Data Science & Machine Learning

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 I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍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 Join our WhatsApp channel for more Statistics Resources 👇👇 https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O Like if you need similar content 😄👍

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✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4k5x6vx No more excuses—just pure learning!✅️

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.

Relatable? 😂

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 Like if you need similar content 😄👍

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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. Like if it helps 😄

𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗮𝗺😍 Start Learning Coding From Scratch  Curriculum designed and taught by Alumni from IITs & Leading Tech Companies. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:-  🌟 Trusted by 7000+ Students 🤝 500+ Hiring Partners 💼 Avg. Rs. 7.2 LPA 🚀 41 LPA Highest Package Eligibility: BTech / BCA / BSc / MCA / MSc  𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-  https://pdlink.in/4hO7rWY Hurry, limited seats available!🏃‍♂️

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

𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗳𝗿𝗼𝗺 𝗚𝗹𝗼𝗯𝗮𝗹 𝗚𝗶𝗮𝗻𝘁𝘀!😍 Want real-world experience in 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4hZlkAW 🔗 Save & share this post with someone who needs it!

🚀 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. Like if you need similar content 😄👍

𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗖𝗮𝗻 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗚𝗲𝘁 𝗬𝗼𝘂 𝗛𝗶𝗿𝗲𝗱!😍 Want to land a Data Analyst or SQL-based job? 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4hCYob9 🚀 Start working on these projects today & boost your SQL skills! 💻

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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. Like if you need similar content 😄👍

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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.

𝗙𝗥𝗘𝗘 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Business Analysis – Foundation 2)Business Analysis Fundamentals 3)The Essentials of Business & Risk Analysis  4)Master Microsoft Power BI  𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/4hHxBdW Enroll For FREE & Get Certified🎓