiles on relevant social media platforms and engage with your audience regularly. Share your content, interact with followers, and participate in industry-related discussions.

Paid advertising on social media can also be a cost-effective way to reach a broader audience. Platforms like Facebook, Instagram, and LinkedIn offer targeting options that allow you to reach users who match your ideal customer profile.

Monitor and Adapt: Marketing is an ever-evolving field, and what works today may not work tomorrow. To stay ahead of the curve, regularly monitor the performance of your marketing efforts. Analyze key metrics such as website traffic, conversion rates, and return on investment (ROI). Use tools like Google Analytics and social media insights to gather data and insights.

Based on your findings, be prepared to adapt your strategies and tactics. If a particular marketing channel isn't delivering the expected results, reallocate your resources to more promising avenues. Stay up-to-date with industry trends and keep an eye on your competitors to ensure your marketing efforts remain relevant and competitive.

In conclusion, effective marketing is essential for the success of any new business. By defining your target audience, creating compelling content, leveraging social media, and continuously monitoring and adapting your strategies, you can position your new business for growth and long-term success in a competitive market. Remember that success may not come overnight, but with persistence and the right marketing approach, your new business can thrive.

#business #digitalbusiness #digitalmarketing #seo #market analysis #data analytics #marketing #management #sales #learn digital marketing #Digital marketing course #seo expert #business success #businessgrowth

[herpersonafire]

Hey everyone! enjoying my (two) week break of uni, so I've been lazy and playing games. Today, working on Python, I'm just doing repetition of learning the basics; Variables, Data types, Logic statements, etc. Hope everyone has a good week!

#codeblr #coding #python #university #uni life #studying #datascience #data analytics #data analysis #studyblr #student life #study motivation #study blog #student

[spectating-dreamer]

VERY IMPORTANT ANALISYS FOR GAYMERS

A while back i felt a lil annoyed that the hit game Valorant with quite the money pool diddn't have a gay agent and so i put my self in the field, by trying to find the gayest agent. Have fun.

I made earlier this year for the R platform but, here the vibes are better

#give me my well deserved gay Agent : ( #gay #lol #data #data analytics #shitpost #analisys #valorant #memes #cypher #valorant omen #gay memes #annoying the straights #gayming #gaming #video games

[j0shm0]

How we all feeling after Japan? With it bringing in spring versus early fall the weather sure was different which lead to some interesting track/racing conditions in my opinion. Yuki sure did deliver a worldy performance in his home race. But that's not what we are here for, we are here to see how the Rookies are improving in their sophomore years.

Logan in 2023 along with a decent amount of others didn't finish the race, and this year in 2024 he was doing well to pressure for points until lap 41 where he went super wide having to reverse onto the tack and put again for new tyres. But outside that one off track event he was still lapping faster than 2023 overall.

As for Oscar he finished on the podium in 2023 and was not so lucky this year with the improvements across the field with teams like Ferrari on top of George and Checo being on form. Regardless of finishing position it can be seen that Oscar was indeed lapping much much faster then he did last year which just goes to show how tight the field is across the top 5 teams.

#f1 #f1 2024 #f1 analysis #mclaren racing #williams racing #logan sargeant #oscar piastri #data analytics #japan gp 2024 #japanese gp 2024 #suzuka gp 2024 #race lap analysis #f1 2023 #f1 2023 vs f1 2024

[kittygirl255]

 Code Lyoko is notoriously known for placing its main cast in constant danger. Many jokes have come from the fandom about certain characters being more prone to getting into danger than others. Out of curiosity, I decided to find out exactly which character nearly died the most and what attack was the deadliest. Starting from episode 1 ‘Teddygozilla’ to episode 95 ‘Echoes’ and logging each ‘death’ into a google spreadsheet. To simplify it, I’ve chosen to log the main characters, William, Sissi, and Jim. I also added two additional categories, Background Characters, and Special Mentions. Background Characters refers to characters that don’t have a prominent role in either the episode or series but are still mixed up in one of Xana’s attacks, while Special Mentions refer to characters that hold a prominent or semi-prominent role within the episode but are not important enough to get their own category.

