Core data science concepts you should know:
🔢 1. Statistics & Probability
Denoscriptive 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
Denoscriptive 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
❤11
Steps to become a data analyst
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://news.1rj.ru/str/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://news.1rj.ru/str/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
❤3🔥2👏1
Data Analyst: Analyzes data to provide insights and reports for decision-making.
Data Scientist: Builds models to predict outcomes and uncover deeper insights from data.
Data Engineer: Creates and maintains the systems that store and process data.
Data Scientist: Builds models to predict outcomes and uncover deeper insights from data.
Data Engineer: Creates and maintains the systems that store and process data.
❤5👍1
If you want to Excel in Data Science and become an expert, master these essential concepts:
Core Data Science Skills:
• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends
Machine Learning (ML):
• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search
Deep Learning (DL):
• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs
Real-World Applications:
• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making
React with ❤️ for more
Core Data Science Skills:
• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends
Machine Learning (ML):
• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search
Deep Learning (DL):
• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs
Real-World Applications:
• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making
React with ❤️ for more
❤9👍1
SQL Cheatsheet 📝
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.
1. Database Basics
-
-
2. Tables
- Create Table:
- Drop Table:
- Alter Table:
3. Insert Data
-
4. Select Queries
- Basic Select:
- Select Specific Columns:
- Select with Condition:
5. Update Data
-
6. Delete Data
-
7. Joins
- Inner Join:
- Left Join:
- Right Join:
8. Aggregations
- Count:
- Sum:
- Group By:
9. Sorting & Limiting
- Order By:
- Limit Results:
10. Indexes
- Create Index:
- Drop Index:
11. Subqueries
-
12. Views
- Create View:
- Drop View:
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.
1. Database Basics
-
CREATE DATABASE db_name;-
USE db_name;2. Tables
- Create Table:
CREATE TABLE table_name (col1 datatype, col2 datatype);- Drop Table:
DROP TABLE table_name;- Alter Table:
ALTER TABLE table_name ADD column_name datatype;3. Insert Data
-
INSERT INTO table_name (col1, col2) VALUES (val1, val2);4. Select Queries
- Basic Select:
SELECT * FROM table_name;- Select Specific Columns:
SELECT col1, col2 FROM table_name;- Select with Condition:
SELECT * FROM table_name WHERE condition;5. Update Data
-
UPDATE table_name SET col1 = value1 WHERE condition;6. Delete Data
-
DELETE FROM table_name WHERE condition;7. Joins
- Inner Join:
SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;- Left Join:
SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;- Right Join:
SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;8. Aggregations
- Count:
SELECT COUNT(*) FROM table_name;- Sum:
SELECT SUM(col) FROM table_name;- Group By:
SELECT col, COUNT(*) FROM table_name GROUP BY col;9. Sorting & Limiting
- Order By:
SELECT * FROM table_name ORDER BY col ASC|DESC;- Limit Results:
SELECT * FROM table_name LIMIT n;10. Indexes
- Create Index:
CREATE INDEX idx_name ON table_name (col);- Drop Index:
DROP INDEX idx_name;11. Subqueries
-
SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);12. Views
- Create View:
CREATE VIEW view_name AS SELECT * FROM table_name;- Drop View:
DROP VIEW view_name;❤5🔥1
🚀 Complete Roadmap to Become a Data Scientist in 5 Months
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
❤7
Here's a good list of cheat sheets for programmers (all free):
Data Science Cheatsheet
https://github.com/aaronwangy/Data-Science-Cheatsheet
SQL Cheatsheet
sqltutorial.org/sql-cheat-sheet
t.me/sqlspecialist/827
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
Java Programming Cheatsheet
https://introcs.cs.princeton.edu/java/11cheatsheet/
Javanoscript Cheatsheet
quickref.me/javanoscript.html
t.me/javanoscript_courses/532
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://news.1rj.ru/str/pythondevelopersindia/314
GIT and Machine Learning Cheatsheet
https://news.1rj.ru/str/datasciencefun/714
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
t.me/webdevelopmentbook/90
Data Visualization
t.me/datasciencefun/698
Free entry to our WhatsApp channel
Join @free4unow_backup for more free resources
Like for more ❤️
ENJOY LEARNING👍👍
Data Science Cheatsheet
https://github.com/aaronwangy/Data-Science-Cheatsheet
SQL Cheatsheet
sqltutorial.org/sql-cheat-sheet
t.me/sqlspecialist/827
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
Java Programming Cheatsheet
https://introcs.cs.princeton.edu/java/11cheatsheet/
Javanoscript Cheatsheet
quickref.me/javanoscript.html
t.me/javanoscript_courses/532
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://news.1rj.ru/str/pythondevelopersindia/314
GIT and Machine Learning Cheatsheet
https://news.1rj.ru/str/datasciencefun/714
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
t.me/webdevelopmentbook/90
Data Visualization
t.me/datasciencefun/698
Free entry to our WhatsApp channel
Join @free4unow_backup for more free resources
Like for more ❤️
ENJOY LEARNING👍👍
❤7