Data Analytics & AI | SQL Interviews | Power BI Resources

7 best GitHub repositories to break into data analytics and data science: 1. 100-Days-Of-ML-Code - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcftdA57) - 𝐒𝐭𝐚𝐫𝐬: ~42k 2. awesome-datascience - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcFYYwx9) - 𝐒𝐭𝐚𝐫𝐬: ~22.7k 3. Data-Science-For-Beginners - 𝐋𝐢𝐧𝐤: (https://lnkd.in/d_zZBadF) - 𝐒𝐭𝐚𝐫𝐬: ~14.5k 4. data-science-interviews - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dkN4RZjH) - 𝐒𝐭𝐚𝐫𝐬: ~5.8k 5. Coding and ML System Design - 𝐋𝐢𝐧𝐤: (https://lnkd.in/gXFaaaQR) - 𝐒𝐭𝐚𝐫𝐬: ~3.5k 6. Machine Learning Interviews from MAANG - 𝐋𝐢𝐧𝐤: https://lnkd.in/gq_huuZD - 𝐒𝐭𝐚𝐫𝐬: 8.1k 7. data-science-ipython-notebooks - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dPmQuPB9) - 𝐒𝐭𝐚𝐫𝐬: ~27.2k These repositories are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.

The 'bias machine': How Google tells you what you want to hear "We're at the mercy of Google." Undecided voters in the US who turn to Google may see dramatically different views of the world – even when they're asking the exact same question. Type in "Is Kamala Harris a good Democratic candidate", and Google paints a rosy picture. Search results are constantly changing, but last week, the first link was a Pew Research Center poll showing that "Harris energises Democrats". Next is an Associated Press article titled "Majority of Democrats think Kamala Harris would make a good president", and the following links were similar. But if you've been hearing negative things about Harris, you might ask if she's a "bad" Democratic candidate instead. Fundamentally, that's an identical question, but Google's results are far more pessimistic. "It's been easy to forget how bad Kamala Harris is," said an article from Reason Magazine in the top spot. Source-Link: BBC

Forecasting vs. Predictive Analytics: The Obama Example Analytics can influence elections, not just predict them. This article explores how the Obama campaign used predictive analytics to outmaneuver traditional forecasting. Forecasting vs. Predictive Analytics Nate Silver’s forecasting predicted state outcomes, while Obama’s team used predictive analytics to score individual voters, targeting those most likely to be persuaded. Impact of Predictive Analytics The Obama campaign optimized interactions, avoiding “do-not-disturb” voters and improving ad spending effectiveness by 18%. Conclusion Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecasting’s broad predictions.

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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 Join @datasciencefun to learning important data science and machine learning concepts ENJOY LEARNING 👍👍

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Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go: 1. Computer vision 2. Natural language processing. I outlined a roadmap for computer vision I believe many beginners will find helpful. 👇👇 Artificial Intelligence

Day 26: Reinforcement Learning - Concept: Learning through interaction. - Implementation: Q-learning. - Evaluation: Reward function, policy. Day 27: Bayesian Networks - Concept: Probabilistic graphical models. - Implementation: Conditional dependencies. - Evaluation: Inference, learning. Day 28: Hidden Markov Models (HMM) - Concept: Time series analysis. - Implementation: Transition probabilities. - Evaluation: Viterbi algorithm. Day 29: Feature Selection Techniques - Concept: Improving model performance. - Implementation: Filter, wrapper methods. - Evaluation: Feature importance. Day 30: Hyperparameter Optimization - Concept: Model tuning. - Implementation: Grid search, random search. - Evaluation: Cross-validation. Share this channel with your real friends: https://t.me/datasciencefun Like if you want me to continue this series 😄❤️ ENJOY LEARNING 👍👍