One issue I came across right away was determining just what exactly counted as a ‘near-death.’ For example, in episode 7 ‘Image Problem’ Jeremie is thrown down a hatch connecting the scanner room to the computer’s mainframe. Depending on a combination of factors, from where he lands on the mainframe, to where on his body her lands, to the height he fell from, an argument could be made that he could possibly survive, but with severe injuries. To avoid the headache, I will be treating each case with a ‘live or die’ mindset. For Background Characters, I only counted each death as a singular entity to avoid calculating the estimated number of casualties. To calculate the exact toll would require more time and effort than I am willing to put in. With the perimeters set, allow me to showcase the results.

The season opens with ‘Teddygozilla’ with Ulrich and Aelita getting the first two deaths of the series, with Ulrich nearly being crushed to death, and Aelita almost falling into the digital sea. Jeremie, Odd, Yumi, Sissi, and Jim join the roster in episode 2 when a nuclear power plant nearly explodes. Episode 11 becomes the first episode to have a character almost die twice in the same episode, when Aelita almost falls into the digital sea and is later stabbed by a Krabe, and Odd when he almost falls into the digital sea twice in the same episode. Episode 17 ‘Amnesia’ becomes the first episode of the series to not have a single death. Each episode has an average of 3.2 deaths per episode with episode 11 ‘Plagued’ and Episode 16 ‘Claustrophobia’ tie at 8 deaths each, and episode 17 ‘Amnesia’ and episode 25 ‘Code: Earth’ tie for the least amount at 0 deaths. At the end of season one total count comes to 83 with the personal count ending in the following: Jeremie-8, Aelita-5, Ulrich-13, Yumi-9, Odd-8, Sissi-10, and Jim-10.

Yumi and Ulrich begin season 2 off as the first deaths, nearly dying of heat stroke in the season opener ‘New Order.' Episode 30 ‘A Great Day’ marks the first death at the hands of someone possessed by Xana with Ulrich nearly being bludgeoned to death via a xanafied Sissi. Aelita gets the record for most deaths in a single episode, with episode 37 ‘Common Interest’ where she almost dies three times because of the Supercomputer’s battery almost dying. Season 2 also holds another record for having the most deaths in a single episode at a count of 11 in episode 40 ‘Attack of the Zombies.’ That episode also marks the first death William experiences. Season 2 ends with an average of 1.9 deaths per episode, with episode 40 at 11, and episodes 28, 33, 36, 41, 43, and 48 all tied for least amount at 0. Season 2 has 51 deaths in total with the personal count being the following: Jeremie-4, Aelita-5, Ulrich-6, Yumi-8, Odd-7, William-1, Sissi-2, and Jim-2.

I included the 2 parter prequel ‘Xana Awakens’ as part of season 3 to make formatting easier. The first deaths of the season happen within the opening episode ‘Lyoko Minus One’ with Ulrich and Yumi nearly being electrocuted by their xanafied teachers and classmates, and Aelita and Odd nearly falling into the digital sea. Jeremie has his only near death experience of the season in the prequel ‘Xana Awakens,’ also marking the first canological near death of the series. William almost dies twice in this season, both in episode 59 ‘The Secret’ from drowning and blowing up by a bomb. Unlike the past two seasons, Xana’s focus was mostly on the Lyoko warriors this season, with collateral damage being quite low. This season ends with 27 deaths in total and has an average of 2 deaths per episode. Episode 59 ‘The Secret’ brings us the most deaths at 5, while episodes 53, 57, 60, and 65 each have 0 deaths. At the end of the season the count comes to Jeremie-1, Aelita-4, Ulrich-6, Yumi-5, Odd-5, William-2, Sissi-1, and Jim-1.

