#data analysis process

[spadesurvey]

Data analysis is an essential component of company operations, comprising a diverse range of complicated and technical activities. 

[rndexperts]

Discover the step-by-step data analysis process: from data collection and cleaning to exploration, modeling, and interpretation. Learn key techniques and tools to extract valuable insights from your data effectively.

[romanticize-until-you-drop]

So I just won a competition for my research project…

MOM DAD IM A REAL SCIENTIST!!

[myriad-of-things]

behold! the yugioh duel monsters english dub crimes and kill count list, up to episode 78 because that's where dice and I are in the show. I've set the sheet up so that all stats will increment automatically as I add more episodes

some fun facts:

Yami Bakura and Marik are currently tied for most crimes! But Yami Bakura tries to kill people more often (he's made 14 attempts, 6 of which were successful). Marik hasn't successfully killed anyone yet

The Pharaoh has the highest murder success rate (75%!) this number will go up once we watch s0

Joey is the show's punching bag (we already knew this) with 0 crimes committed to 11 crimes committed against him

Kaiba has the most crimes committed against his person (12), the majority of which are murder attempts (6). he also holds the record for the most attempts on his life

despite being a side character that doesn't actively duel, Téa has an alarming lot of crimes committed against her

the most popular crime is attempted murder! followed by actual murder! then it's kidnapping and mind control (shoutout for Marik for single-handedly carrying that stat)

disclaimer: it's a bit hard to separate crimes against Yugi from crimes against the Pharaoh. one could argue most of the crimes committed against Yugi were actually meant for the Pharaoh. it's also hard to gauge how many times people have tried to kill the Pharaoh, because every duel against him could be counted as attempted murder. as such, I'm counting attempted murder as actual, outside of card game murder. hence why Yugi has more attempts against him than the Pharaoh

[otaku553]

can I ask what ur summer research was about?!!! :0 (u absolutely don't have to answer if ur not comfortable, I'm just a giant nerd and loves to hear about research dhdnfjdmdjfng)

Of course!!! I’d be glad to talk about it!!

In short, my research over the summer was putting mice in mazes and looking at how well they remember mazes, how quickly they learn the maze, and how learning one maze can help them learn other mazes faster. More detailed under the read more!

Previous maze studies with mice have shown that mice can actually learn very quickly when they’re learning behaviors that are in line with their own evolutionary advantages rather than arbitrary associations. It’s the difference between telling a mouse “go around these tunnels and try to find water to survive” vs telling it “do these very specific and completely useless 5 things and then you’ll get water”. Maze navigation for mice is already a natural complex behavior: mice are burrowing rodents and already have the predisposition for running around tunnels. We set up a maze and cameras and infrared lights around it to record it doing its thing, so that we can look at its natural behaviors with no human interference.

The unique thing about this project is less the behavior and rapid learning of the mouse and more the maze that we use for the study, called the Manhattan Maze. I think my mentor created it but I’m not too certain? But the basic concept of it, as shown in the figure above, is that there are two boxes of parallel tunnels and one layer of acrylic in the middle. Through holes in the acrylic, the mouse can climb between the two layers and make a “turn”. Essentially, this maze is the most reconfigurable setup for studying mouse behavior in mazes possible, because the middle layer of acrylic, which we call a mask, can be changed 2^(n^2) ways (n being the number of channels in a layer) to make completely different mazes. For the figure above, a 4x4 Manhattan maze, we have 2^16 possible different configurations, but we were actually running it on an 11x11 maze, so there were 2^121 different possible configurations! This way, we can look at the mouse in tons of different mazes without actually having to make a new maze altogether and transfer the mouse every time.

And they learn extremely fast! The mazes we used were pretty much linear paths with small dead ends that weren’t far off from the main path, and required 9 turning decisions to get from starting point to end point. There were 3 of these different masks, and after training for one day on one of them, the next day, almost all mice that completed the training managed to learn completely new 9-decision masks in 3 hours or less!

