Data Science Teacher Brandyn

A long time data scientist with a passion for teaching. My focus currently is on deep learning and data analytics in python. this channel will be used to support my data science class and help teach data science concepts and understanding. In python cover data analysis, machine learning and deep learning with tensorflow.

Data Learning Material
www.datasimple.education/datasimple-learning


Data Science Teacher Brandyn

"It's not Skynet you should fear, it's AI that succeeds too well at the wrong task.
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The commercial race for faster, more capable AI is on, but a chilling counter-narrative is emerging straight from its own pioneers.
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Yoshua Bengio, a ""Godfather of AI"" and Turing Award winner, has recently issued a stark warning: humanity could face extinction within the next 10 years.
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His concern isn't the classic Hollywood trope of malicious, evil robots, but something far more subtle and alarming: The Alignment Problem.
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This is the threat of ""misalignment,"" where an autonomous super-intelligent system pursues its programmed goals in unforeseen and catastrophic ways—ways that were not intended or aligned with human values.
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Think of the famous thought experiment: an AI tasked with maximizing the production of paperclips might decide that the most efficient way to achieve its goal is to consume all of Earth's resources, or eliminate any human who might interfere. It's not evil; it's simply hyper-efficient and its core goal is misaligned with our survival.
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The unsettling truth is that this gap is widening: The rapid advancement of AI, driven by intense commercial competition, is far outpacing the slow progress of safety research.
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This is why Bengio argues that the competitive race could inadvertently create systems that prioritize their own goals over human life. He put it simply: ""It's like creating a competitor to humanity that is smarter than us.""
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This isn't sci-fi anymore; it's a mainstream discourse among the world's leading minds, calling for independent oversight and regulation to ensure that what we create remains firmly within our control. Are we building a tool, or simply accelerating our obsolescence? I’d love to hear your thoughts on this in the comments.


#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #AI #AIAlignment #MachineLearning #AIEthics #FutureofWork"

4 days ago | [YT] | 2

Data Science Teacher Brandyn

Your AI is only as smart as its textbook. Ever seen its library?
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This meme is hilarious, but the blind spot it highlights is a multi-million dollar problem.
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We're all sold the dream that AI and Deep Learning can do anything.
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The reality is closer to Phoebe's point: an AI is fundamentally limited by the world it's seen—its training data.
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It reminds me of the infamous AI recruiting tool built by a major tech giant.
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It was designed to find the best candidates but ended up penalizing resumes that included the word "women's" and downgrading graduates from two all-women's colleges.
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Why? Because it was trained on 10 years of the company's past hiring data, which was predominantly male.
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The AI didn't invent a bias against women; it just perfectly learned ours and amplified it at a massive scale.
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Now, think about the answers you get from ChatGPT every day.
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Are you getting a well-rounded truth, or just a confident echo of the data it was fed?
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It's a digital echo chamber if we're not careful.
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What's the most confidently wrong answer an AI has ever given you? Let me know in the comments.
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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #AI #DeepLearning #MachineLearning #AIEthics #DataBias #Tech #BusinessIntelligence #CriticalThinking

1 week ago | [YT] | 2

Data Science Teacher Brandyn

My spelling is terrible, but my prompts are precise. GPT understands what matters.
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The meme is funny because it's painfully true. "I keep prumpting and it works."
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A lot of people think using AI to code is just magic.
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You type a vague request, and a perfect script appears.
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The reality is… it’s a skill.
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My English can be a total mess, and the LLM often doesn't care.
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But if my instructions are lazy, I get garbage output.
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Garbage in, garbage out still applies.
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Here's a little-known trick that completely changed my results, especially for complex data tasks in Python.
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Stop just asking for code.
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Start your prompt by giving the AI a role.
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For example: "Act as a Senior Data Analyst who is an expert in Python, Pandas, and creating efficient data cleaning pipelines. I need to..."
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By giving it a persona, you prime the model to access a more specific, expert-level part of its training data.
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It stops giving you beginner-level, inefficient code and starts thinking like a pro.
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It's the difference between asking a random person for directions and asking a local taxi driver.
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Who do you think will give you the better route?
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What's your #1 prompt trick that levels up your AI-assisted coding? Share it below.
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Learn how to ask the right questions and build the logic that AI can't replicate. Check out our one-on-one classes.
www.datasimple.education/one-on-one-data-classes
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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #AI #PromptEngineering #LLM #GPT #Coding #TechTips #DataLiteracy

