NulledCodeSolutions.online

Data Science: Supervised Machine Learning in Python
Last updated 11/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.05 GB | Duration: 6h 24m​

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

What you’ll learn
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
Understand and implement a Decision Tree in Python
Understand and implement the Perceptron in Python
Understand the limitations of the Perceptron
Understand hyperparameters and how to apply cross-validation
Understand the concepts of feature extraction and feature selection
Understand the pros and cons between classic machine learning methods and deep learning
Use Sci-Kit Learn
Implement a machine learning web service

Requirements
Python, Numpy, and Pandas experience
Probability and statistics (Gaussian distribution)
Strong ability to write algorithms

Description
In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what’s going to drive innovation in the coming years. It’s embedded into all sorts of different products.Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.It’s important to know both the advantages and disadvantages of each algorithm we look at.Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.We’ll do a comparison with deep learning so you understand the pros and cons of each approach.We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about "remembering facts", it’s about "seeing for yourself" via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can’t implement it, you don’t understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times.Suggested Prerequisites:calculus (for some parts)probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail – email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math – get important details about algorithms that other courses leave out

Data Science: Deep Learning and Neural Networks in Python
Duration: 12h 8m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 3.83 GB
Genre: eLearning | Language: English​

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code
What you’ll learn

• Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
• Learn how a neural network is built from basic building blocks (the neuron)
• Code a neural network from scratch in Python and numpy
• Code a neural network using Google’s TensorFlow
• Describe different types of neural networks and the different types of problems they are used for
• Derive the backpropagation rule from first principles
• Create a neural network with an output that has K > 2 classes using softmax
• Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
• Install TensorFlow
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Requirements

• Basic math (calculus derivatives, matrix arithmetic, probability)
• Install Numpy and Python
• Don’t worry about installing TensorFlow, we will do that in the lectures.
• Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description
Ever wondered how AI technologies likeOpenAIChatGPT,GPT-4,DALL-E,Midjourney, andStable Diffusionreally work? In this course, you will learn the foundations of these groundbreaking applications.

This course will get you started in building your FIRSTartificial neural networkusingdeep learningtechniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google’s new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested inmachine learninganddata sciencein general. We go beyond basic models like logistic regression and linear regression and I show you something thatautomatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we’ll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone’s emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks – slightly modified architectures and what they are used for.

NOTE:

If youalreadyknow about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic,Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such asConvolutional Neural Networks,Restricted Boltzmann Machines,Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about "remembering facts", it’s about"seeing for yourself" via experimentation. It will teach you how to visualize what’s happening in the model internally. If you wantmorethan just a superficial look at machine learning models, this course is for you.

"If you can’t implement it, you don’t understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times.

Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Be familiar with basic linear models such as linear regression and logistic regression

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

• Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course
• Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

More Info

Data Science Methods and Techniques [2026]
2026-01-02
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 11.18 GB | Duration: 21h 45m​

Learn Data Science Methods and Techniques for Data Analysis and Machine Learning [2026]

What you’ll learn
Knowledge about Data Science methods, techniques, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Requirements
Basic knowledge of the Python programming language and preferably the Pandas library
The four ways of counting (+-/)
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection

Description
Welcome to the course Data Science Methods and Techniques for Data Analysis and Machine Learning!Data Science is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Data Science Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.This course will teach you a large selection of Data Science methods and techniques, which will give you an excellent foundation for Data Science jobs and studies. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer.This is a three-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and Unsupervised Learning.You will learn to master Regression, Regression analysis, Prediction and supervised learning. This course has the most complete and fundamental master-level regression content packages on , with hands-on, useful practical theory, and also automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.You will learn to master Classification and supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifiers Ensembles and Voting Classifier Ensembles.You will learn to master Cluster Analysis and unsupervised learning. This part of the course is about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and some useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.You will learnKnowledge about Data Science methods, techniques, theory, best practices, and tasksDeep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidenceDetailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised LearningHands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python librariesAdvanced knowledge of A.I. prediction models and automatic model creationCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resourcesOption: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work lifeAnd much more.This course includesan easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this coursean easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Data Science or coding taskcontent that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineera large collection of unique content, and this course will teach you many new things that only can be learned from this course on a course structure built on a proven and professional framework for learning.a compact course structure and no killing timeThis course is an excellent way to learn to master Regression, Prediction, Classification, and Cluster analysis!These are the most important and useful tools for modeling, AI, and forecasting.Is this course for you?This course is for you, regardless if you are a beginner or an experienced Data ScientistThis course is for you, regardless if you have a Ph.D. or no education or experience at allThis course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and unsupervised learning.Course requirementsBasic knowledge of the Python programming language and preferably the Pandas libraryThe four ways of counting (+-/)Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedEnroll now to receive 15+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!