Let's start with the topics we gonna cover in this 30 Days of Data Science Series, We will primarily focus on learning Data Science and Machine Learning Algorithms Day 1: Linear Regression - Concept: Predict continuous values. - Implementation: Ordinary Least Squares. - Evaluation: R-squared, RMSE. Day 2: Logistic Regression - Concept: Binary classification. - Implementation: Sigmoid function. - Evaluation: Confusion matrix, ROC-AUC. Day 3: Decision Trees - Concept: Tree-based model for classification/regression. - Implementation: Recursive splitting. - Evaluation: Accuracy, Gini impurity. Day 4: Random Forest - Concept: Ensemble of decision trees. - Implementation: Bagging. - Evaluation: Out-of-bag error, feature importance. Day 5: Gradient Boosting - Concept: Sequential ensemble method. - Implementation: Boosting. - Evaluation: Learning rate, number of estimators. Day 6: Support Vector Machines (SVM) - Concept: Classification using hyperplanes. - Implementation: Kernel trick. - Evaluation: Margin maximization, support vectors. Day 7: k-Nearest Neighbors (k-NN) - Concept: Instance-based learning. - Implementation: Distance metrics. - Evaluation: k-value tuning, distance functions. Day 8: Naive Bayes - Concept: Probabilistic classifier. - Implementation: Bayes' theorem. - Evaluation: Prior probabilities, likelihood. Day 9: k-Means Clustering - Concept: Partitioning data into k clusters. - Implementation: Centroid initialization. - Evaluation: Inertia, silhouette score. Day 10: Hierarchical Clustering - Concept: Nested clusters. - Implementation: Agglomerative method. - Evaluation: Dendrograms, linkage methods. Day 11: Principal Component Analysis (PCA) - Concept: Dimensionality reduction. - Implementation: Eigenvectors, eigenvalues. - Evaluation: Explained variance. Day 12: Association Rule Learning - Concept: Discover relationships between variables. - Implementation: Apriori algorithm. - Evaluation: Support, confidence, lift. Day 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - Concept: Density-based clustering. - Implementation: Epsilon, min samples. - Evaluation: Core points, noise points. Day 14: Linear Discriminant Analysis (LDA) - Concept: Linear combination for classification. - Implementation: Fisher's criterion. - Evaluation: Class separability. Day 15: XGBoost - Concept: Extreme Gradient Boosting. - Implementation: Tree boosting. - Evaluation: Regularization, parallel processing. Day 16: LightGBM - Concept: Gradient boosting framework. - Implementation: Leaf-wise growth. - Evaluation: Speed, accuracy. Day 17: CatBoost - Concept: Gradient boosting with categorical features. - Implementation: Ordered boosting. - Evaluation: Handling of categorical data. Day 18: Neural Networks - Concept: Layers of neurons for learning. - Implementation: Backpropagation. - Evaluation: Activation functions, epochs. Day 19: Convolutional Neural Networks (CNNs) - Concept: Image processing. - Implementation: Convolutions, pooling. - Evaluation: Feature maps, filters. Day 20: Recurrent Neural Networks (RNNs) - Concept: Sequential data processing. - Implementation: Hidden states. - Evaluation: Long-term dependencies. Day 21: Long Short-Term Memory (LSTM) - Concept: Improved RNN. - Implementation: Memory cells. - Evaluation: Forget gates, output gates. Day 22: Gated Recurrent Units (GRU) - Concept: Simplified LSTM. - Implementation: Update gate. - Evaluation: Performance, complexity. Day 23: Autoencoders - Concept: Data compression. - Implementation: Encoder, decoder. - Evaluation: Reconstruction error. Day 24: Generative Adversarial Networks (GANs) - Concept: Generative models. - Implementation: Generator, discriminator. - Evaluation: Adversarial loss. Day 25: Transfer Learning - Concept: Pre-trained models. - Implementation: Fine-tuning. - Evaluation: Domain adaptation.

𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬 𝐀𝐜𝐫𝐨𝐬𝐬 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐌𝐋 𝐑𝐨𝐥𝐞𝐬. 📝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (Avg salary for a fresher: 6-8 LPA) 1. Excel 2. SQL (80% of the interview will be on expertise in SQL) 3. Python (Basic to intermediate knowledge required) 4. Data visualization tool (Most common: Tableau/PowerBI) 5. Statistics (Basic to intermediate) 📝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 (Avg salary for a fresher: 10-15 LPA) 1. Excel, SQL, Python, Tableau/PowerBI, Statistics (All data analyst skills) 2. Mathematics (Linear algebra, Calculus) 3. Machine learning (Scikit-learn: Supervised, Unsupervised, Recommender systems, Timeseries modelling) 4. Deep learning (TensorFlow, PyTorch) 5. NLP (NLTK, spacy, gensim) 📝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 9-12 LPA) 1. Big data tools (Hadoop, Spark, Hive) 2. Python, Java or Scala 3. Data pipeline automation 4. SQL & NoSQL databases 5. ETL tools & Data warehousing (Apache Nifi, Talend, Airflow) 6. Cloud computing (AWS, Azure, GCP) 📝 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 10-12 LPA) 1. Cloud platforms (AWS, Azure, GCP) 2. Machine learning 3. DevOps & CI/CD 4. Version control 5. Code optimization & Tuning 📝 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 8-10 LPA) 1. CI/CD for ML Pipelines 2. Docker, Kubernetes & Container orchestration 3. Monitoring & Logging (Prometheus, Grafana, ELK stack: Elasticsearch, Logstash, Kibana) 4. Model versioning & Governance (MLflow, DVC) 5. Infrastructure as code (IaC): Teraform, CloudFormation, Ansible 6. API development & Integration 7. Automated testing for data validation, model performance & pipeline integrity I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Why Python is a Must-Have Skill? If you're diving into programming or data science, mastering Python is essential! Its versatility and simplicity make it the go-to language across industries. ◆ Powerful and Versatile From web development to data analysis, Python’s broad libraries and frameworks adapt to almost any project. ◆ Data-Driven Python, combined with libraries like Pandas and NumPy, allows you to analyze and manipulate datasets efficiently. ◆ Automate the Boring Stuff Automate repetitive tasks, streamline workflows, and boost productivity with Python’s easy-to-use scripts. ◆ AI and Machine Learning With frameworks like TensorFlow and Scikit-learn, Python is at the forefront of AI, enabling you to build predictive models and explore deep learning. ◆ Readable and Beginner-Friendly Python’s simple syntax makes it easy to learn, even for beginners, without sacrificing power and functionality. ◆ Community Support Backed by a massive global community, Python is constantly evolving, with new libraries and resources available at your fingertips. I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/analyst/907371 Hope you'll like it Like this post if you need more resources like this 👍❤️

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Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest: • Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge. • Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you. • Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role. But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI. No matter where your path leads, the key is to start now.

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💻 String Functions in SQL If you're working with databases, string manipulation is a must have! Here is a quick overview of common SQL string functions 👇 --- 📝 1. CONCAT() - Description: Concatenates two or more strings. - Syntax: SELECT CONCAT(string1, string2, ...) AS concatenated_string - Example: SELECT CONCAT(first_name, ' ', last_name) AS full_name --- 📝 2. SUBSTRING()/SUBSTR() - Description: Extracts a substring from a string. - Syntax: SELECT SUBSTRING(string FROM start_position FOR length) AS substring - Example: SELECT SUBSTRING(product_name FROM 1 FOR 5) AS short_name --- 📝 3. CHAR_LENGTH()/LENGTH() - Description: Returns the length of a string. - Syntax: SELECT CHAR_LENGTH(string) AS length - Example: SELECT CHAR_LENGTH(product_name) AS product_name_length --- 📝 4. UPPER() - Description: Converts all characters to uppercase. - Syntax: SELECT UPPER(string) AS uppercase_string - Example: SELECT UPPER(first_name) AS upper_name --- 📝 5. LOWER() - Description: Converts all characters to lowercase. - Syntax: SELECT LOWER(string) AS lowercase_string - Example: SELECT LOWER(last_name) AS lower_name --- 📝 6. TRIM() - Description: Removes specified prefixes/suffixes or whitespace from a string. - Syntax: SELECT TRIM([LEADING | TRAILING | BOTH] characters FROM string) AS trimmed_string - Example: SELECT TRIM(TRAILING ' ' FROM full_name) AS trimmed_name --- 📝 7. LEFT() - Description: Returns a specified number of characters from the left of a string. - Syntax: SELECT LEFT(string, num_characters) AS left_string - Example: SELECT LEFT(product_name, 5) AS left_product_name --- 📝 8. RIGHT() - Description: Returns a specified number of characters from the right of a string. - Syntax: SELECT RIGHT(string, num_characters) AS right_string - Example: SELECT RIGHT(order_number, 4) AS right_order_number --- 📝 9. REPLACE() - Description: Replaces occurrences of a substring within a string. - Syntax: SELECT REPLACE(string, old_substring, new_substring) AS replaced_string - Example: SELECT REPLACE(description, 'old', 'new') AS updated_description I have curated essential SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you need more 👍❤️ Hope it helps :)

