If you want to grow, keep these 5 tips in mind:
1. Understand that real change takes time—stay patient.
2. Make learning a daily habit, even if it’s just a little.
3. Choose friends who push you to improve, not just those who agree.
4. Reflect on your progress—celebrate every step forward.
5. Be mindful of your daily habits—they shape who you become.
1. Understand that real change takes time—stay patient.
2. Make learning a daily habit, even if it’s just a little.
3. Choose friends who push you to improve, not just those who agree.
4. Reflect on your progress—celebrate every step forward.
5. Be mindful of your daily habits—they shape who you become.
❤1
Free Programming and Data Analytics Resources 👇👇
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📚 Useful Resources for the Programmers
Data Analyst Roadmap
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Interactive React Native Resources
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https://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
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Unity Documentation
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Advanced Javascript concepts
https://t.iss.one/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
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✅ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
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https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/1371
🔍 Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
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3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
🤖 Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
📚 Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
🍻 Free Programming Courses by Microsoft
❯ JavaScript
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https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
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❤2
Core data science concepts you should know:
🔢 1. Statistics & Probability
Descriptive statistics: Mean, median, mode, standard deviation, variance
Inferential statistics: Hypothesis testing, confidence intervals, p-values, t-tests, ANOVA
Probability distributions: Normal, Binomial, Poisson, Uniform
Bayes' Theorem
Central Limit Theorem
📊 2. Data Wrangling & Cleaning
Handling missing values
Outlier detection and treatment
Data transformation (scaling, encoding, normalization)
Feature engineering
Dealing with imbalanced data
📈 3. Exploratory Data Analysis (EDA)
Univariate, bivariate, and multivariate analysis
Correlation and covariance
Data visualization tools: Matplotlib, Seaborn, Plotly
Insights generation through visual storytelling
🤖 4. Machine Learning Fundamentals
Supervised Learning: Linear regression, logistic regression, decision trees, SVM, k-NN
Unsupervised Learning: K-means, hierarchical clustering, PCA
Model evaluation: Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and overfitting/underfitting
Bias-variance tradeoff
🧠 5. Deep Learning (Basics)
Neural networks: Perceptron, MLP
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation
Gradient descent and learning rate
CNNs and RNNs (intro level)
🗃️ 6. Data Structures & Algorithms (DSA)
Arrays, lists, dictionaries, sets
Sorting and searching algorithms
Time and space complexity (Big-O notation)
Common problems: string manipulation, matrix operations, recursion
💾 7. SQL & Databases
SELECT, WHERE, GROUP BY, HAVING
JOINS (inner, left, right, full)
Subqueries and CTEs
Window functions
Indexing and normalization
📦 8. Tools & Libraries
Python: pandas, NumPy, scikit-learn, TensorFlow, PyTorch
R: dplyr, ggplot2, caret
Jupyter Notebooks for experimentation
Git and GitHub for version control
🧪 9. A/B Testing & Experimentation
Control vs. treatment group
Hypothesis formulation
Significance level, p-value interpretation
Power analysis
🌐 10. Business Acumen & Storytelling
Translating data insights into business value
Crafting narratives with data
Building dashboards (Power BI, Tableau)
Knowing KPIs and business metrics
React ❤️ for more
🔢 1. Statistics & Probability
Descriptive statistics: Mean, median, mode, standard deviation, variance
Inferential statistics: Hypothesis testing, confidence intervals, p-values, t-tests, ANOVA
Probability distributions: Normal, Binomial, Poisson, Uniform
Bayes' Theorem
Central Limit Theorem
📊 2. Data Wrangling & Cleaning
Handling missing values
Outlier detection and treatment
Data transformation (scaling, encoding, normalization)
Feature engineering
Dealing with imbalanced data
📈 3. Exploratory Data Analysis (EDA)
Univariate, bivariate, and multivariate analysis
Correlation and covariance
Data visualization tools: Matplotlib, Seaborn, Plotly
Insights generation through visual storytelling
🤖 4. Machine Learning Fundamentals
Supervised Learning: Linear regression, logistic regression, decision trees, SVM, k-NN
Unsupervised Learning: K-means, hierarchical clustering, PCA
Model evaluation: Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and overfitting/underfitting
Bias-variance tradeoff
🧠 5. Deep Learning (Basics)
Neural networks: Perceptron, MLP
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation
Gradient descent and learning rate
CNNs and RNNs (intro level)
🗃️ 6. Data Structures & Algorithms (DSA)
Arrays, lists, dictionaries, sets
Sorting and searching algorithms
Time and space complexity (Big-O notation)
Common problems: string manipulation, matrix operations, recursion
💾 7. SQL & Databases
SELECT, WHERE, GROUP BY, HAVING
JOINS (inner, left, right, full)
Subqueries and CTEs
Window functions
Indexing and normalization
📦 8. Tools & Libraries
Python: pandas, NumPy, scikit-learn, TensorFlow, PyTorch
R: dplyr, ggplot2, caret
Jupyter Notebooks for experimentation
Git and GitHub for version control
🧪 9. A/B Testing & Experimentation
Control vs. treatment group
Hypothesis formulation
Significance level, p-value interpretation
Power analysis
🌐 10. Business Acumen & Storytelling
Translating data insights into business value
Crafting narratives with data
Building dashboards (Power BI, Tableau)
Knowing KPIs and business metrics
React ❤️ for more
❤4
𝟳 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 & 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍
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❤1
Roadmap to become a data analyst
1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.