Who this course is for:
This course is for you, regardless if you are a beginner or an experienced Data Scientist, This course is for you, regardless if you have a Ph.D. or no education or experience at all

For More Courses Visit & Bookmark Your Preferred Language Blog
From Here: – – – – – – – –

Data Quality and Ground Truth for Generative AI
.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 34m | 94.2 MB
Instructor: Harsh Karna​

Generative AI systems fail when data quality and validation are weak. This course teaches you how to build reliable ground truth datasets and design structured evaluation workflows that reduce risk before deployment.

What you’ll learn

Generative AI projects often fail not because the model is advanced, but because the underlying data is flawed or the validation process is incomplete. In this course, Data Quality and Ground Truth for Generative AI, you’ll gain the ability to build strong data foundations and design validation workflows that expose weaknesses before AI systems reach production.

First, you’ll explore how to assess data sources for quality, representativeness, and bias, and understand why consistent labeling practices are critical for building reliable ground truth datasets. Next, you’ll discover how to document dataset lineage, assumptions, and constraints to ensure reproducibility and auditability across teams and model versions. Finally, you’ll learn how to validate AI systems using appropriate evaluation methods, structured error analysis, and scalable workflows that reduce risk across iterative development cycles.

When you’re finished with this course, you’ll have the skills and knowledge needed to reduce generative AI failures by strengthening data quality and validation practices from the ground up.

Homepage

Data Entry Skills via Google Sheets : A Practical Course
Published 2/2024
Duration: 5h37m | .MP4 1280×720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.67 GB
Genre: eLearning | Language: English​

Complete Guide For Data Entry Skills to become a Successful Data Entry Worker by using Google sheets, including chat GPT

What you’ll learn
Introduction to data entry skills
Introduction and advantage of google sheets
Use the Google sheets in offline mode
Different methods of access google sheets
Basic tools of google sheets for data entry works
Freezing rows & columns
Selecting and Adding Rows & Columns
Manage and arrange the data in google sheets for data entry works
Export into google sheet and download in different formats
Auto Sync from google sheets to MS Excel
Arithmetic related functions in google sheets for data entry works
Mathematic related functions in google sheets for data entry works
Text related functions in google sheets for data entry works
Date and time related functions in google sheets for data entry works
Usage of conditional formatting in google sheets
Usage of sort and filter options in google sheets for data entry works
Data Validation and hyperlink options in google sheets
Data scraping from website to google sheets
Translate any language to your preference language in google sheets
Convert the data from PDF to google sheets
Convert the data from picture to google sheets
Protect the google sheets and ranges
Visualize your data by using charts and graphs
Sharing the file with restrictions
Published to web option in google sheets to share the file for large people
Usage of IF and VLOOKUP functions in google sheets for data entry works
Common shortcut keys in google sheets
Tips and trick in google sheets related with data entry works
Usage of chat GPT in google sheets
Manage the add-on in google sheets
Personalized document creation by using document studio in google sheets

Requirements
No experience needed, You just need a desire to learn
You need a computer with internet connection