[4th-make-quail]

remember that interview i had that i really wanted to get the job for? WELL i'm going in for a THIRD INTERVIEW tomorrow with the fucking MD ahahaha!!! they have 2 roles now, the new one for quality systems engineer which needs lead auditor which i don't have (i have internal auditor), and my agency are repping another candidate for it, so they're trying to push it as being the pair of us working together well cos i have the textiles background and he doesn't

SO LET'S SEE HOW IT GOES I GUESS!!!! annoyingly it's at 1pm which means i'll be travelling during lunchtime which i hate but at least this time i won't have to do a 2hr factory tour sdgkhlf'g

[d0nutzgg]

Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!

I'm super excited.

Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!

[brilliantorinsane]

Belligerent Ghost analysis and data gathering are coming along, and in the meantime I'm curious: what are folks' thoughts/feelings about this episode?

[lazer-exe]

"spotify wrapped was clearly AI"

Two questions. What, exactly, do you think AI is? And did you think spotify had people HAND PICKING your top songs before this???? be for real

[spadesurvey]

Six Steps of Data Analysis Process

Six data analysis processes will assist you in making informed decisions: inquire, prepare, process, analyze, share, and act. Remember that they are distinct from the data life cycle, which explains the changes that data experiences over its existence.

Let’s go over the steps and see how they can assist you address challenges on the profession. This blog gives a full explanation of the data analysis process, including the important processes and recommended practices at each stage.

Steps for the Data Analysis Process:

Step 1: Define the Question

In the first phase of the process, the data analysis is assigned a problem/business job. The analyst must grasp the task and the stakeholder’s expectations for the solution. A stakeholder is someone who has invested their money and resources in a project. The analyst must be able to ask several questions in order to identify the best solution to their situation.

To effectively understand the problem, the analyst must first identify its fundamental cause. The analyst must avoid distractions while examining the situation. Effective communication with stakeholders and coworkers is essential for fully understanding the underlying problem. Questions to ask yourself during the Ask phase are:

What are the challenges raised by my stakeholders?

What are their expectations for the solution?

Step 2. Data Collection

The second phase is to prepare or collect data. This process entails collecting and storing data for future analysis. The analyst must collect data from numerous sources in accordance with the assignment assigned. Data must be obtained from a variety of sources, both internal and external. Internal data is available within the organization for which you work, whereas external data is available from sources other than your organization.

– First-party data refers to data obtained by an individual using their own resources. – Data acquired and sold is referred to as second-party data. – Third-party data refers to data obtained from outside sources. Data is commonly acquired through interviews, surveys, feedback, and questionnaires. The collected data can be saved in a spreadsheet or SQL database.

Step 3: Cleaning the data

After you’ve collected your data, the next step is to prepare it for analysis. This implies cleaning, or’scrubbing’ it, which is critical for ensuring that you’re working with high-quality data. Important data cleansing tasks include:

– Eliminating severe errors, duplication, and outliers—all of which are unavoidable issues when combining data from many sources. – Eliminating unnecessary data points—extracting useless observations that have no influence on the intended analysis. – Adding structure to your data—general ‘housekeeping’, such as addressing typos or layout flaws, to make it easier to map and handle your data. – Filling in large gaps—as you clean up, you may discover that vital data is missing. Once you’ve found gaps, you can start filling them.

Step 4: Analyzing the data

Finally, you have cleared your data. Now comes the fun part: analyzing it! The type of data analysis you perform is primarily determined by your goals. But there are numerous techniques accessible. Some of the terms you may be familiar with include univariate or bivariate analysis, time series analysis, and regression analysis. More important than the different varieties is how you use them. This depends on the insights you want to get. Generally speaking, all sorts of data analysis fall into one of four categories.

