Top 10 Data Science Concepts You Should Know ๐ง
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
โค4๐1
๐ Data Science Project Ideas to Practice & Master Your Skills โ
๐ข Beginner Level
โข Titanic Survival Prediction (Logistic Regression)
โข House Price Prediction (Linear Regression)
โข Exploratory Data Analysis on IPL or Netflix Dataset
โข Customer Segmentation (K-Means Clustering)
โข Weather Data Visualization
๐ก Intermediate Level
โข Sentiment Analysis on Tweets
โข Credit Card Fraud Detection
โข Time Series Forecasting (Stock or Sales Data)
โข Image Classification using CNN (Fashion MNIST)
โข Recommendation System for Movies/Products
๐ด Advanced Level
โข End-to-End Machine Learning Pipeline with Deployment
โข NLP Chatbot using Transformers
โข Real-Time Dashboard with Streamlit + ML
โข Anomaly Detection in Network Traffic
โข A/B Testing & Business Decision Modeling
๐ฌ Double Tap โค๏ธ for more! ๐ค๐
๐ข Beginner Level
โข Titanic Survival Prediction (Logistic Regression)
โข House Price Prediction (Linear Regression)
โข Exploratory Data Analysis on IPL or Netflix Dataset
โข Customer Segmentation (K-Means Clustering)
โข Weather Data Visualization
๐ก Intermediate Level
โข Sentiment Analysis on Tweets
โข Credit Card Fraud Detection
โข Time Series Forecasting (Stock or Sales Data)
โข Image Classification using CNN (Fashion MNIST)
โข Recommendation System for Movies/Products
๐ด Advanced Level
โข End-to-End Machine Learning Pipeline with Deployment
โข NLP Chatbot using Transformers
โข Real-Time Dashboard with Streamlit + ML
โข Anomaly Detection in Network Traffic
โข A/B Testing & Business Decision Modeling
๐ฌ Double Tap โค๏ธ for more! ๐ค๐
โค5
COMMON TERMINOLOGIES IN PYTHON - PART 1
Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?
In this series, we would be looking at the common Terminologies in python.
It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:
IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.
Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately
System Python - This is the version of python that comes with your operating system
Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions
REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)
Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.
Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function
Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.
Note: A return value can be any of these variable types: handle, integer, object, or string
Script - This is a file where you store your python code in a text file and execute all of the code with a single command
Script files - this is a file containing a group of python scripts
Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?
In this series, we would be looking at the common Terminologies in python.
It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:
IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.
Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately
System Python - This is the version of python that comes with your operating system
Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions
REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)
Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.
Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function
Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.
Note: A return value can be any of these variable types: handle, integer, object, or string
Script - This is a file where you store your python code in a text file and execute all of the code with a single command
Script files - this is a file containing a group of python scripts
โค4
Being a Generalist Data Scientist won't get you hired.
Here is how you can specialize ๐
Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.
To discover what you enjoy the most, try answering different questions for each DS role:
- ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ
Qs:
โHow should we monitor model performance in production?โ
- ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ / ๐๐ซ๐จ๐๐ฎ๐๐ญ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
Qs:
โHow can we visualize customer segmentation to highlight key demographics?โ
- ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
Qs:
โHow can we use clustering to identify new customer segments for targeted marketing?โ
- ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐๐ฌ๐๐๐ซ๐๐ก๐๐ซ
Qs:
โWhat novel architectures can we explore to improve model robustness?โ
- ๐๐๐๐ฉ๐ฌ ๐๐ง๐ ๐ข๐ง๐๐๐ซ
Qs:
โHow can we automate the deployment of machine learning models to ensure continuous integration and delivery?โ
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
Here is how you can specialize ๐
Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.
To discover what you enjoy the most, try answering different questions for each DS role:
- ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ
Qs:
โHow should we monitor model performance in production?โ
- ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ / ๐๐ซ๐จ๐๐ฎ๐๐ญ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
Qs:
โHow can we visualize customer segmentation to highlight key demographics?โ
- ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
Qs:
โHow can we use clustering to identify new customer segments for targeted marketing?โ
- ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐๐ฌ๐๐๐ซ๐๐ก๐๐ซ
Qs:
โWhat novel architectures can we explore to improve model robustness?โ
- ๐๐๐๐ฉ๐ฌ ๐๐ง๐ ๐ข๐ง๐๐๐ซ
Qs:
โHow can we automate the deployment of machine learning models to ensure continuous integration and delivery?โ
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
โค7
What are the differences between a Power BI dataset, a Report, and a Dashboard?
In Power BI:
1. Dataset: It's where your raw data resides. Think of it as your data source. You import or connect to data, transform it, and then store it in a dataset within Power BI.
2. Report: Reports visualize data from your dataset. They consist of visuals like charts, graphs, tables, etc., created using the data in your dataset. Reports allow you to explore and analyze your data in depth.
3. Dashboard: Dashboards are a collection of visuals from one or more reports, designed to give a snapshot view of your data. They provide a high-level overview of key metrics and trends. You can pin visuals from different reports onto a dashboard to create a unified view.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
In Power BI:
1. Dataset: It's where your raw data resides. Think of it as your data source. You import or connect to data, transform it, and then store it in a dataset within Power BI.
2. Report: Reports visualize data from your dataset. They consist of visuals like charts, graphs, tables, etc., created using the data in your dataset. Reports allow you to explore and analyze your data in depth.
3. Dashboard: Dashboards are a collection of visuals from one or more reports, designed to give a snapshot view of your data. They provide a high-level overview of key metrics and trends. You can pin visuals from different reports onto a dashboard to create a unified view.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
โค5๐2