Description
Eight Reasons why you should choose this Data Entry Skills via Google Sheets : A Practical Course
You will learn various data entry techniques using Google sheets
Carefully designed curriculum By Proficient In Google sheets for data entry works
you can complete this course In Short time
Data Entry related examples and case studies Provided
Practical Examples And practice exercises Are Able to Download in The Recourse Section
A Verifiable Certificate will Be Provided On the Completion
24 Downloadable resources attached in this course and You are able to practice it.
Data Entry related doubt resolved by Chat GPT
I will lead you through this course on essential data entry skills aimed at transforming you into a proficient data entry professional in data entry position. The course offers comprehensive insights into data entry tasks within Google Sheets. I will systematically instruct you, starting from the fundamentals and progressing to advanced techniques for data entry work in Google Sheets.
This course is designed for beginners and intermediate users who want to enhance their data entry skills by using Google Sheets, the google sheets is a globally recognized and powerful spreadsheet application in the world. Throughout the program, participants will acquire the necessary knowledge, techniques and strategies to become a successful Data entry worker in google sheets.
The beauty of this course is everything demonstrate with Practical file, it helps to minimize your data entry errors, and maximize your efficiency.
This course will cover data entry related tools, websites, techniques , strategies and Chat GPT to become a master in data entry work in google sheets, after finishing this course you are able do your data entry related work independently and efficiently.
Go ahead and click the enroll button, and I’ll see you in Chapter 1!
Cheers
AMAMA Mubeen
Who this course is for:
People who are interest to become a Data entry professional
Peoples who are interested to learn about Data entry
All individual, who work at office
All students, who are engaging with computer and data entry work
Anyone who wants to learn Google sheets for data entry work
Students who want to Engage with Computer and Data Entry Work with Google Sheets

More Info

Data Blending and Joins in Alteryx
Duration: 1h 30m | .MP4 1280×720 30 fps(r) | AAC, 48000 Hz, 2ch | 283.21 MB
Genre: eLearning | Language: English​

Alteryx is a powerful tool to manipulate and analyze data. This course will introduce you to common functions in Alteryx that will help you combine data from multiple tables or sources for further summarization or aggregation.

More Info:
Data Blending and Joins in Alteryx

Free Download Data Architecture Mastery Think Like Senior Data Architect
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.36 GB | Duration: 19h 0m
Master data architecture decisions, patterns, and trade-offs – from lakehouse to AI-ready systems
What you’ll learn

Master core data modeling paradigms using first-principles frameworks that turn business Requirements into defensible architectural decisions.
Build lakehouses, data mesh platforms, streaming pipelines, and serving layers with clear tradeoff reasoning across cloud architectures and open table formats.
Design AI-ready architectures for RAG, vector databases, and agentic AI, with data quality, FinOps cost models, and operational SLOs built in from day one.
Apply governance, security, and compliance by design; prevent costly anti-patterns; and produce a portfolio ADR demonstrating senior architect-level thinking.
Understand how team topology and Conway’s Law shape data platforms, and make organizational design decisions that prevent architecture from drifting into chaos.
Evaluate emerging paradigms and build migration-ready architectures that adapt to change without full rewrites – a critical skill for long-lived platforms.
Requirements
Some experience in data engineering, analytics engineering, or a related data platform role.
Comfortable reading and understanding technical concepts – you work with data systems regularly, even if not as a hands-on coder.
Basic familiarity with data pipeline and storage concepts – terms like ETL, data warehouse, or data lake should not be new to you.
No prior architecture experience needed – this course builds structured architectural thinking from the ground up.
Description
Most data engineers can build pipelines. Few can architect systems that survive scale, regulation, cost pressure, and the next paradigm shift. This course closes that gap.Data Architecture Mastery teaches you to think like a senior data architect – not by memorizing tools, but by building a repeatable decision framework you can apply to any stack, any workload, and any constraint.What you’ll learnecompose any business requirement into six architectural constraint dimensions and use a weighted scoring framework to make defensible technology decisionsEvaluate storage paradigms – data warehouse, data lake, lakehouse, data mesh, and data fabric – and choose the right one for your workloadNavigate modern data paradigms: evaluate lakehouse architecture, open table formats, real-time OLAP engines, columnar storage, and medallion architecture – and know when each is the right call versus a legacy warehouseArchitect pipeline patterns across batch ETL/ELT, streaming, CDC, Lambda, Kappa, and Zero ETLApply data modeling paradigms – Kimball star schema, Inmon 3NF, Data Vault 2.0, MDM, SCD and One Big Table – to the right use caseBuild AI-ready platforms with RAG pipelines, embedding architectures, feature stores, and agentic AI systemsDesign the serving layer: semantic layers, data contracts, materialized views, and reverse ETLModel total cost of ownership, apply hot/warm/cold storage tiering, and use FinOps practices to prevent runaway cloud spendDefine SLOs with error budgets, design for failure with chaos engineering, and build production-grade observability into your pipelinesImplement privacy by design, data classification, active metadata, policy-as-code, access control architectures, and GDPR/CCPA compliance patternsApply Strangler Fig, Blue-Green deployment, and Parallel Run patterns for zero-risk platform migrationsAlign architecture with team structure using Conway’s Law, and avoid the most costly anti-patterns seen in production systemsDocument every decision using the Architecture Decision Canvas – a portfolio artifact you keep and build on after the courseEvery module builds on a single framework. By the end you’ll have a completed Architecture Decision Canvas for a real workload from your own platform – something you can present to leadership, defend in review, and update as Requirements change.This course is for senior data engineers, data architects, technical leads and even data engineering managers who want to move from building to designing, from tool familiarity to architectural reasoning, and from opinions to decisions they can defend in any room.
Data engineers who know the tools but want to level up into architectural thinking and design decisions.,Analytics engineers and data leads looking to expand their scope from pipeline-level to platform-level responsibility.,Engineers transitioning into data architect or staff engineer roles who need a structured, repeatable decision framework.,Teams preparing their data platforms for AI and GenAI workloads who need a principled, future-ready architecture approach.,Senior Engineering Managers, Data Directors & Technical Leads responsible for platform strategy and investment decisions who wants to gain deep data architectural knowledge and evaluate proposals critically, not just contextually.
Homepage
Code:Copy to clipboard