2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.
3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.
4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.
5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.
6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.
7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.
8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.
9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.
10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.
1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.
2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.
3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.
4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.
5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.
6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.
7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.
8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.
9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.
10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.
❤1
Forwarded from Python Projects & Resources
𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍
Dreaming of a career in Data Analytics but don’t know where to begin?
The Career Essentials in Data Analysis program by Microsoft and LinkedIn is a 100% FREE learning path designed to equip you with real-world skills and industry-recognized certification.
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❤1
Advanced Data Science Concepts 🚀
1️⃣ Feature Engineering & Selection
Handling Missing Values – Imputation techniques (mean, median, KNN).
Encoding Categorical Variables – One-Hot Encoding, Label Encoding, Target Encoding.
Scaling & Normalization – StandardScaler, MinMaxScaler, RobustScaler.
Dimensionality Reduction – PCA, t-SNE, UMAP, LDA.
2️⃣ Machine Learning Optimization
Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization.
Model Validation – Cross-validation, Bootstrapping.
Class Imbalance Handling – SMOTE, Oversampling, Undersampling.
Ensemble Learning – Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.
3️⃣ Deep Learning & Neural Networks
Neural Network Architectures – CNNs, RNNs, Transformers.
Activation Functions – ReLU, Sigmoid, Tanh, Softmax.
Optimization Algorithms – SGD, Adam, RMSprop.
Transfer Learning – Pre-trained models like BERT, GPT, ResNet.
4️⃣ Time Series Analysis
Forecasting Models – ARIMA, SARIMA, Prophet.
Feature Engineering for Time Series – Lag features, Rolling statistics.
Anomaly Detection – Isolation Forest, Autoencoders.
5️⃣ NLP (Natural Language Processing)
Text Preprocessing – Tokenization, Stemming, Lemmatization.
Word Embeddings – Word2Vec, GloVe, FastText.
Sequence Models – LSTMs, Transformers, BERT.
Text Classification & Sentiment Analysis – TF-IDF, Attention Mechanism.
6️⃣ Computer Vision
Image Processing – OpenCV, PIL.
Object Detection – YOLO, Faster R-CNN, SSD.
Image Segmentation – U-Net, Mask R-CNN.
7️⃣ Reinforcement Learning
Markov Decision Process (MDP) – Reward-based learning.
Q-Learning & Deep Q-Networks (DQN) – Policy improvement techniques.
Multi-Agent RL – Competitive and cooperative learning.
8️⃣ MLOps & Model Deployment
Model Monitoring & Versioning – MLflow, DVC.
Cloud ML Services – AWS SageMaker, GCP AI Platform.
API Deployment – Flask, FastAPI, TensorFlow Serving.
Like if you want detailed explanation on each topic ❤️
Data Science & Machine Learning Resources: https://t.iss.one/datasciencefun
Hope this helps you 😊
1️⃣ Feature Engineering & Selection
Handling Missing Values – Imputation techniques (mean, median, KNN).
Encoding Categorical Variables – One-Hot Encoding, Label Encoding, Target Encoding.
Scaling & Normalization – StandardScaler, MinMaxScaler, RobustScaler.
Dimensionality Reduction – PCA, t-SNE, UMAP, LDA.
2️⃣ Machine Learning Optimization
Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization.
Model Validation – Cross-validation, Bootstrapping.
Class Imbalance Handling – SMOTE, Oversampling, Undersampling.
Ensemble Learning – Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.
3️⃣ Deep Learning & Neural Networks
Neural Network Architectures – CNNs, RNNs, Transformers.
Activation Functions – ReLU, Sigmoid, Tanh, Softmax.
Optimization Algorithms – SGD, Adam, RMSprop.
Transfer Learning – Pre-trained models like BERT, GPT, ResNet.
4️⃣ Time Series Analysis
Forecasting Models – ARIMA, SARIMA, Prophet.
Feature Engineering for Time Series – Lag features, Rolling statistics.
Anomaly Detection – Isolation Forest, Autoencoders.
5️⃣ NLP (Natural Language Processing)
Text Preprocessing – Tokenization, Stemming, Lemmatization.
Word Embeddings – Word2Vec, GloVe, FastText.
