Which of the following is NOT one of the 5 Vs of Big Data?
Anonymous Quiz
8%
A) Volume
19%
B) Velocity
9%
C) Variety
64%
D) Version
❤2
Which Apache Hadoop component is responsible for storing data?
Anonymous Quiz
13%
A) YARN
28%
B) MapReduce
46%
C) HDFS
13%
D) Hive
❤1
Which Big Data framework is known for fast, in-memory processing?
Anonymous Quiz
27%
A) Apache Hadoop
53%
B) Apache Spark
13%
C) MySQL
7%
D) PostgreSQL
❤1
Which of the following is an example of Big Data?
Anonymous Quiz
2%
A) A small list of employee names in a spreadsheet
94%
B) Millions of social media posts generated every day
2%
C) A handwritten notebook
2%
D) A single text file with 10 records
❤1
What is the main advantage of Apache Spark over Hadoop MapReduce?
Anonymous Quiz
4%
A) It requires more hardware
5%
B) It only supports Java
90%
C) It performs in-memory processing, making it much faster
1%
D) It cannot process large datasets
❤1
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❤1
📊 Data Science Roadmap 🚀
📂 Start Here
∟📂 What is Data Science & Why It Matters?
∟📂 Roles (Data Analyst, Data Scientist, ML Engineer)
∟📂 Setting Up Environment (Python, Jupyter Notebook)
📂 Python for Data Science
∟📂 Python Basics (Variables, Loops, Functions)
∟📂 NumPy for Numerical Computing
∟📂 Pandas for Data Analysis
📂 Data Cleaning & Preparation
∟📂 Handling Missing Values
∟📂 Data Transformation
∟📂 Feature Engineering
📂 Exploratory Data Analysis (EDA)
∟📂 Descriptive Statistics
∟📂 Data Visualization (Matplotlib, Seaborn)
∟📂 Finding Patterns & Insights
📂 Statistics & Probability
∟📂 Mean, Median, Mode, Variance
∟📂 Probability Basics
∟📂 Hypothesis Testing
📂 Machine Learning Basics
∟📂 Supervised Learning (Regression, Classification)
∟📂 Unsupervised Learning (Clustering)
∟📂 Model Evaluation (Accuracy, Precision, Recall)
📂 Machine Learning Algorithms
∟📂 Linear Regression
∟📂 Decision Trees & Random Forest
∟📂 K-Means Clustering
📂 Model Building & Deployment
∟📂 Train-Test Split
∟📂 Cross Validation
∟📂 Deploy Models (Flask / FastAPI)
📂 Big Data & Tools
∟📂 SQL for Data Handling
∟📂 Introduction to Big Data (Hadoop, Spark)
∟📂 Version Control (Git & GitHub)
📂 Practice Projects
∟📌 House Price Prediction
∟📌 Customer Segmentation
∟📌 Sales Forecasting Model
📂 ✅ Move to Next Level
∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch)
∟📂 NLP (Text Analysis, Chatbots)
∟📂 MLOps & Model Optimization
Data Science Resources: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
React "❤️" for more! 🚀📊
📂 Start Here
∟📂 What is Data Science & Why It Matters?
∟📂 Roles (Data Analyst, Data Scientist, ML Engineer)
∟📂 Setting Up Environment (Python, Jupyter Notebook)
📂 Python for Data Science
∟📂 Python Basics (Variables, Loops, Functions)
∟📂 NumPy for Numerical Computing
∟📂 Pandas for Data Analysis
📂 Data Cleaning & Preparation
∟📂 Handling Missing Values
∟📂 Data Transformation
∟📂 Feature Engineering
📂 Exploratory Data Analysis (EDA)
∟📂 Descriptive Statistics
∟📂 Data Visualization (Matplotlib, Seaborn)
∟📂 Finding Patterns & Insights
📂 Statistics & Probability
∟📂 Mean, Median, Mode, Variance
∟📂 Probability Basics
∟📂 Hypothesis Testing
📂 Machine Learning Basics
∟📂 Supervised Learning (Regression, Classification)
∟📂 Unsupervised Learning (Clustering)
∟📂 Model Evaluation (Accuracy, Precision, Recall)
📂 Machine Learning Algorithms
∟📂 Linear Regression
∟📂 Decision Trees & Random Forest
∟📂 K-Means Clustering
📂 Model Building & Deployment
∟📂 Train-Test Split
∟📂 Cross Validation
∟📂 Deploy Models (Flask / FastAPI)
📂 Big Data & Tools
∟📂 SQL for Data Handling
∟📂 Introduction to Big Data (Hadoop, Spark)
∟📂 Version Control (Git & GitHub)
📂 Practice Projects
∟📌 House Price Prediction
∟📌 Customer Segmentation
∟📌 Sales Forecasting Model
📂 ✅ Move to Next Level
∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch)
∟📂 NLP (Text Analysis, Chatbots)
∟📂 MLOps & Model Optimization
Data Science Resources: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
React "❤️" for more! 🚀📊
❤8
☁️ 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗔𝗪𝗦 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 | 𝗙𝗥𝗘𝗘 𝗔𝗪𝗦 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀🚀
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❤1
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🤖 Artificial Intelligence
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❤1
You're an upcoming data scientist?
This is for you.
The key to success isn't hoarding every tutorial and course.
It's about taking that first, decisive step.
Start small. Start now.
I remember feeling paralyzed by options:
Coursera, Udacity, bootcamps, blogs...
Where to begin?
Then my mentor gave me one piece of advice:
"Stop planning. Start doing.
Pick the shortest video you can find.
Watch it. Now."
It was tough love, but it worked.
I chose a 3-minute intro to pandas.
Then a quick matplotlib demo.
Suddenly, I was building momentum.
Each bite-sized lesson built my confidence.
Every "I did it!" moment sparked joy.
I was no longer overwhelmed—I was excited.
So here's my advice for you:
1. Find a 5-minute data science video. Any topic.
2. Watch it before you finish your coffee.
3. Do one thing you learned. Anything.
Remember:
A messy start beats a perfect plan
Every. Single. Time.
This is for you.
The key to success isn't hoarding every tutorial and course.
It's about taking that first, decisive step.
Start small. Start now.
I remember feeling paralyzed by options:
Coursera, Udacity, bootcamps, blogs...
Where to begin?
Then my mentor gave me one piece of advice:
"Stop planning. Start doing.
Pick the shortest video you can find.
Watch it. Now."
It was tough love, but it worked.
I chose a 3-minute intro to pandas.
Then a quick matplotlib demo.
Suddenly, I was building momentum.
Each bite-sized lesson built my confidence.
Every "I did it!" moment sparked joy.
I was no longer overwhelmed—I was excited.
So here's my advice for you:
1. Find a 5-minute data science video. Any topic.
2. Watch it before you finish your coffee.
3. Do one thing you learned. Anything.
Remember:
A messy start beats a perfect plan
Every. Single. Time.
❤10