Complete roadmap to learn data science in 2024 👇👇
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
👍8❤2🥰2
Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.
◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
🗓️Week 2: Intermediate Data Analytics Skills
◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
🗓️Week 3: Advanced Techniques and Tools
◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
🗓️Week 4: Projects and Practice
◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.
◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
🗓️Week 2: Intermediate Data Analytics Skills
◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
🗓️Week 3: Advanced Techniques and Tools
◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
🗓️Week 4: Projects and Practice
◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
👍10❤5
A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
👍4❤1
You can use ChatGPT to make money online.
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt→ "
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt→ "
3. Ghostwrite eBooks:
Use ChatGPT to write eBooks on different topics for online sale.
Prompt→ "
4. Compose Music Reviews or Critiques:
Use ChatGPT to write detailed reviews of music, albums, and artists.
Prompt: "
5. Develop Mobile App Content:
Use ChatGPT to create mobile app content like denoscriptions, guides, and FAQs.
Prompt: "
6. Create Resume Templates:
Use ChatGPT to create diverse resume templates for various jobs.
Prompt→ "I want to offer a range of professional resume templates on my website. Can you help me create five different templates, each tailored to a specific career field like IT, healthcare, and marketing?"
7. Write Travel Guides:
Use ChatGPT to write travel guides with tips and itineraries for different places.
Prompt→ "I'm creating a travel blog about European cities. Can you help me write a comprehensive guide for first-time visitors to Paris, including must-see sights, local dining recommendations, and travel tips?"
8. Draft Legal Documents:
Use ChatGPT to write basic legal documents like contracts and terms of service.
Prompt→ "I need to draft a terms of service document for my new e-commerce website. Can you help me create a draft that covers all necessary legal points in clear language?"
9. Write Video Game Reviews:
Use ChatGPT to write engaging video game reviews, covering gameplay and graphics.
Prompt→ "I run a gaming blog. Can you help me write a detailed review of the latest [Game Title], focusing on its gameplay mechanics, storyline, and graphics quality?"
10. Develop Personal Branding Materials:
Use ChatGPT to help build a personal branding package, including bios, LinkedIn profiles, and website content.
Prompt→ "I'm a freelance graphic designer looking to strengthen my personal brand. Can you help me write a compelling biography, update my LinkedIn profile, and create content for my portfolio website?"
ENJOY LEARNING 👍👍
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt→ "
I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"2. Create Online Course Material:
Make detailed and educational online course content.
Prompt→ "
I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"3. Ghostwrite eBooks:
Use ChatGPT to write eBooks on different topics for online sale.
Prompt→ "
I want to publish an eBook about healthy eating habits. Can you help me outline and ghostwrite the chapters, focusing on practical tips and easy recipes?"4. Compose Music Reviews or Critiques:
Use ChatGPT to write detailed reviews of music, albums, and artists.
Prompt: "
I run a music review blog. Can you help me write a detailed review of the latest album by [Artist Name], focusing on their musical style, lyrics, and overall impact?"5. Develop Mobile App Content:
Use ChatGPT to create mobile app content like denoscriptions, guides, and FAQs.
Prompt: "
I'm developing a fitness app and need help writing the app denoscription for the store, user instructions, and a list of frequently asked questions."6. Create Resume Templates:
Use ChatGPT to create diverse resume templates for various jobs.
Prompt→ "I want to offer a range of professional resume templates on my website. Can you help me create five different templates, each tailored to a specific career field like IT, healthcare, and marketing?"
7. Write Travel Guides:
Use ChatGPT to write travel guides with tips and itineraries for different places.
Prompt→ "I'm creating a travel blog about European cities. Can you help me write a comprehensive guide for first-time visitors to Paris, including must-see sights, local dining recommendations, and travel tips?"
8. Draft Legal Documents:
Use ChatGPT to write basic legal documents like contracts and terms of service.
Prompt→ "I need to draft a terms of service document for my new e-commerce website. Can you help me create a draft that covers all necessary legal points in clear language?"
9. Write Video Game Reviews:
Use ChatGPT to write engaging video game reviews, covering gameplay and graphics.
Prompt→ "I run a gaming blog. Can you help me write a detailed review of the latest [Game Title], focusing on its gameplay mechanics, storyline, and graphics quality?"
10. Develop Personal Branding Materials:
Use ChatGPT to help build a personal branding package, including bios, LinkedIn profiles, and website content.