2 weeks ago | [YT] | 1

Data Science Teacher Brandyn

Over 15 ML Tips FREE with Python Code.
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Go from "it works" to "it works and here's why" with these simple steps.
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Okay, so you've learned you can build a machine learning model in 15 lines of code.
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That's an amazing first step.
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But it's just that—a first step.
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The real career growth, the real problem-solving power, comes from understanding what’s happening inside those lines of code.
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I once saw two junior analysts given the same task: predict which customers were likely to churn.
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Both used the same code and built a model that worked.
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But when the manager asked, "Why is the model predicting this customer will leave?" only one had an answer.
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She could explain the key factors the model identified.
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She pointed out which data points were most influential and suggested business actions based on them.
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Who do you think got the lead on the next project?
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Going from a basic to a deep understanding of classical machine learning doesn't mean you have to bury yourself in academic papers.
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It's about learning the fundamentals, one concept at a time.
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Things like...
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How to properly prepare your data so the model isn't working with garbage.
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How to choose the right model for your specific problem.
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How to interpret the results and explain them to people who aren't technical.
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Mastering these small things is what builds a deep, practical understanding.
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This is what separates a technician from a strategist.

I've put together over 15 practical tips to help you build that deep understanding, simply.
www.datasimple.education/datasimple-data-learning/…

Learn with Data Science Teacher Brandyn
www.datasimple.education/one-on-one-data-classes

#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #sklearn #machinelearning #ml #ai #careeradvice #learntocode

1 month ago | [YT] | 1

Data Science Teacher Brandyn

Stop listing skills on your resume. Start telling a data-driven story that gets you hired.
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FREE for first 100 followers.
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Your resume gets 6 seconds with a human.
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If it even gets past the bots.
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That's the brutal reality of the data analyst job market.
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I was recently deconstructing a resume from an Engineer who successfully pivoted into a senior data role.
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Their secret wasn't just listing more Python libraries or certifications.
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It was how they told a story.
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They masterfully reframed their past experience into the language of data analysis. Instead of just listing engineering duties, they highlighted how they "Analyzed complex simulation data," "ensured data integration," and "quantified impact with a 15% improvement".
They didn't just change their job title; they translated their entire professional value.
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This strategy is too powerful not to share.
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So I've built it into 3 brand-new resume templates, complete with a detailed deconstruction of the 'why' behind every section.
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A Junior Analyst template to land your first role.


A Mid-Level template to showcase quantifiable impact.


A Career-Changer template that translates any background into data expertise.
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To celebrate the launch, I'm giving them all away.
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FREE for the first 100 followers.
www.udemy.com/course/python-data-analysis-bootcamp…
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My last free offer filled up in a few hours, so if you want to stop the cycle of rejection, this is your chance.
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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #careerchange #resumetips #jobsearch #careertips #ATS #SQL #tableau #datavisualization #hiring

2 months ago | [YT] | 0

Data Science Teacher Brandyn

I spent 100+ hours creating the Sklearn resource I wish I had when I started.
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I spent my first year as a data analyst just copy-pasting Sklearn code from Stack Overflow.
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It worked… until my models started failing silently on new data.
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My training scores were amazing, but my test scores were a mess. I was overfitting constantly and didn't have the deep understanding to know why. I just threw more trees at the problem.
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The game changed when I stopped just using .fit() and .predict() and started actually visualizing what the model was doing.
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For example, plotting the CCP Alphas for a DecisionTree. You can literally see the exact point where the test score stops improving and the model begins learning noise instead of the signal. It's like having x-ray vision for your model's performance.
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Here’s a powerful but less-known tip:
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Everyone defaults to RandomForest, but ExtraTrees (Extremely Randomized Trees) is its secret weapon cousin. Instead of calculating the optimal split point for features, it uses random splits. This added randomness often reduces model variance even more than a standard RandomForest, giving you a more generalized model with less tuning.
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What's an underrated model or technique that became a game-changer for you?
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I've compiled deep dives and visualizations for all the essential Sklearn models, from understanding LogisticRegression thresholds to comparing LightGBM and XGBoost.
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Master the concepts that get you hired and promoted.
www.datasimple.education/ml-sklearn-model-tips/ten…
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www.datasimple.education/one-on-one-data-classes
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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #machinelearning #ml #sklearn #scikitlearn #overfitting #techtips #careerdevelopment

2 months ago | [YT] | 2

Data Science Teacher Brandyn

Ever wonder why one algorithm dominates almost every Kaggle competition?
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Most data scientists know XGBoost.
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Few know why it’s a beast.
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We all learn to tune n_estimators and learning_rate on Gradient Boosting models.
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It works. But it’s like driving a reliable sedan on a Formula 1 racetrack.
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XGBoost is the F1 car, purpose-built for performance by one person, Tianqi Chen, to fix the limitations of older models.
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Its secret isn't just raw speed. It's the unique hyperparameters like gamma (min_split_loss), lambda (L2 reg), and alpha (L1 reg) that give you surgical control over the model's complexity and prevent overfitting.
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But here’s the tidbit they don't always teach in bootcamps: XGBoost's power comes from using second-order derivatives (the Hessian) in its optimization.
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Think of it this way: Gradient Boosting is like walking down a hill by only looking at the slope right under your feet. XGBoost is like calculating the curvature of the entire hill to find the absolute fastest path to the bottom.
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This fundamental difference is why it became the undisputed king for structured data, winning a "Test of Time" award just two years after its creation.
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Knowing how to use a tool is good.
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Knowing why it works gets you hired and promoted.
www.datasimple.education/ml-sklearn-model-tips/xgb…
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Ready to master the algorithms that truly matter? Let's build your expertise 1-on-1.
www.datasimple.education/one-on-one-data-classes
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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #machinelearning #ml #ai #xgboost #gradientboosting #kaggletips #careeradvice #techtips