Step 5. Data Visualization

The fifth phase is visualizing the data. Nothing is more captivating than a visualization. The altered data must now be represented visually. The reason for creating data visualizations is that there may be people, primarily stakeholders, who are not technical. Visualizations are created to simplify the interpretation of complex data. Tableau and Looker are the two most popular tools for creating stunning data visuals. Tableau is a simple drag-and-drop tool for producing attractive representations. Looker is a data visualization tool that connects directly to a database and generates visualizations.

Step 6. Presenting the Data

Presenting data is putting raw information into a format that is easily understandable and meaningful to many stakeholders. This technique includes creating visual representations, such as charts, graphs, and tables, to effectively communicate patterns, trends, and insights derived from data analysis.

The goal is to make difficult material clearer and more accessible to both technical and non-technical audiences. Effective data presentation necessitates a careful selection of visualization techniques based on the nature of the data and the precise message desired. It progresses from plain display to storytelling, in which the presenter analyzes the findings, emphasizes significant aspects, and leads the audience through the narrative as the data unfolds.

Whether through reports, presentations, or interactive dashboards, the art of presenting data entails striking a balance between simplicity and complexity, ensuring that the audience understands the relevance of the information offered and can utilize it to make informed decisions.

How Spade Survey Helps:

Data is the foundation of any business seeking to develop through digital transformation. From uncovering new business possibilities to mitigating risks, data analysis transforms raw data into actionable insights, giving you a strategic advantage.

Data analysis is an essential component of company operations, comprising a diverse range of complicated and technical activities. Outsourcing data analysis services in India to our skilled team might result in strategic corporate insight and informed decision-making. We employ advanced techniques to evaluate massive amounts of unstructured and structured data, resulting in deep and relevant insights that go beyond mere descriptive statistics. Our data analysis services for small businesses are specifically designed to provide high-value insights that promote growth and competitiveness.

We recognize the sensitivity of your company data and strictly comply with worldwide data protection requirements. Our strong data governance policies ensure that your data is always confidential, secure, and available. By outsourcing your data analysis to us, you benefit from our technical experience, innovative solutions, and secure processes.

Contact us right away to benefit from our innovative data analysis services and obtain a competitive advantage in your industry. Use our data analysis services to make better use of data. We can assist you with collecting, storing, cleansing, visualizing, analyzing, and exploring massive datasets to discover hidden patterns and significant insights.

Conclusion:

In this article, we reviewed the key processes in the data analytics process. These essential processes can be modified, rearranged, and reused as needed, but they serve as the foundation for all data analysts’ work. The data analysis process is a critical framework that transforms raw data into actionable insights in six steps: problem definition, data collection and cleaning, data analysis, results interpretation, and effective communication of findings.

Businesses, researchers, and decision-makers can use this organized method to identify important trends, solve problems rapidly, and make confident data-driven judgments. These methods, whether employed for market research, company strategy, or scientific studies, ensure that data-driven findings are accurate and reliable. Embracing this method enables firms to improve performance, decrease risks, and achieve success in an increasingly data-driven environment. Contact us today to discuss your project and see how our data analysis services may help you make smarter business decisions.

[antlerknives]

currently working on the early stages (ie. user research) of a spotify user interface redesign as a personal portfolio project and i am ridiculously excited about it

[blueish-bird]

spent a few hours writing a pretend video essay about the process/function of conservative media analysis with an extended portion about The Matrix life is good

[chaosintheavenue]

I need to pluck Trin and Vari out from my brain and get them helping me with this analysis :(

[goodoldbandit]

Powering the Future

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in How High‑Performance Computing Ignites Innovation Across Disciplines. Explore how HPC and supercomputers drive breakthrough research in science, finance, and engineering, fueling innovation and transforming our world. High‑Performance Computing (HPC) and supercomputers are the engines that power modern scientific, financial,…

[ghoststellar]

>> Also does anyone have a muse interest tracker that doesn't involve Google, Microsoft or AI services?

