🚀 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.iss.one/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/
Javascript Cheatsheet
quickref.me/javascript.html
t.iss.one/javascript_courses/532
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://t.iss.one/pythondevelopersindia/314
GIT and Machine Learning Cheatsheet
https://t.iss.one/datasciencefun/714
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
t.iss.one/webdevelopmentbook/90
Data Visualization
t.iss.one/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.iss.one/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/
Javascript Cheatsheet
quickref.me/javascript.html
t.iss.one/javascript_courses/532
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://t.iss.one/pythondevelopersindia/314
GIT and Machine Learning Cheatsheet
https://t.iss.one/datasciencefun/714
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
t.iss.one/webdevelopmentbook/90
Data Visualization
t.iss.one/datasciencefun/698
Free entry to our WhatsApp channel
Join @free4unow_backup for more free resources
Like for more ❤️
ENJOY LEARNING👍👍
❤7
SQL Joins — A Practical Cheatsheet for Professionals
If you’re working with relational data — whether you’re a business analyst, backend dev, or aspiring data scientist — mastering SQL joins isn’t optional. It’s fundamental.
Here’s a concise guide to the most important join types, with real-world use cases:
INNER JOIN
Returns records with matching keys from both tables.
Use case: Show only customers who’ve placed at least one order.
LEFT JOIN (OUTER)
Returns all rows from the left table, and matched rows from the right.
Use case: List all customers, including those with zero orders.
RIGHT JOIN (OUTER)
Returns all rows from the right table. Rarely used, but powerful.
Use case: Show all orders, even if the customer was deleted.
FULL OUTER JOIN
Returns all records from both tables.
Use case: Capture everything — matched and unmatched.
CROSS JOIN
Returns the cartesian product.
Use case: Generate every possible product/supplier combo.
SELF JOIN
Joins a table to itself.
Use case: Show employees and their reporting managers.
Best Practices
Use aliases (A, B) for clean code
Prefer JOIN ON over WHERE for clarity
Always test joins with LIMIT to prevent overloads
If you’re working with relational data — whether you’re a business analyst, backend dev, or aspiring data scientist — mastering SQL joins isn’t optional. It’s fundamental.
Here’s a concise guide to the most important join types, with real-world use cases:
INNER JOIN
Returns records with matching keys from both tables.
Use case: Show only customers who’ve placed at least one order.
LEFT JOIN (OUTER)
Returns all rows from the left table, and matched rows from the right.
Use case: List all customers, including those with zero orders.
RIGHT JOIN (OUTER)
Returns all rows from the right table. Rarely used, but powerful.
Use case: Show all orders, even if the customer was deleted.
FULL OUTER JOIN
Returns all records from both tables.
Use case: Capture everything — matched and unmatched.
CROSS JOIN
Returns the cartesian product.
Use case: Generate every possible product/supplier combo.
SELF JOIN
Joins a table to itself.
Use case: Show employees and their reporting managers.
Best Practices
Use aliases (A, B) for clean code
Prefer JOIN ON over WHERE for clarity
Always test joins with LIMIT to prevent overloads
❤6🔥3
The Data Science skill no one talks about...
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesn’t.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Let’s go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
We say that a user churns when she decides to stop using Uber.
But why?
There are different reasons why a user would stop using Uber. For example:
1. “Lyft is offering better prices for that geo” (pricing problem)
2. “Car waiting times are too long” (supply problem)
3. “The Android version of the app is very slow” (client-app performance problem)
You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.
Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?
This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.
You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.
For example…
Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)
One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:
The A group. No user in this group will receive any discount.
The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.
You could add more groups (e.g. C, D, E…) to test different pricing points.
1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesn’t.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Let’s go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
👩💼: “We want to decrease user churn by 5% this quarter”
We say that a user churns when she decides to stop using Uber.
But why?
There are different reasons why a user would stop using Uber. For example:
1. “Lyft is offering better prices for that geo” (pricing problem)
2. “Car waiting times are too long” (supply problem)
3. “The Android version of the app is very slow” (client-app performance problem)
You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.
Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?
This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.
You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.
For example…
Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)
One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:
The A group. No user in this group will receive any discount.
The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.
You could add more groups (e.g. C, D, E…) to test different pricing points.
In a nutshell
1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem
❤10
📊 Data Science Essentials: What Every Data Enthusiast Should Know!
1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.
2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.
3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.
4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.
5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (
6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.
7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.
8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.
🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!
Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!
1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.
2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.
3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.
4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.
5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (
SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.
7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.
8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.
🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!
Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!
❤5👍2