https://www.udemy.com/course/data-architecture-mastery-think-like-senior-data-architect/

Code:Copy to clipboard

DDownload
https://ddownload.com/rby85y6b1b95/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part1.rar
https://ddownload.com/cytwb7m9bjw4/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part2.rar
https://ddownload.com/sfbixvs2u13d/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part3.rar
https://ddownload.com/r6g6wo4hmt4i/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part4.rar
https://ddownload.com/p0rv481ehasv/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part5.rar
https://ddownload.com/nj66v9gwhwwh/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part6.rar
Rapidgator
https://rg.to/file/7b0225ee9abfa0ec757aa3dd021ae093/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part1.rar.html
https://rg.to/file/3e6b1ec96b5d9dc5dc755b5942f692e5/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part2.rar.html
https://rg.to/file/91c1366aa8488ee789153e4ddf8ef8c5/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part3.rar.html
https://rg.to/file/818c84ac5352d3edf66339b4395cf139/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part4.rar.html
https://rg.to/file/411d74332d8b071860053bc3c171e4f7/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part5.rar.html
https://rg.to/file/3d43b1ddb7f9e9b2488b300afa53784a/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part6.rar.html
AlfaFile Free Download
https://alfafile.net/file/A4dvn/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part1.rar
https://alfafile.net/file/A4dvp/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part2.rar
https://alfafile.net/file/A4dvJ/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part3.rar
https://alfafile.net/file/A4dvP/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part4.rar
https://alfafile.net/file/A4dvq/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part5.rar
https://alfafile.net/file/A4dv6/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part6.rar
FreeDL (Friendly Host)
https://frdl.io/outz3ckc7qva/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part1.rar.html
https://frdl.io/x6kvwwaxgh4k/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part2.rar.html
https://frdl.io/p4w3vpils6dz/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part3.rar.html
https://frdl.io/j5dy88dppz6k/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part4.rar.html
https://frdl.io/f1u9ipgk1lkx/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part5.rar.html
https://frdl.io/gag6uhvyvt6p/kxlvn.Data.Architecture.Mastery.Think.Like.Senior.Data.Architect.part6.rar.html

No Password – Links are Interchangeable

MP4 | Free Download Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 16.44 GB | Duration: 30h 44m
Learn data analysis from scratch using Excel dashboards, SQL queries, Python automation & real-world projects (2026)
What you’ll learn
Analyze data using Microsoft Excel with formulas, functions, charts, pivot tables, dashboards, and projects