Roadmap to Becoming a Python Developer 🚀 1. Basics 🌱 - Learn programming fundamentals and Python syntax. 2. Core Python 🧠 - Master data structures, functions, and OOP. 3. Advanced Python 📈 - Explore modules, file handling, and exceptions. 4. Web Development 🌐 - Use Django or Flask; build REST APIs. 5. Data Science 📊 - Learn NumPy, pandas, and Matplotlib. 6. Projects & Practice💡 - Build projects, contribute to open-source, join communities. Python Interview Q&A: https://topmate.io/analyst/907371 Like for more ❤️ ENJOY LEARNING 👍👍

Complete Syllabus for Data Analytics interview: SQL: 1. Basic    - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING    - Basic JOINS (INNER, LEFT, RIGHT, FULL)    - Creating and using simple databases and tables 2. Intermediate    - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)    - Subqueries and nested queries - Common Table Expressions (WITH clause)    - CASE statements for conditional logic in queries 3. Advanced    - Advanced JOIN techniques (self-join, non-equi join)    - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)    - optimization with indexing    - Data manipulation (INSERT, UPDATE, DELETE) Python: 1. Basic    - Syntax, variables, data types (integers, floats, strings, booleans)    - Control structures (if-else, for and while loops)    - Basic data structures (lists, dictionaries, sets, tuples)    - Functions, lambda functions, error handling (try-except)    - Modules and packages 2. Pandas & Numpy    - Creating and manipulating DataFrames and Series    - Indexing, selecting, and filtering data    - Handling missing data (fillna, dropna)    - Data aggregation with groupby, summarizing data    - Merging, joining, and concatenating datasets 3. Basic Visualization    - Basic plotting with Matplotlib (line plots, bar plots, histograms)    - Visualization with Seaborn (scatter plots, box plots, pair plots)    - Customizing plots (sizes, labels, legends, color palettes)    - Introduction to interactive visualizations (e.g., Plotly) Excel: 1. Basic    - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)    - Introduction to charts and basic data visualization    - Data sorting and filtering    - Conditional formatting 2. Intermediate    - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)    - PivotTables and PivotCharts for summarizing data    - Data validation tools    - What-if analysis tools (Data Tables, Goal Seek) 3. Advanced    - Array formulas and advanced functions    - Data Model & Power Pivot - Advanced Filter - Slicers and Timelines in Pivot Tables    - Dynamic charts and interactive dashboards Power BI: 1. Data Modeling    - Importing data from various sources    - Creating and managing relationships between different datasets    - Data modeling basics (star schema, snowflake schema) 2. Data Transformation    - Using Power Query for data cleaning and transformation    - Advanced data shaping techniques    - Calculated columns and measures using DAX 3. Data Visualization and Reporting   - Creating interactive reports and dashboards    - Visualizations (bar, line, pie charts, maps)    - Publishing and sharing reports, scheduling data refreshes Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution. Like for more 😄❤️