Sequence Models – LSTMs, Transformers, BERT.
Text Classification & Sentiment Analysis – TF-IDF, Attention Mechanism.
6️⃣ Computer Vision
Image Processing – OpenCV, PIL.
Object Detection – YOLO, Faster R-CNN, SSD.
Image Segmentation – U-Net, Mask R-CNN.
7️⃣ Reinforcement Learning
Markov Decision Process (MDP) – Reward-based learning.
Q-Learning & Deep Q-Networks (DQN) – Policy improvement techniques.
Multi-Agent RL – Competitive and cooperative learning.
8️⃣ MLOps & Model Deployment
Model Monitoring & Versioning – MLflow, DVC.
Cloud ML Services – AWS SageMaker, GCP AI Platform.
API Deployment – Flask, FastAPI, TensorFlow Serving.
Like if you want detailed explanation on each topic ❤️
Data Science & Machine Learning Resources: https://t.iss.one/datasciencefun
Hope this helps you 😊
❤3
ETL vs ELT – Explained Using Apple Juice analogy! 🍎🧃
We often hear about ETL and ELT in the data world — but how do they actually apply in tools like Excel and Power BI?
Let’s break it down with a simple and relatable analogy 👇
✅ ETL (Extract → Transform → Load)
🧃 First you make the juice, then you deliver it
➡️ Apples → Juice → Truck
🔹 In Power BI / Excel:
You clean and transform the data in Power Query
Then load the final data into your report or sheet
💡 That’s ETL – transformation happens before loading
✅ ELT (Extract → Load → Transform)
🍏 First you deliver the apples, and make juice later
➡️ Apples → Truck → Juice
🔹 In Power BI / Excel:
You load raw data into your model or sheet
Then transform it using DAX, formulas, or pivot tables
💡 That’s ELT – transformation happens after loading
We often hear about ETL and ELT in the data world — but how do they actually apply in tools like Excel and Power BI?
Let’s break it down with a simple and relatable analogy 👇
✅ ETL (Extract → Transform → Load)
🧃 First you make the juice, then you deliver it
➡️ Apples → Juice → Truck
🔹 In Power BI / Excel:
You clean and transform the data in Power Query
Then load the final data into your report or sheet
💡 That’s ETL – transformation happens before loading
✅ ELT (Extract → Load → Transform)
🍏 First you deliver the apples, and make juice later
➡️ Apples → Truck → Juice
🔹 In Power BI / Excel:
You load raw data into your model or sheet
Then transform it using DAX, formulas, or pivot tables
💡 That’s ELT – transformation happens after loading
❤2
𝟱 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍
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7 Baby Steps to Learn Excel
1. Understand the Basics: Start by getting familiar with Excel's interface, including workbooks, worksheets, cells, rows, and columns. Learn basic operations like entering and editing data, formatting cells, and using basic formulas (e.g., SUM, AVERAGE, COUNT).
2. Master Essential Functions: Excel's power lies in its functions. Focus on learning frequently used ones like:
Mathematical: SUM, AVERAGE, ROUND
Text: CONCATENATE, LEFT, RIGHT, LEN
Logical: IF, AND, OR
Lookup: VLOOKUP, HLOOKUP, INDEX, MATCH
3. Work with Data: Learn how to organize, sort, and filter data effectively. Practice creating and formatting tables to handle structured data, and explore data validation to restrict input values.
4. Visualize with Charts: Understand how to create charts like bar, line, and pie charts to represent data visually. Learn the importance of choosing the right chart type and practice customizing them for clarity and impact.
5. Explore Pivot Tables: Pivot tables are essential for summarizing large datasets. Learn how to create pivot tables, use slicers for dynamic filtering, and analyze data using fields like Rows, Columns, Values, and Filters.
6. Use Advanced Features: Dive into advanced features like conditional formatting, macros, and Excel's built-in tools for data analysis (e.g., Goal Seek, Solver, and Data Analysis ToolPak). Learn how to work with Array Formulas and explore the power of XLOOKUP (in newer versions).
7. Engage with Excel Communities: Join Excel communities on forums like Reddit’s r/Excel, or Microsoft’s Excel Community. Participate in challenges on platforms like ExcelJet, LeetCode, or Kaggle to improve your problem-solving skills and get insights from experts.
Additional Tips:
- Regularly practice on real-world datasets.
- Learn keyboard shortcuts to speed up your work.
- Explore Microsoft Excel's official documentation and free online tutorials for deeper insights.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Hope it helps :)
1. Understand the Basics: Start by getting familiar with Excel's interface, including workbooks, worksheets, cells, rows, and columns. Learn basic operations like entering and editing data, formatting cells, and using basic formulas (e.g., SUM, AVERAGE, COUNT).