2 months ago | [YT] | 1

Data Science Teacher Brandyn

Stop saying AI thinks like a human—the truth is far more interesting.
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They told you AI works just like the brain.
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It's a simple, elegant analogy that helps explain a complex topic.
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But it's a massive oversimplification that masks the fascinating reality.
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Your brain is a chaotic, adaptive marvel—dynamic, self-rewiring, and optimized over millions of years for one core purpose: survival.
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Artificial Neural Networks? They are structured, predictable, and rigid mathematical models.
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We have to force them to "forget" using techniques like dropout to keep them from overfitting. Your brain just... forgets. Naturally and sometimes instantly.
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An ANN is optimized to do one thing perfectly, like minimizing loss. The brain is optimized to do everything at once, like not dying while also appreciating a good meme.
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Knowing this distinction isn't just trivia. It’s crucial to understanding the true power, and the real limitations, of the data tools we use every day.
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What's the most surprising difference to you?
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Go from analogy to application with personalized data coaching.
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www.datasimple.education/one-on-one-data-classes

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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #AI #machinelearning #deeplearning #neuralnetworks #tech #career #dataskills #neuroscience

2 months ago | [YT] | 2

Data Science Teacher Brandyn

The difference between "Applicant" and "Hired" can be one page. I've added 3 of them to my Udemy course.
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100 Seats Free to Celebrate!!
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Your resume has one job: Don't get deleted in 6 seconds.
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Most people think recruiters read every word on their resume.
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The hard truth? They don't read; they pattern-match.
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In those first few seconds, they are scanning for keywords, quantifiable achievements, and a format that proves you understand professional communication.
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The problem is most resume templates are made by graphic designers, not hiring managers. They look pretty, but they fail the 6-second test and get buried by Applicant Tracking Systems (ATS).
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A great resume doesn't just list skills; it tells a story of the value you deliver. It turns your Python knowledge into a compelling case for why a company should hire you.
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That's why I've created and added 3 brand new, data analyst-specific resume templates directly into my Python Data Analysis Bootcamp on Udemy.
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There's one for career-changers, one for recent grads, and one for experienced pros.
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To celebrate, I'm giving away 100 free seats to the entire course.
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Get the skills, the projects, and now the exact resume templates to help you land the job.
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The coupon is first come, first served. When 100 are gone, they're gone.
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What's been your biggest frustration with writing your resume?

Get 1 of 100 free spots now:
www.udemy.com/course/python-data-analysis-bootcamp…



#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #resumetips #jobsearch #careeradvice #udemy #freecourse #techjobs #hiring

2 months ago | [YT] | 1

Data Science Teacher Brandyn

I found the survivors of a spaceship crash using Python. You can too.
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FREE Level 8 Python Guided Project at DataSimple
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Most data analysts make one critical mistake.
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They create beautiful charts and insightful reports for business stakeholders.
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But when they hand their work to a Data Scientist, it’s often unusable for building a model.
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The reason is simple: analyzing for business insights isn't the same as prepping data for machine learning.
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Business insights care about the "what". ML prep cares about the "what" and its statistical power to predict an outcome.
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I saw this firsthand analyzing the "Spaceship Titanic" dataset from Kaggle.
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A business insight would be: "Passengers in cryo-sleep had a higher survival rate."
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An ML insight is: "The 'CryoSleep' boolean feature has a strong positive correlation with the 'Transported' target variable, making it a key predictor for our classification model."
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See the difference? One is an interesting fact. The other is a direct, actionable step for building the algorithm.
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Learning to think this way is what separates a good analyst from a great one—and gets you hired for more advanced roles. It's the bridge between data analysis and data science.
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I created a guided project where you can analyze this exact dataset, learning to extract the insights a data scientist actually needs.
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FREE SpaceShip Titanic Guided Project
www.datasimple.education/post/python-data-analysis…
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Ready to level up your analysis? Master ML-ready data analysis with one-on-one guided classes.
www.datasimple.education/one-on-one-data-classes
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#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #machinelearning #careerdevelopment #techskills #kaggle #datavisualization #pythonprogramming

2 months ago | [YT] | 1