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😍
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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
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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.
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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 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤1
𝗧𝗼𝗽 𝟱 𝗙𝗿𝗲𝗲 𝗞𝗮𝗴𝗴𝗹𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗝𝘂𝗺𝗽𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍
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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😍
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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😍
Looking to learn Python from scratch—without spending a rupee? 💻
Offered by trusted platforms like Harvard University, IBM, Udacity, freeCodeCamp, and OpenClassrooms, each course is self-paced, easy to follow, and includes a certificate of completion🔥👨🎓
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3HNeyBQ
Kickstart your career✅️
Looking to learn Python from scratch—without spending a rupee? 💻
Offered by trusted platforms like Harvard University, IBM, Udacity, freeCodeCamp, and OpenClassrooms, each course is self-paced, easy to follow, and includes a certificate of completion🔥👨🎓
𝐋𝐢𝐧𝐤👇:-
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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
Forwarded from Artificial Intelligence
𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗤𝗟 𝗖𝗮𝗻 𝗕𝗲 𝗙𝘂𝗻! 𝟰 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗧𝗵𝗮𝘁 𝗙𝗲𝗲𝗹 𝗟𝗶𝗸𝗲 𝗮 𝗚𝗮𝗺𝗲😍
Think SQL is all about dry syntax and boring tutorials? Think again.🤔
These 4 gamified SQL websites turn learning into an adventure — from solving murder mysteries to exploring virtual islands, you’ll write real SQL queries while cracking clues and completing missions📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4nh6PMv
These platforms make SQL interactive, practical, and fun✅️
Think SQL is all about dry syntax and boring tutorials? Think again.🤔
These 4 gamified SQL websites turn learning into an adventure — from solving murder mysteries to exploring virtual islands, you’ll write real SQL queries while cracking clues and completing missions📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4nh6PMv
These platforms make SQL interactive, practical, and fun✅️
❤2
This is how data analytics teams work!
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!
End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!
End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤1
𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀𝗲𝘁 😍
✅ Artificial Intelligence – Master AI & Machine Learning
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𝐋𝐢𝐧𝐤 👇:-
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✅ Artificial Intelligence – Master AI & Machine Learning
✅ Blockchain – Understand decentralization & smart contracts💰
✅ Cloud Computing – Learn AWS, Azure&cloud infrastructure ☁
✅ Web 3.0 – Explore the future of the Internet &Apps 🌐
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4aM1QO0
Enroll For FREE & Get Certified 🎓
Practice projects to consider:
1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.
2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.
3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.
4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.
1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.
2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.
3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.
4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.
❤2
Forwarded from Python Projects & Resources
𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 😍
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/40OgK1w
Enroll For FREE & Get Certified 🎓
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/40OgK1w
Enroll For FREE & Get Certified 🎓