Python Roadmap 👆

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 – 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲!😍

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Data Science Interview Questions

Question 1 : How would you approach building a recommendation system for personalized content on Facebook? Consider factors like scalability and user privacy.

   - Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.


Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?

   - Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.


Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?

   - Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.

Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.

   - Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.

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10 great Python packages for Data Science not known to many:

1️⃣ CleanLab

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

2️⃣ LazyPredict

A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.

3️⃣ Lux

A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

4️⃣ PyForest

A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

5️⃣ PivotTableJS

PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

6️⃣ Drawdata

Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.

7️⃣ black

The Uncompromising Code Formatter

8️⃣ PyCaret

An open-source, low-code machine learning library in Python that automates the machine learning workflow.

9️⃣ PyTorch-Lightning by LightningAI

Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

🔟 Streamlit

A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗙𝗥𝗘𝗘 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼𝘀!😍

Want to become a Data Analytics pro?🔥

These tutorials simplify complex topics into easy-to-follow lessons✨️

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What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀?

These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 & 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗰𝗵.

𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
- Supervised vs. Unsupervised Learning
- Overfitting and Underfitting
- Cross-validation
- Bias-Variance Tradeoff
- Accuracy vs Interpretability
- Accuracy vs Latency

𝗠𝗟 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
- K-Nearest Neighbors
- Naive Bayes
- Linear Regression
- Ridge and Lasso Regression
- K-Means Clustering
- Hierarchical Clustering
- PCA

𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗦𝘁𝗲𝗽𝘀
- EDA
- Data Cleaning (e.g. missing value imputation)
- Data Preprocessing (e.g. scaling)
- Feature Engineering (e.g. aggregation)
- Feature Selection (e.g. variable importance)
- Model Training (e.g. gradient descent)
- Model Evaluation (e.g. AUC vs Accuracy)
- Model Productionization

𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴
- Grid Search
- Random Search
- Bayesian Optimization

𝗠𝗟 𝗖𝗮𝘀𝗲𝘀
- [Capital One] Detect credit card fraudsters
- [Amazon] Forecast monthly sales
- [Airbnb] Estimate lifetime value of a guest

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Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview

1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.

2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.

3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.

4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.

5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.

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Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

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🚀 Top 10 Tools Data Scientists Love! 🧠

In the ever-evolving world of data science, staying updated with the right tools is crucial to solving complex problems and deriving meaningful insights.

🔍 Here’s a quick breakdown of the most popular tools:

1. Python 🐍: The go-to language for data science, favored for its versatility and powerful libraries.
2. SQL 🛠️: Essential for querying databases and manipulating data.
3. Jupyter Notebooks 📓: An interactive environment that makes data analysis and visualization a breeze.
4. TensorFlow/PyTorch 🤖: Leading frameworks for deep learning and neural networks.
5. Tableau 📊: A user-friendly tool for creating stunning visualizations and dashboards.
6. Git & GitHub 💻: Version control systems that every data scientist should master.
7. Hadoop & Spark 🔥: Big data frameworks that help process massive datasets efficiently.
8. Scikit-learn 🧬: A powerful library for machine learning in Python.
9. R 📈: A statistical programming language that is still a favorite among many analysts.
10. Docker 🐋: A must-have for containerization and deploying applications.

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Seaborn Cheatsheet ✅

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Key Concepts for Machine Learning Interviews

1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.

2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.

3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.

4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.

5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).

6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.

7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.

8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.

10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.

11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.

12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.

13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.

14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.

15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.

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Accenture Data Scientist Interview Questions!

1st round-

Technical Round

- 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions.

- 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge.

- 3-4 Machine Learning questions completely based on my Projects, starting from
Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions.

2nd round-

- Couple of python questions agains on pandas and numpy and some hypothetical data.

- Machine Learning projects explanations and cross questions.

- Case Study and a quiz question.

3rd and Final round.

HR interview

Simple Scenerio Based Questions.

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Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

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Top 10 machine Learning algorithms for beginners 👇👇

1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.

2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).

3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.

4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.

5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.

6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.

7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.

8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.

9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.

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Key Concepts for Data Science Interviews

1. Data Cleaning and Preprocessing: Master techniques for cleaning, transforming, and preparing data for analysis, including handling missing data, outlier detection, data normalization, and feature engineering.

2. Statistics and Probability: Have a solid understanding of descriptive and inferential statistics, including distributions, hypothesis testing, p-values, confidence intervals, and Bayesian probability.

3. Linear Algebra and Calculus: Understand the mathematical foundations of data science, including matrix operations, eigenvalues, derivatives, and gradients, which are essential for algorithms like PCA and gradient descent.

4. Machine Learning Algorithms: Know the fundamentals of machine learning, including supervised and unsupervised learning. Be familiar with key algorithms like linear regression, logistic regression, decision trees, random forests, SVMs, and k-means clustering.

5. Model Evaluation and Validation: Learn how to evaluate model performance using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrices. Understand techniques like cross-validation and overfitting prevention.

6. Feature Engineering: Develop the ability to create meaningful features from raw data that improve model performance. This includes encoding categorical variables, scaling features, and creating interaction terms.