Season 4 begins with episode 66 ‘William Returns’ with Yumi nearly falling into the digital sea. Ulrich gets his first death of the series in Episode 69 ‘Wreck Room’ with Sissi from nearly falling off of a roof. Odd comes in, in episode 73 ‘Replika’ falling into the digital sea. Jeremie, Aelita, and Jim come in after a meteor crashes into the earth in episode 75 ‘Hot Shower’. Episode 81 ‘A Lack of Goodwill’ brings the first time the entire team nearly dies in a single episode. The final deaths come in episode 94 ‘Fight to the Finish’, with Yumi and Ulrich nearly killed by a Xanafied William, Franz Hopper sacrificing himself, and Xana from Jeremie’s antivirus program. There are a total of 61 deaths in Season 4, and an average of 2 deaths per episode. Episodes 75 ‘Hot Shower’, and 86 ‘Canine Conundrum’, both tie for most deaths at 9 each. Episodes 70, 71,73,74,78,79,83,85,87,89,92, and 95 all have 0 deaths, leaving all tied for the least deaths in the season. The individual death count for the season comes to the following: Jeremie- 5, Aelita- 8, Ulrich- 7, Yumi- 12, Odd- 6, William- 0, Sissi- 5, and Jim- 3.

By the end of the series, there are a total of 222 deaths in total. The episode with the highest count is episode 40 ‘Attack of the Zombies’ at 11. For the personal count, I will be starting from the lowest to the highest. William is at the bottom with a total of 3, Jim next at 16, then Jeremie and Sissi tie at 18, and Aelita at 23. Odd takes third place at 26. Yumi takes second place with 34 deaths in total. Ulrich claims first place, beating Yumi by 1 with 35 deaths at the end.

In the beginning, I set out to find which character and which episode had the highest ‘death count’. I was thoroughly surprised as the series went on, seeing Ulrich, who I had originally thought to have the lowest count, come out in first place. Discovering the total death count at the end of each season was also fascinating. For example, finding out that season 1 had 83 deaths in total. I was aware the first season was one of the most deadly, but seeing the actual number was surprising. As said, these are not the exact number, it is very possible that I have missed counted, or perhaps there was a judgment I had made that you didn’t agree with. At the end of the day, I did this out of curiosity and for fun, and I can only hope you enjoyed reading my findings as I had collected them.

If you would like to see the full graph here is the link:

https://docs.google.com/spreadsheets/d/1jEUk5lypN37hHpK2pzbA95euYr68clNryzQFvgzbVqQ/edit?usp=sharing

#code lyoko #aelita schaeffer #aelita stones #jeremie belpois #yumi ishiyama #ulrich stern #odd della robbia #william dunbar #big data #data analytics

[capitalism-and-analytics]

For anyone currently in school or recently in school

I am doing a few guest lectures at some of the universities in my state and working on my presentation. Curious if anyone has any recommendations of topics they really enjoyed from a past guest speaker that isn't major/field-specific?

#college #college student #studying #studyblr #school #university #accounting #finance #consulting #data analytics #data analysis #data visualization #big data #data

[walkawaytall]

So, I've been a data analyst for like five years and I'm currently working on a degree in data analytics which is...a mixed bag as far as experiences go, but once I get the piece of paper saying I went to school for a thing I already do, they have to give me more money (and my company is paying for most of it), so I'm doing the thing. Anyway, I am currently working on this project for a data quality and validation class, which is quite literally my job, and it is driving me insane.

The project is all about cleaning data to prepare it to be loaded into an existing system due to a company merger, something I happen to be uniquely qualified to do due to personal experience -- more so than even many of my colleagues. That's all fine and good. Except the "existing system", which is straight-up referred to as a "database", is just another tab on the Excel spreadsheet, and nowhere have I seen the clarification "look, we're doing it this way because it's easily accessible, but in no universe should you store a database's worth of information on a spreadsheet, nor is this tab actually a database."

And considering how many "databases" people like to keep on spreadsheets much to the chagrin of every data professional in existence, I just want to talk to whoever wrote this curriculum.

#data analytics #data analyst

[runesinthenight]

Wanna confuse my teacher with me?

I am a grad student. For one of my classes my professor has us research current trends. This is a very non-serious poll but, if you have any further information on why you picked what you did, feel free to let me know. If there are other reasons why you're on here, let me know. If you have honestly no idea why you're here, that's fair.