Clean, organize, and validate real-world data using Excel best practices
Create Excel dashboards and reports used in business and analyst roles
Write SQL queries to retrieve, filter, group, and analyze data from databases
Use SQL functions and JOINs to work with multiple tables
Learn Python fundamentals required for data analysis
Work with CSV and JSON files using Python
Perform data analysis using Python Pandas
Visualize data using Python charts and plots
Understand when to use Excel, SQL, or Python in real data analyst workflows
Apply skills through practical projects and real datasets
Build a strong foundation for entry-level data analyst roles
Requirements
No prior programming or data analysis experience required
Basic computer usage knowledge is enough
Willingness to learn and practice step by step
Description
Become a Data Analyst with Practical Skills – Not Just TheoryThis Data Analyst Bootcamp is designed for beginners who want a clear, structured, and practical path to learning data analysis using Excel, SQL, and Python.Instead of learning tools separately, this course shows you how data analysts actually work in real companies – starting with Excel, moving to SQL, and then using Python for automation and advanced analysis.What You Will Learn:• Use Excel for data cleaning, analysis, dashboards, and reporting• Write SQL queries to retrieve and analyze data from databases• Use Python for data handling, automation, and analysis with Pandas• Work on practical projects and real datasets• Understand how Excel, SQL, and Python work together in real analyst roles• Build a strong foundation for Data Analyst jobs or internshipsHow This Course Is Structured:This course follows a beginner-friendly, job-oriented sequence:Excel for Data Analysis – basics to advanced dashboards, projects, and AI-assisted ExcelSQL for Data Analysis – querying, filtering, functions, and joinsPython Foundations & Projects – complete Python basics with projectsPython for Data Analysis – Pandas, data visualization, and analysis workflowsAdditional Skills – automation, extra Excel topics, and practice datasetsTo give you the best learning experience, technical lessons are created with the help of experienced professionals, while the overall learning path, guidance, and support are provided directly by the instructor.Who this course is for:• Beginners who want to start a career as a Data Analyst• Students and fresh graduates from any background• Working professionals looking to switch to data roles• Excel users who want to upgrade to SQL and Python• Anyone who prefers clear explanations and practical learningNo prior programming or data experience is required.Who This Course Is NOT For:• Advanced data scientists• People looking only for deep machine learning or AI theory• Database administrators or backend developersThis course focuses on practical data analysis foundations, not advanced specialization.Why This Course Is Different:• Clear learning path instead of scattered topics• Strong focus on Excel dashboards and projects• Practical SQL and Python usage (not certification theory)• Beginner-friendly explanations• Designed for non-native English speakersStart Learning with Confidence:If you want a structured, practical, and beginner-friendly Data Analyst course that focuses on real skills, this bootcamp is for you.Enroll now and start your Data Analyst journey step by step.Let’s get started and build real-world data skills step by step.Post your questions in the Q&A section and I’ll support you throughout the journey.
Beginners who want to start a career in Data Analysis,Students and fresh graduates from any background,Working professionals planning to switch to Data Analyst roles,Excel users who want to upgrade their skills with SQL and Python,Non-technical learners who prefer clear and step-by-step explanations,Anyone preparing for entry-level data analyst jobs or internships
Homepage
Code:Copy to clipboard

https://www.udemy.com/course/data-analyst-complete-course-excel-sql-python-projects/

