How to choose Data Science Career 👆

🔰 Machine Learning Roadmap for Beginners 2025
├── 🧠 What is Machine Learning?
├── 🧪 ML vs AI vs Deep Learning
├── 🔢 Math Foundation (Linear Algebra, Calculus, Stats Basics)
├── 🐍 Python Libraries (NumPy, Pandas, Scikit-learn)
├── 📊 Data Preprocessing & Cleaning
├── 📉 Feature Selection & Engineering
├── 🧭 Supervised Learning (Regression, Classification)
├── 🧱 Unsupervised Learning (Clustering, Dimensionality Reduction)
├── 🕹 Model Evaluation (Confusion Matrix, ROC, AUC)
├── ⚙️ Model Tuning (Hyperparameter Tuning, Grid Search)
├── 🧰 Ensemble Methods (Bagging, Boosting, Random Forests)
├── 🔮 Introduction to Neural Networks
├── 🔁 Overfitting vs Underfitting
├── 📈 Model Deployment (Streamlit, Flask, FastAPI Basics)
├── 🧪 ML Projects (Classification, Forecasting, Recommender)
├── 🏆 ML Competitions (Kaggle, Hackathons)

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#machinelearning

If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ Master Advanced SQL

Foundations: Learn database structures, tables, and relationships.

Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.

Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.

JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.

Advanced Concepts: CTEs, window functions, and query optimization.

Metric Development: Build and report metrics effectively.


2️⃣ Study Statistics & A/B Testing

Descriptive Statistics: Know your mean, median, mode, and standard deviation.

Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.

Probability: Understand basic probability and Bayes' theorem.

Intro to ML: Start with linear regression, decision trees, and K-means clustering.

Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.

A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ Learn Python for Data

Data Manipulation: Use pandas for data cleaning and manipulation.

Data Visualization: Explore matplotlib and seaborn for creating visualizations.

Hypothesis Testing: Dive into scipy for statistical testing.

Basic Modeling: Practice building models with scikit-learn.


4️⃣ Develop Product Sense

Product Management Basics: Manage projects and understand the product life cycle.

Data-Driven Strategy: Leverage data to inform decisions and measure success.

Metrics in Business: Define and evaluate metrics that matter to the business.


5️⃣ Hone Soft Skills

Communication: Clearly explain data findings to technical and non-technical audiences.

Collaboration: Work effectively in teams.

Time Management: Prioritize and manage projects efficiently.

Self-Reflection: Regularly assess and improve your skills.


6️⃣ Bonus: Basic Data Engineering

Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.

ETL: Set up extraction jobs, manage dependencies, clean and validate data.

Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.

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Platforms to learn Data Science 👆

𝗧𝗵𝗲 𝟰 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝗯 (𝗘𝘃𝗲𝗻 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲) 💼

Recruiters don’t want to see more certificates—they want proof you can solve real-world problems. That’s where the right projects come in. Not toy datasets, but projects that demonstrate storytelling, problem-solving, and impact.

Here are 4 killer projects that’ll make your portfolio stand out 👇

🔹 1. Exploratory Data Analysis (EDA) on Real-World Dataset

Pick a messy dataset from Kaggle or public sources. Show your thought process.

✅ Clean data using Pandas
✅ Visualize trends with Seaborn/Matplotlib
✅ Share actionable insights with graphs and markdown

Bonus: Turn it into a Jupyter Notebook with detailed storytelling

🔹 2. Predictive Modeling with ML

Solve a real problem using machine learning. For example:

✅ Predict customer churn using Logistic Regression
✅ Predict housing prices with Random Forest or XGBoost
✅ Use scikit-learn for training + evaluation

Bonus: Add SHAP or feature importance to explain predictions

🔹 3. SQL-Powered Business Dashboard

Use real sales or ecommerce data to build a dashboard.

✅ Write complex SQL queries for KPIs
✅ Visualize with Power BI or Tableau
✅ Show trends: Revenue by Region, Product Performance, etc.

Bonus: Add filters & slicers to make it interactive

🔹 4. End-to-End Data Science Pipeline Project

Build a complete pipeline from scratch.

✅ Collect data via web scraping (e.g., IMDb, LinkedIn Jobs)
✅ Clean + Analyze + Model + Deploy
✅ Deploy with Streamlit/Flask + GitHub + Render

Bonus: Add a blog post or LinkedIn write-up explaining your approach

🎯 One solid project > 10 certificates.

Make it visible. Make it valuable. Share it confidently.

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AI Engineer vs Software Engineer 👆

𝟱 𝗖𝗼𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 💻

You don’t need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindset—and these 5 types of challenges are the ones that actually matter.