I talked about sexual content on AO3 last week so at this point, anything's on the table.

#I'll read all your answers #I want to confuse my teacher #He didn't know what AO3 or angst was #He will not understand Tumblr #It will be glorious #Write as much or as little as you want #Do a dissertation on why you use Tumblr #make me regret posting this #have fun with this #college #grad school #grad student #homework #data analytics #tumblr #tumblr polls #polls

[anishmary]

In the subject of data analytics, this is the most important concept that everyone needs to understand. The capacity to draw insightful conclusions from data is a highly sought-after talent in today's data-driven environment. In this process, data analytics is essential because it gives businesses the competitive edge by enabling them to find hidden patterns, make informed decisions, and acquire insight. This thorough guide will take you step-by-step through the fundamentals of data analytics, whether you're a business professional trying to improve your decision-making or a data enthusiast eager to explore the world of analytics.

Step 1: Data Collection - Building the Foundation

Identify Data Sources: Begin by pinpointing the relevant sources of data, which could include databases, surveys, web scraping, or IoT devices, aligning them with your analysis objectives. Define Clear Objectives: Clearly articulate the goals and objectives of your analysis to ensure that the collected data serves a specific purpose. Include Structured and Unstructured Data: Collect both structured data, such as databases and spreadsheets, and unstructured data like text documents or images to gain a comprehensive view. Establish Data Collection Protocols: Develop protocols and procedures for data collection to maintain consistency and reliability. Ensure Data Quality and Integrity: Implement measures to ensure the quality and integrity of your data throughout the collection process.

Step 2: Data Cleaning and Preprocessing - Purifying the Raw Material

Handle Missing Values: Address missing data through techniques like imputation to ensure your dataset is complete. Remove Duplicates: Identify and eliminate duplicate entries to maintain data accuracy. Address Outliers: Detect and manage outliers using statistical methods to prevent them from skewing your analysis. Standardize and Normalize Data: Bring data to a common scale, making it easier to compare and analyze. Ensure Data Integrity: Ensure that data remains accurate and consistent during the cleaning and preprocessing phase.

Step 3: Exploratory Data Analysis (EDA) - Understanding the Data

Visualize Data with Histograms, Scatter Plots, etc.: Use visualization tools like histograms, scatter plots, and box plots to gain insights into data distributions and patterns. Calculate Summary Statistics: Compute summary statistics such as means, medians, and standard deviations to understand central tendencies. Identify Patterns and Trends: Uncover underlying patterns, trends, or anomalies that can inform subsequent analysis. Explore Relationships Between Variables: Investigate correlations and dependencies between variables to inform hypothesis testing. Guide Subsequent Analysis Steps: The insights gained from EDA serve as a foundation for guiding the remainder of your analytical journey.

Step 4: Data Transformation - Shaping the Data for Analysis

Aggregate Data (e.g., Averages, Sums): Aggregate data points to create higher-level summaries, such as calculating averages or sums. Create New Features: Generate new features or variables that provide additional context or insights. Encode Categorical Variables: Convert categorical variables into numerical representations to make them compatible with analytical techniques. Maintain Data Relevance: Ensure that data transformations align with your analysis objectives and domain knowledge.

Step 5: Statistical Analysis - Quantifying Relationships

Hypothesis Testing: Conduct hypothesis tests to determine the significance of relationships or differences within the data. Correlation Analysis: Measure correlations between variables to identify how they are related. Regression Analysis: Apply regression techniques to model and predict relationships between variables. Descriptive Statistics: Employ descriptive statistics to summarize data and provide context for your analysis. Inferential Statistics: Make inferences about populations based on sample data to draw meaningful conclusions.

Step 6: Machine Learning - Predictive Analytics

Algorithm Selection: Choose suitable machine learning algorithms based on your analysis goals and data characteristics. Model Training: Train machine learning models using historical data to learn patterns. Validation and Testing: Evaluate model performance using validation and testing datasets to ensure reliability. Prediction and Classification: Apply trained models to make predictions or classify new data. Model Interpretation: Understand and interpret machine learning model outputs to extract insights.