7. Deep Learning: Understand the basics of neural networks and deep learning. Familiarize yourself with architectures like CNNs, RNNs, and frameworks like TensorFlow and PyTorch.

8. Natural Language Processing (NLP): Learn key NLP techniques such as tokenization, stemming, lemmatization, and sentiment analysis. Understand the use of models like BERT, Word2Vec, and LSTM for text data.

9. Big Data Technologies: Gain knowledge of big data frameworks and tools like Hadoop, Spark, and NoSQL databases that are used to process large datasets efficiently.

10. Data Visualization and Storytelling: Develop the ability to create compelling visualizations using tools like Matplotlib, Seaborn, or Tableau. Practice conveying your data findings clearly to both technical and non-technical audiences through visual storytelling.

11. Python and R: Be proficient in Python and R for data manipulation, analysis, and model building. Familiarity with libraries like Pandas, NumPy, Scikit-learn, and tidyverse is essential.

12. Domain Knowledge: Develop a deep understanding of the specific industry or domain you're working in, as this context helps you make more informed decisions during the data analysis and modeling process.

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Data Science Interview Questions

1: How would you preprocess and tokenize text data from tweets for sentiment analysis? Discuss potential challenges and solutions.

- Answer: Preprocessing and tokenizing text data for sentiment analysis involves tasks like lowercasing, removing stop words, and stemming or lemmatization. Handling challenges like handling emojis, slang, and noisy text is crucial. Tools like NLTK or spaCy can assist in these tasks.


2: Explain the collaborative filtering approach in building recommendation systems. How might Twitter use this to enhance user experience?

- Answer: Collaborative filtering recommends items based on user preferences and similarities. Techniques include user-based or item-based collaborative filtering and matrix factorization. Twitter could leverage user interactions to recommend tweets, users, or topics.


3: Write a Python or Scala function to count the frequency of hashtags in a given collection of tweets.

- Answer (Python):
   

     def count_hashtags(tweet_collection):
         hashtags_count = {}
         for tweet in tweet_collection:
             hashtags = [word for word in tweet.split() if word.startswith('#')]
             for hashtag in hashtags:
                 hashtags_count[hashtag] = hashtags_count.get(hashtag, 0) + 1
         return hashtags_count
     

4: How does graph analysis contribute to understanding user interactions and content propagation on Twitter? Provide a specific use case.

- Answer: Graph analysis on Twitter involves examining user interactions. For instance, identifying influential users or detecting communities based on retweet or mention networks. Algorithms like PageRank or Louvain Modularity can aid in these analyses.

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Data Analyst vs Data Scientist 👆

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𝐔𝐈/𝐔𝐗 𝐃𝐞𝐬𝐢𝐠𝐧 𝐅𝐑𝐄𝐄 𝐎𝐧𝐥𝐢𝐧𝐞 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬😍

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How much Statistics must I know to become a Data Scientist?

This is one of the most common questions

Here are the must-know Statistics concepts every Data Scientist should know:

𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆

↗ Bayes' Theorem & conditional probability
↗ Permutations & combinations
↗ Card & die roll problem-solving

𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀

↗ Mean, median, mode
↗ Standard deviation and variance
↗  Bernoulli's, Binomial, Normal, Uniform, Exponential distributions

𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀

↗ A/B experimentation
↗ T-test, Z-test, Chi-squared tests
↗ Type 1 & 2 errors
↗ Sampling techniques & biases
↗ Confidence intervals & p-values
↗ Central Limit Theorem
↗ Causal inference techniques

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴

↗ Logistic & Linear regression
↗ Decision trees & random forests
↗ Clustering models
↗ Feature engineering
↗ Feature selection methods
↗ Model testing & validation
↗ Time series analysis

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When you start making good money, do this:

1. Buy fewer clothes, but wear the highest quality.
2. Eat premium food, not junk.
3. Hire a helper for household chores. Buy back your time.
4. Upgrade your mattress. Sleep changes everything.
5. Invest in experiences, not just stuff.
6. Upgrade your financial adviser. The one who got you here won’t get you to the next level.
7. Surround yourself with high-value people.

Small shifts. Big impact.

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Trump Takes Action: Tariffs on China, Energy Dominance, Vaccine Ban & IRS Shakeup 🇺🇸🔥

🚨 Major moves from President Trump:

💰Tariffs on China: Trump announced that he has imposed import duties totaling 600 billion rubles—more than any other U.S. president before him.

⚡️Energy Dominance: Trump signed an executive order creating the National Council for Energy Dominance, chaired by Secretary of State Bergum, aiming to unleash America’s full energy potential.

🚫COVID-19 Vaccine Ban in Schools: Schools receiving federal funding can no longer require the COVID-1COVID-19 vaccine—a decisive move that shuts down speculation about Trump's stance on vaccines.

📉Reports suggest the IRS is prepaIRS is preparing mass layoffs next week followingmajor audit of the agency.

🔥Bold moves, big changes—what’s next?

#Trump #Tariffs #EnergyDominance #COVID19 #VaccineBan #IRS #China #AmericaFirst #BreakingNews

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George Soros said at the WEF that President Trump is a fraud and a complete narcissist who wants the world to revolve around him.

#Soros #WEF #Trump

👂 More on Trump's Ear ⚠️

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