Code:Copy to clipboard

DDownload
https://ddownload.com/2d34lagyoofo/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part16.rar
https://ddownload.com/7lv9vf6y2fek/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part13.rar
https://ddownload.com/90j356bkwpv8/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part08.rar
https://ddownload.com/dm7owqarebws/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part15.rar
https://ddownload.com/e8oo18zz3bh8/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part12.rar
https://ddownload.com/kkrimwgsb35j/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part07.rar
https://ddownload.com/orz7u3xxt7yq/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part09.rar
https://ddownload.com/qey99jgum6jt/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part17.rar
https://ddownload.com/quaoq89ui0li/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part11.rar
https://ddownload.com/qxcblju1u64v/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part03.rar
https://ddownload.com/r2cc87mn5hiq/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part06.rar
https://ddownload.com/r30jj2awqmw5/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part04.rar
https://ddownload.com/rztyu1jf47ak/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part01.rar
https://ddownload.com/tixk9ar1dabf/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part02.rar
https://ddownload.com/tzsxr8hp8e5m/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part14.rar
https://ddownload.com/v589oiov2fr2/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part10.rar
https://ddownload.com/zoygp41b8nix/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part05.rar
Rapidgator
https://rg.to/file/0750caeee544d3b257d9211ff2e41ec6/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part17.rar.html
https://rg.to/file/0dd69f67affd82eec50ae7973e132efc/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part05.rar.html
https://rg.to/file/0e76eef18d3e4709e4fdc3a2a5b99da9/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part16.rar.html
https://rg.to/file/0f856a4a5e4b4019f5e529fa047e8f2e/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part04.rar.html
https://rg.to/file/6213ddbf2f391f93393121e8e8c3a69f/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part08.rar.html
https://rg.to/file/8a6305ddb999f35d4499a75a68157c5b/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part13.rar.html
https://rg.to/file/8ba52d39281c1313b6e4041b20147fb1/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part01.rar.html
https://rg.to/file/a294845019ee0a447ebde5d8108469b8/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part03.rar.html
https://rg.to/file/ae92c3d7fcdfedd871cc7803db9b6181/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part07.rar.html
https://rg.to/file/b61845079c9632981f1368e26cb0fed3/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part14.rar.html
https://rg.to/file/bade28d30cdcc8e151fc718c54ec9f88/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part12.rar.html
https://rg.to/file/c69d09bcf5adf2b9b4b298d21ec7c0f2/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part06.rar.html
https://rg.to/file/ca463ab803f08451a481f733d27b1c52/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part11.rar.html
https://rg.to/file/dcf3c817d90f46035219cfcdb677decb/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part02.rar.html
https://rg.to/file/e75071c70828b0ff5bef6ede6b16a246/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part09.rar.html
https://rg.to/file/f93762e9b36f289eb7144845476a52e0/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part15.rar.html
https://rg.to/file/fb6d47d9f161b8e4c5644f4b5332343c/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part10.rar.html
AlfaFile Free Download
https://alfafile.net/file/A4Un2/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part15.rar
https://alfafile.net/file/A4Un5/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part17.rar
https://alfafile.net/file/A4Un6/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part13.rar
https://alfafile.net/file/A4Un7/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part04.rar
https://alfafile.net/file/A4UnE/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part05.rar
https://alfafile.net/file/A4UnJ/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part09.rar
https://alfafile.net/file/A4UnP/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part10.rar
https://alfafile.net/file/A4UnW/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part14.rar
https://alfafile.net/file/A4Una/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part16.rar
https://alfafile.net/file/A4Unb/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part02.rar
https://alfafile.net/file/A4Unj/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part01.rar
https://alfafile.net/file/A4Unm/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part03.rar
https://alfafile.net/file/A4Unn/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part07.rar
https://alfafile.net/file/A4Unp/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part08.rar
https://alfafile.net/file/A4Unq/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part11.rar
https://alfafile.net/file/A4Uns/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part12.rar
https://alfafile.net/file/A4Uny/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part06.rar
FreeDL (Friendly Host)
https://frdl.io/08g1d4xzp2dn/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part10.rar.html
https://frdl.io/0l41kpgphosm/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part05.rar.html
https://frdl.io/3tdntc5o89uc/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part03.rar.html
https://frdl.io/6mdpabb3mh85/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part17.rar.html
https://frdl.io/aalabgvthcgc/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part15.rar.html
https://frdl.io/f4blhtncescw/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part04.rar.html
https://frdl.io/fibbyktja449/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part09.rar.html
https://frdl.io/i1ay1aqbf48l/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part08.rar.html
https://frdl.io/l6z22zb3v1ot/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part16.rar.html
https://frdl.io/lsitdtkjtdn6/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part01.rar.html
https://frdl.io/nr3g2ckhgdyb/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part12.rar.html
https://frdl.io/pyu2bqqcv8pl/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part07.rar.html
https://frdl.io/rgktilt95pbz/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part11.rar.html
https://frdl.io/scca1gj52uc9/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part02.rar.html
https://frdl.io/vbzsbh5aw090/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part14.rar.html
https://frdl.io/wo43bh68voha/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part06.rar.html
https://frdl.io/ztfevqgrxr1n/epjgc.Data.Analyst.Complete.Course.Excel.Sql..Python.Projects.part13.rar.html

No Password – Links are Interchangeable