Step 7: Data Visualization - Communicating Insights

Chart and Graph Creation: Create various types of charts, graphs, and visualizations to represent data effectively. Dashboard Development: Build interactive dashboards to provide stakeholders with dynamic views of insights. Visual Storytelling: Use data visualization to tell a compelling and coherent story that communicates findings clearly. Audience Consideration: Tailor visualizations to suit the needs of both technical and non-technical stakeholders. Enhance Decision-Making: Visualization aids decision-makers in understanding complex data and making informed choices.

Step 8: Data Interpretation - Drawing Conclusions and Recommendations

Recommendations: Provide actionable recommendations based on your conclusions and their implications. Stakeholder Communication: Communicate analysis results effectively to decision-makers and stakeholders. Domain Expertise: Apply domain knowledge to ensure that conclusions align with the context of the problem.

Step 9: Continuous Improvement - The Iterative Process

Monitoring Outcomes: Continuously monitor the real-world outcomes of your decisions and predictions. Model Refinement: Adapt and refine models based on new data and changing circumstances. Iterative Analysis: Embrace an iterative approach to data analysis to maintain relevance and effectiveness. Feedback Loop: Incorporate feedback from stakeholders and users to improve analytical processes and models.

Step 10: Ethical Considerations - Data Integrity and Responsibility

Data Privacy: Ensure that data handling respects individuals' privacy rights and complies with data protection regulations. Bias Detection and Mitigation: Identify and mitigate bias in data and algorithms to ensure fairness. Fairness: Strive for fairness and equitable outcomes in decision-making processes influenced by data. Ethical Guidelines: Adhere to ethical and legal guidelines in all aspects of data analytics to maintain trust and credibility.

Data analytics is an exciting and profitable field that enables people and companies to use data to make wise decisions. You'll be prepared to start your data analytics journey by understanding the fundamentals described in this guide. To become a skilled data analyst, keep in mind that practice and ongoing learning are essential. If you need help implementing data analytics in your organization or if you want to learn more, you should consult professionals or sign up for specialized courses. The ACTE Institute offers comprehensive data analytics training courses that can provide you the knowledge and skills necessary to excel in this field, along with job placement and certification. So put on your work boots, investigate the resources, and begin transforming.

#data analytics #data analysis #data management #big data

[lorcanaloser]

Karkat pairings wordcounts in 2023: Visualized

This data was collected by tallying up the wordcounts for various fics on ao3 based on the following criteria:

Karkat X homestuck character pairings (not all of them, just the ones I could really think of at the time)

The fic has less than 5 grey 'pairing' tags on it

The fic was posted in 2023

I am not making any judgement call with this, but simply want to show people the distribution of work that had been done, with the following caveat: DaveKats word count was from December alone.

#homestuck #data analytics #shitpost #jadekat #rosekat #junekat #roxykat #solkat #nepkat #karezi #vriskat #kankat #erikar #fefkat #davekat #karkat vantas

[emmaformuladata]

[06/03/2024 Day 3/100]

I forgot to post this morning, but better late than never✌🏼

Today my brain has been melted with maths. I feel I might have to just accept that this stuff I’m studying is horrid and it’s going to take much longer to understand than I originally thought 🙃

I managed to get some more reading done for another module which I didn’t plan to do today but I had to just change up what I was doing because I was losing motivation (and the will to live💀).

Also got back in to better habits of drinking my greens and drinking water before I have caffeine so, proud of me for that. Still keeping up with the Skin + Me routine too so hopefully I’ll start seeing a difference relatively soon.

But yea, all in all, relatively productive. I’m going to read some more of my book. Currently reading More Perfect by Temi Oh. It’s a dystopian fiction and so far I’m loving it, but it is a lengthy one so will update at *some point* in the future.

In terms of plans for tomorrow, I just want to continue being productive with regards to uni, I would also like to add in some movement tomorrow because that’s an area I have been severely lacking in recently.

#100 days of productivity #journal #datascience #data analytics #university #study aesthetic #studyblr #study blog #blogging #blog #study motivation #study #studyspo #reading