Here’s what to focus on (with examples) 👇

🔹 1. String Manipulation (Common in Data Cleaning)

✅ Parse messy columns (e.g., split “Name_Age_City”)
✅ Regex to extract phone numbers, emails, URLs
✅ Remove stopwords or HTML tags in text data

Example: Clean up a scraped dataset from LinkedIn bias

🔹 2. GroupBy and Aggregation with Pandas

✅ Group sales data by product/region
✅ Calculate avg, sum, count using .groupby()
✅ Handle missing values smartly

Example: “What’s the top-selling product in each region?”

🔹 3. SQL Join + Window Functions

✅ INNER JOIN, LEFT JOIN to merge tables
✅ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
✅ Use CTEs to break complex queries

Example: “Get 2nd highest salary in each department”

🔹 4. Data Structures: Lists, Dicts, Sets in Python

✅ Use dictionaries to map, filter, and count
✅ Remove duplicates with sets
✅ List comprehensions for clean solutions

Example: “Count frequency of hashtags in tweets”

🔹 5. Basic Algorithms (Not DP or Graphs)

✅ Sliding window for moving averages
✅ Two pointers for duplicate detection
✅ Binary search in sorted arrays

Example: “Detect if a pair of values sum to 100”

🎯 Tip: Practice challenges that feel like real-world data work, not textbook CS exams.

Use platforms like:

StrataScratch
Hackerrank (SQL + Python)
Kaggle Code

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Get File Size using Python 👆

Important data science topics you should definitely be aware of

1. Statistics & Probability

Descriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals

2. Data Manipulation & Analysis

Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation

3. Programming (Python/R)

Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)

4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly

5. Machine Learning

Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN

Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering

Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search

6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras

7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization

8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)

9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring

10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies

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Like for the detailed explanation on each topic 😄👍

🌮 Data Analyst Vs Data Engineer Vs Data Scientist 🌮


Skills required to become data analyst
👉 Advanced Excel, Oracle/SQL
👉 Python/R

Skills required to become data engineer
👉 Python/ Java.
👉 SQL, NoSQL technologies like Cassandra or MongoDB
👉 Big data technologies like Hadoop, Hive/ Pig/ Spark

Skills required to become data Scientist
👉 In-depth knowledge of tools like R/ Python/ SAS.
👉 Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
👉 SQL and NoSQL

Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics

Today, lets understand Machine Learning in simplest way possible

What is Machine Learning?

Think of it like this:

Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.

Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.

The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”

Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.

That’s what machine learning does — but instead of a kid, it's a computer.

In Tech Terms (Still Simple):

You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.

Later, when you give it new data, it makes a smart guess.

Few Common Uses of ML You See Every Day:

Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.

Should we start covering all data Science and machine learning concepts like this?

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Machine Learning Types 👆

So now that you know what machine learning is (teaching computers to learn from data), the next thing is.

How do they learn?

That’s where algorithms come in.
Think of algorithms as different learning styles.

Just like people — some learn best by watching videos, others by solving problems — computers have different ways to learn too. These different ways are what we call machine learning algorithms.

Let’s start with the most common and simple ones.

I’ll explain them one by one in a way that makes sense.

Here’s a quick list of popular ML algorithms:
Linear Regression – predicts numbers (like house prices).
Logistic Regression – predicts categories (yes/no, spam/not spam).
Decision Trees – makes decisions by asking questions.
Random Forest – a group of decision trees working together.
K-Nearest Neighbors (KNN) – looks at neighbors to decide.
Support Vector Machine (SVM) – draws lines to separate data.
Naive Bayes – based on probability, good for text (like spam filters).
K-Means Clustering – groups similar things together.
Principal Component Analysis (PCA) – reduces complexity of data.
Neural Networks – the backbone of deep learning (used in face recognition, voice assistants, etc.).

Wanna need a detailed explanation on each algorithm?

React with ♥️ and let me know in the comments if you really want to learn more about the algorithms.

You can now find Data Science & Machine Learning resources on WhatsApp as well: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Now let's understand Linear Regression in detail.

Linear Regression is all about predicting a continuous value (like salary, price, temperature) based on another variable (like years of experience, number of products sold, etc.).

Let's say, You’re trying to predict someone's salary based on their years of experience. As experience increases, you generally expect the salary to increase too. What linear regression does is find the best line that fits this trend.

The line is represented by this simple equation:

Salary = m * Years of Experience + b

Here:
m is the slope of the line (it tells you how much salary increases with each additional year of experience).
b is the y-intercept (the starting point, or the salary when there's no experience).

The Process:

Training the model: The algorithm looks at all your data and tries to draw the straightest line possible that fits the pattern between experience and salary. It does this by adjusting the m (slope) and b (intercept) to minimize the difference between predicted and actual salaries.

Making predictions: Once the model has learned the best line, it can predict salaries for new people based on their years of experience. For example, if you tell it someone has 5 years of experience, it will give you the predicted salary.

Linear regression is great when there's a straight-line relationship between variables. It helps you make predictions, and because it’s simple, it’s often used as a starting point for many problems.

React with ♥️ if you need similar explanation for the rest of the algorithms

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Top Machine Learning Libraries 👆