[jcmarchi]

From Recurrent Networks to GPT-4: Measuring Algorithmic Progress in Language Models - Technology Org

New Post has been published on https://thedigitalinsider.com/from-recurrent-networks-to-gpt-4-measuring-algorithmic-progress-in-language-models-technology-org/

From Recurrent Networks to GPT-4: Measuring Algorithmic Progress in Language Models - Technology Org

In 2012, the best language models were small recurrent networks that struggled to form coherent sentences. Fast forward to today, and large language models like GPT-4 outperform most students on the SAT. How has this rapid progress been possible? 

Image credit: MIT CSAIL

In a new paper, researchers from Epoch, MIT FutureTech, and Northeastern University set out to shed light on this question. Their research breaks down the drivers of progress in language models into two factors: scaling up the amount of compute used to train language models, and algorithmic innovations. In doing so, they perform the most extensive analysis of algorithmic progress in language models to date.

Their findings show that due to algorithmic improvements, the compute required to train a language model to a certain level of performance has been halving roughly every 8 months. “This result is crucial for understanding both historical and future progress in language models,” says Anson Ho, one of the two lead authors of the paper. “While scaling compute has been crucial, it’s only part of the puzzle. To get the full picture you need to consider algorithmic progress as well.”

The paper’s methodology is inspired by “neural scaling laws”: mathematical relationships that predict language model performance given certain quantities of compute, training data, or language model parameters. By compiling a dataset of over 200 language models since 2012, the authors fit a modified neural scaling law that accounts for algorithmic improvements over time. 

Based on this fitted model, the authors do a performance attribution analysis, finding that scaling compute has been more important than algorithmic innovations for improved performance in language modeling. In fact, they find that the relative importance of algorithmic improvements has decreased over time. “This doesn’t necessarily imply that algorithmic innovations have been slowing down,” says Tamay Besiroglu, who also co-led the paper.

“Our preferred explanation is that algorithmic progress has remained at a roughly constant rate, but compute has been scaled up substantially, making the former seem relatively less important.” The authors’ calculations support this framing, where they find an acceleration in compute growth, but no evidence of a speedup or slowdown in algorithmic improvements.

By modifying the model slightly, they also quantified the significance of a key innovation in the history of machine learning: the Transformer, which has become the dominant language model architecture since its introduction in 2017. The authors find that the efficiency gains offered by the Transformer correspond to almost two years of algorithmic progress in the field, underscoring the significance of its invention.

While extensive, the study has several limitations. “One recurring issue we had was the lack of quality data, which can make the model hard to fit,” says Ho. “Our approach also doesn’t measure algorithmic progress on downstream tasks like coding and math problems, which language models can be tuned to perform.”

Despite these shortcomings, their work is a major step forward in understanding the drivers of progress in AI. Their results help shed light about how future developments in AI might play out, with important implications for AI policy. “This work, led by Anson and Tamay, has important implications for the democratization of AI,” said Neil Thompson, a coauthor and Director of MIT FutureTech. “These efficiency improvements mean that each year levels of AI performance that were out of reach become accessible to more users.”

“LLMs have been improving at a breakneck pace in recent years. This paper presents the most thorough analysis to date of the relative contributions of hardware and algorithmic innovations to the progress in LLM performance,” says Open Philanthropy Research Fellow Lukas Finnveden, who was not involved in the paper.

“This is a question that I care about a great deal, since it directly informs what pace of further progress we should expect in the future, which will help society prepare for these advancements. The authors fit a number of statistical models to a large dataset of historical LLM evaluations and use extensive cross-validation to select a model with strong predictive performance. They also provide a good sense of how the results would vary under different reasonable assumptions, by doing many robustness checks. Overall, the results suggest that increases in compute have been and will keep being responsible for the majority of LLM progress as long as compute budgets keep rising by ≥4x per year. However, algorithmic progress is significant and could make up the majority of progress if the pace of increasing investments slows down.”

Written by Rachel Gordon

Source: Massachusetts Institute of Technology

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