2. Master Essential Functions: Excel's power lies in its functions. Focus on learning frequently used ones like:
Mathematical: SUM, AVERAGE, ROUND
Text: CONCATENATE, LEFT, RIGHT, LEN
Logical: IF, AND, OR
Lookup: VLOOKUP, HLOOKUP, INDEX, MATCH
3. Work with Data: Learn how to organize, sort, and filter data effectively. Practice creating and formatting tables to handle structured data, and explore data validation to restrict input values.
4. Visualize with Charts: Understand how to create charts like bar, line, and pie charts to represent data visually. Learn the importance of choosing the right chart type and practice customizing them for clarity and impact.
5. Explore Pivot Tables: Pivot tables are essential for summarizing large datasets. Learn how to create pivot tables, use slicers for dynamic filtering, and analyze data using fields like Rows, Columns, Values, and Filters.
6. Use Advanced Features: Dive into advanced features like conditional formatting, macros, and Excel's built-in tools for data analysis (e.g., Goal Seek, Solver, and Data Analysis ToolPak). Learn how to work with Array Formulas and explore the power of XLOOKUP (in newer versions).
7. Engage with Excel Communities: Join Excel communities on forums like Reddit’s r/Excel, or Microsoft’s Excel Community. Participate in challenges on platforms like ExcelJet, LeetCode, or Kaggle to improve your problem-solving skills and get insights from experts.
Additional Tips:
- Regularly practice on real-world datasets.
- Learn keyboard shortcuts to speed up your work.
- Explore Microsoft Excel's official documentation and free online tutorials for deeper insights.
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❤1
𝗧𝗼𝗽 𝟱 𝗙𝗿𝗲𝗲 𝗞𝗮𝗴𝗴𝗹𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗝𝘂𝗺𝗽𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍
Want to break into Data Science but not sure where to start?🚀
These free Kaggle micro-courses are the perfect launchpad — beginner-friendly, self-paced, and yes, they come with certifications!👨🎓🎊
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No subscription. No hidden fees. Just pure learning from a trusted platform✅️
Want to break into Data Science but not sure where to start?🚀
These free Kaggle micro-courses are the perfect launchpad — beginner-friendly, self-paced, and yes, they come with certifications!👨🎓🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4l164FN
No subscription. No hidden fees. Just pure learning from a trusted platform✅️
10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
❤1
Forwarded from Python Projects & Resources
𝟱 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗮𝗿𝗲𝗲𝗿 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍
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❤1
Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
❤1
Forwarded from Python Projects & Resources
𝟱 𝗙𝗥𝗘𝗘 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗗𝗮𝘁𝗮 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍
Want to break into Data Analytics or Data Science—but don’t know where to begin?🚀
Harvard University offers 5 completely free online courses that will build your foundation in Python, statistics, machine learning, and data visualization — no prior experience or degree required!👨🎓💫
𝐋𝐢𝐧𝐤👇:-
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These Harvard-certified courses will boost your resume, LinkedIn profile, and skills✅️
Want to break into Data Analytics or Data Science—but don’t know where to begin?🚀
Harvard University offers 5 completely free online courses that will build your foundation in Python, statistics, machine learning, and data visualization — no prior experience or degree required!👨🎓💫
𝐋𝐢𝐧𝐤👇:-
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These Harvard-certified courses will boost your resume, LinkedIn profile, and skills✅️
Some essential concepts every data scientist should understand:
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.
### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).
### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.
### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.
### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).
### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.
### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).
### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.
### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.
### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.
### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.
### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.
### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.
### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.
### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.
### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).
### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.
### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.
### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).
### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.
### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).
### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.
### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.
### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.
### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.
### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.
### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.
### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.
### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤3
Forwarded from Artificial Intelligence
𝟱 𝗙𝗥𝗘𝗘 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗜𝗕𝗠, 𝗨𝗱𝗮𝗰𝗶𝘁𝘆 & 𝗠𝗼𝗿𝗲😍
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𝐋𝐢𝐧𝐤👇:-
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Kickstart your career✅️
Looking to learn Python from scratch—without spending a rupee? 💻
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Kickstart your career✅️
Essential Programming Languages to Learn Data Science 👇👇
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
❤3
𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍
I failed my first data interview — and here’s why:⬇️
❌ No structured learning
❌ No real projects
❌ Just random YouTube tutorials and half-read blogs
If this sounds like you, don’t repeat my mistake✨️
Recruiters want proof of skills, not just buzzwords📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ka1ZOl
All The Best 🎊
I failed my first data interview — and here’s why:⬇️
❌ No structured learning
❌ No real projects
❌ Just random YouTube tutorials and half-read blogs
If this sounds like you, don’t repeat my mistake✨️
Recruiters want proof of skills, not just buzzwords📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ka1ZOl
All The Best 🎊
❤2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
List of Top 12 Coding Channels on WhatsApp:
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
❤1