#analytics management

[thedatabull]

Integrated Optimal Data Guidance

We provide a data relationship management tool that enables organizations to manage complex data structures and connections more effectively. Contact us now!

[arabellasdoingthework]

Maybe my fifth try at studyblr. First time was early COVID era lol. Do interact! Maybe some accountability will help me stay more consistent!! It should also help that it's still like 5th week of lectures, not a crazy time like finals lol

Today's tasks:

Revisit Online Marketing notes

Web Analytics Notes

Write Information Law notes based off slides

Play BOTW

[unheavenlycreatures]

so apparently i am the kind of employee where if i go fully MIA for like two weeks due to a medical emergency (meaning that someone needs to step in and cover me for that time) i come back to a promotion and like 12 major things taken off my plate and redelegated

[the-acid-pear]

Y'know this little throwaway gag is so bizarre to me and I know this game is a bit very different to 2 and 3 but look at Matt's reaction when Jack raids the place in 3:

You'd argue he's simply stopping Jack bc he hates this guy and he also hates this job which Could Be True but i highly doubt bc overall despite his virginity and overall cursed vibe, Matt seems to be a good employee, by all means (I mean, Peter literally gave him a vacation instead of firing him in 2, so that says a lot).

Plus, Dave hates this guy as much as he hates him! He literally always calls him creepy and, AND!

This is the only footage you get of the prize corner in 2. Which is also the first game to show Matt and Dave's disdain for one another, Dave being likely more scared of Matt than Matt will ever be of him.

Which is all very curious. 2 does set a drastic change for Matt too with him going from being just strange to outright creepy, so was the old pizza place closing something that actually affected him or was he consistently that creepy all along? And if the later, did he just start hating Dave after that or did they always have beef and they simply had some sort of arrangement (or even higher word from Steven who tended to let Dave do whatever he wanted in general) that let him do so?

[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.

[recsspecs]

27 Exam strategies from 'Cracking the GRE Premium Edition with 6 Practice Tests, 2020' which can also become life tips

As you become more familiar with the test, you will also develop a sense of “the ETS mentality.” This is a predictable kind of thinking that influences nearly every part of nearly every ETS exam. By learning to recognize the ETS mentality, you’ll earn points even when you aren’t sure why an answer is correct. You’ll inevitably do better on the test by learning to think like the people who wrote it.

You’ll do better on the GRE by putting aside your feelings about real education and surrendering yourself to the strange logic of the standardized test.

You might be surprised to learn that the GRE isn’t written by distinguished professors, renowned scholars, or graduate school admissions officers. For the most part, it’s written by ordinary ETS employees, sometimes with freelance help from local graduate students. You have no reason to be intimidated.

Our focus is on the basic concepts that will enable you to attack any problem, strip it down to its essential components, and solve it in as little time as possible.

In many ways, taking a standardized test is a skill and, as with any skill, you can become more proficient at it by both practicing and following the advice of a good teacher.

Think of your GRE preparation as if you were practicing for a piano recital or a track meet; you wouldn’t show up at the concert hall or track field without having put in hours of practice beforehand (at least we hope you wouldn’t!). If you want to get a good score on the GRE, you’ll have to put in the necessary preparation time.

After all, the GRE leaves you no room to make explanations or justifications for your responses.

However, the difficulty of an individual question plays no role in determining your score; that is, your score is calculated by your performance on the entirety of the scored sections, not just a handful of the hardest questions on a given section.

This strategy is called Take the Easy Test First. Skip early and skip often.

On your first pass through the questions, if you see a question you don’t like, a question that looks hard, or a question that looks time consuming, you’re going to walk on by and leave it for the end.

Sometimes, however, a question that looks easy turns out to be more troublesome than you thought. The question may be trickier than it first appeared, or you may have simply misread it, and it seems hard only because you’re working with the wrong information.

Over four hours, your brain is going to get tired.

Once you read a question wrong, however, it is almost impossible to un-read that and see it right. As long as you are still immersed in the question, you could read it 10 times in a row and you will read it the same wrong way each time.

Whether a question is harder than it first appeared, or made harder by the fact that you missed a key phrase or piece of information, the approach you’ve taken is not working.

Reset your brain by walking away from the problem, but mark the question before you do. Do two or three other questions, and then return to the marked problem. When you walk away, your brain doesn’t just forget the problem, it keeps on processing in the background. The distraction of the other questions helps your brain to consider the question from other angles. When you return to the problem, you may find that the part that gave you so much trouble the first time is now magically clear. If the problem continues to give you trouble, walk away again.

Staying with a problem when you’re stuck burns time but yields no points. You might spend two, three, five, or even six minutes on a problem but still be no closer to the answer.

In the five minutes you spend on a problem that you’ve misread, you could nail three or four easier questions. When you return to the question that gave you trouble, there is a good chance that you will spot your error, and the path to the correct answer will become clear. If it doesn’t become clear, walk away again. Any time you encounter resistance on the test, do not keep pushing; bend like a reed and walk away

You should take the easy test first and you should spend most of your time on questions that you know how to answer, or are reasonably certain you can answer.

As a result, it’s better to guess than it is to leave a question blank. At least by guessing, you stand a chance at getting lucky and guessing correctly.

In fact, sometimes it is easier to identify the wrong answers and eliminate them than it is to find the right ones,

Trap answers are specifically designed to appeal to test takers. Oftentimes, they’re the answers that seem to scream out “pick me!” as you work through a question. However, these attractive answers are often incorrect.

Get into the habit of double-checking all of your answers before you click on your answer choice

The only way to reliably avoid careless errors is to adopt habits that make them less likely to occur.

Every time you begin a new section, focus on that section and put the last section you completed behind you. Don’t think about that pesky synonym from an earlier section while a geometry question is on your screen. You can’t go back, and besides, your impression of how you did on a section is probably much worse than reality.

The week before the test is not the time for any major life changes. This is NOT the week to quit smoking, start smoking, quit drinking coffee, start drinking coffee, start a relationship, end a relationship, or quit a job. Business as usual, okay?

Before you dive in, you might wish to take one of the practice tests in this book or online to get a sense of where you are starting from.

Accuracy is better than speed. Slow down and focus on accumulating as many points as possible. Forcing yourself to work faster results in careless errors and lower scores.

[abathurofficial]

Abathur

At Abathur, we believe technology should empower, not complicate.

Our mission is to provide seamless, scalable, and secure solutions for businesses of all sizes. With a team of experts specializing in various tech domains, we ensure our clients stay ahead in an ever-evolving digital landscape.

Why Choose Us? Expert-Led Innovation – Our team is built on experience and expertise. Security First Approach – Cybersecurity is embedded in all our solutions. Scalable & Future-Proof – We design solutions that grow with you. Client-Centric Focus – Your success is our priority.

[womaneng]

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A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

Hey there! 🚀 Becoming a data analyst is an awesome journey! Here’s a roadmap for you:

1. Start with the Basics 📚:

- Dive into the basics of data analysis and statistics. 📊

- Platforms like Learnbay (Data Analytics Certification Program For Non-Tech Professionals), Edx, and Intellipaat offer fantastic courses. Check them out! 🎓

2. Master Excel 📈:

- Excel is your best friend! Learn to crunch numbers and create killer spreadsheets. 📊🔢

3. Get Hands-on with Tools 🛠️:

- Familiarize yourself with data analysis tools like SQL, Python, and R. Pluralsight has some great courses to level up your skills! 🐍📊

4. Data Visualization 📊:

- Learn to tell a story with your data. Tools like Tableau and Power BI can be game-changers! 📈📉

5. Build a Solid Foundation 🏗️:

- Understand databases, data cleaning, and data wrangling. It’s the backbone of effective analysis! 💪🔍

6. Machine Learning Basics 🤖:

- Get a taste of machine learning concepts. It’s not mandatory but can be a huge plus! 🤓🤖

7. Projects, Projects, Projects! 🚀:

- Apply your skills to real-world projects. It’s the best way to learn and showcase your abilities! 🌐💻

8. Networking is Key 👥:

- Connect with fellow data enthusiasts on LinkedIn, attend meetups, and join relevant communities. Networking opens doors! 🌐👋

9. Certifications 📜:

- Consider getting certified. It adds credibility to your profile. 🎓💼

10. Stay Updated 🔄:

- The data world evolves fast. Keep learning and stay up-to-date with the latest trends and technologies. 📆🚀

. . .

[magtecbusinesssolutions]

Transform your business with Magtec ERP! 🌐✨ Discover endless possibilities on a single platform. Book a demo today and see how we can elevate your operations to the next level! 🚀📈

[unnonexistence]

i get to work with time series data at work & im excited about it

[queeraskae]

Sometimes, I still think back on how two years ago my therapist explained the functionality of the Four Basic Emotions to me, and what the required action was for each of them. It was MINDBLOWING. My life has been so much simpler since that day🥺❤️

[calliopeservices2]

AI Reputation Manager : Le Nouveau Métier pour Référencer Votre Entreprise sur les Plateformes IA

L’intelligence artificielle (IA) transforme notre façon de communiquer, d’interagir et même de gérer la réputation des entreprises. Aujourd’hui, un nouveau métier émerge : l’AI Reputation Manager. Ce professionnel est spécialisé dans l’optimisation de la présence et de la réputation d’une entreprise sur les plateformes alimentées par l’IA. Ce rôle s’inscrit désormais dans l’offre de Communication & Marketing Digital de Calliope Services, qui accompagne les entreprises dans leur transformation digitale.

L’intelligence artificielle (IA) transforme notre façon de communiquer, d’interagir et même de gérer la réputation des entreprises. Aujourd’hui, un nouveau métier émerge : l’AI Reputation Manager. Ce professionnel est spécialisé dans l’optimisation de la présence et de la réputation d’une entreprise sur les plateformes alimentées par l’IA. Ce rôle s’inscrit désormais dans l’offre de Communication…

[truetechreview]

Top 5 DeepSeek AI Features Powering Industry Innovation

Table of Contents1. The Problem: Why Legacy Tools Can’t Keep Up2. What Makes DeepSeek AI Unique?3. 5 Game-Changing DeepSeek AI Features (with Real Stories)3.1 Adaptive Learning Engine3.2 Real-Time Anomaly Detection3.3 Natural Language Reports3.4 Multi-Cloud Sync3.5 Ethical AI Auditor4. How These Features Solve Everyday Challenges5. Step-by-Step: Getting Started with DeepSeek AI6. FAQs: Your…

[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.

[ontonix]

Complexity Science is Dead.

The so-called Complexity Science has been around for a few decades now. Chaos, as well as complexity, were buzzwords in the 1980s, pretty much like Artificial Intelligence is today. There are Complexity Institutes in many countries. They speak of Complexity Theory. In a beautiful and recent video called “The End of Science”, Sabine Hossenfelder, speaks of Complexity Science and how it has not…

[ames-rants-here]

so, i am trying to get into finance and data analytics, and i am starting to realize it is a very misogynistic field. shit is getting real. wow