Prompt→ "I'm a freelance graphic designer looking to strengthen my personal brand. Can you help me write a compelling biography, update my LinkedIn profile, and create content for my portfolio website?"
ENJOY LEARNING 👍👍
👍8❤2
10 creative ways to use ChatGPT to learn data science from scratch
1. Understand Core Data Science Concepts
Break down complex data science topics into simple explanations.
Prompt →
"I'm new to data science. Can you explain core concepts like data cleaning, feature engineering, and model evaluation in a beginner-friendly way?"
2. Create a Personalized Study Plan
Plan your data science learning journey with a tailored schedule.
Prompt →
"I want to master data science in 6 months while dedicating 2 hours daily. Can you create a detailed weekly study plan with resources for Python, statistics, and machine learning?"
3. Generate Coding Exercises and Solutions
Practice coding with real-world datasets and scenarios.
Prompt →
"Can you provide 10 hands-on coding exercises in Python for data cleaning and visualization, with step-by-step solutions?"
4. Simplify Machine Learning Algorithms
Learn how machine learning algorithms work with relatable analogies.
Prompt →
"Can you explain how decision trees and random forests work using a real-life analogy, like planning a family vacation?"
5. Analyze Real-World Datasets
Practice working with datasets to build skills.
Prompt →
"Can you guide me through analyzing a real-world dataset, like predicting house prices, using Python step by step?"
6. Build a Portfolio Project
Create impactful projects to showcase your skills.
Prompt →
"I want to build a data science portfolio project on customer churn prediction. Can you help me outline the steps, tools, and methods to use?"
7. Mock Data Science Interviews
Prepare for interviews with tailored questions and answers.
Prompt →
"Can you simulate a mock interview for a data science role, focusing on Python, SQL, and machine learning questions?"
8. Write Blogs or Articles on Data Science
Share knowledge by writing educational content.
Prompt →
"I want to write a blog post about the importance of feature scaling in machine learning. Can you help me draft an engaging and informative article?"
9. Visualize Data Better
Learn to create compelling data visualizations.
Prompt →
"Can you guide me on how to use Matplotlib and Seaborn to create a dashboard-like visualization for sales data?"
10. Stay Updated with the Latest Trends
Get concise summaries of the latest research and tools in data science.
Prompt →
"What are the top 5 emerging trends or tools in data science that I should explore to stay ahead in 2025?"
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
#chatgptprompts
1. Understand Core Data Science Concepts
Break down complex data science topics into simple explanations.
Prompt →
"I'm new to data science. Can you explain core concepts like data cleaning, feature engineering, and model evaluation in a beginner-friendly way?"
2. Create a Personalized Study Plan
Plan your data science learning journey with a tailored schedule.
Prompt →
"I want to master data science in 6 months while dedicating 2 hours daily. Can you create a detailed weekly study plan with resources for Python, statistics, and machine learning?"
3. Generate Coding Exercises and Solutions
Practice coding with real-world datasets and scenarios.
Prompt →
"Can you provide 10 hands-on coding exercises in Python for data cleaning and visualization, with step-by-step solutions?"
4. Simplify Machine Learning Algorithms
Learn how machine learning algorithms work with relatable analogies.
Prompt →
"Can you explain how decision trees and random forests work using a real-life analogy, like planning a family vacation?"
5. Analyze Real-World Datasets
Practice working with datasets to build skills.
Prompt →
"Can you guide me through analyzing a real-world dataset, like predicting house prices, using Python step by step?"
6. Build a Portfolio Project
Create impactful projects to showcase your skills.
Prompt →
"I want to build a data science portfolio project on customer churn prediction. Can you help me outline the steps, tools, and methods to use?"
7. Mock Data Science Interviews
Prepare for interviews with tailored questions and answers.
Prompt →
"Can you simulate a mock interview for a data science role, focusing on Python, SQL, and machine learning questions?"
8. Write Blogs or Articles on Data Science
Share knowledge by writing educational content.
Prompt →
"I want to write a blog post about the importance of feature scaling in machine learning. Can you help me draft an engaging and informative article?"
9. Visualize Data Better
Learn to create compelling data visualizations.
Prompt →
"Can you guide me on how to use Matplotlib and Seaborn to create a dashboard-like visualization for sales data?"
10. Stay Updated with the Latest Trends
Get concise summaries of the latest research and tools in data science.
Prompt →
"What are the top 5 emerging trends or tools in data science that I should explore to stay ahead in 2025?"
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
#chatgptprompts
👍7❤1🥰1
Various types of test used in statistics for data science
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
👍6
Essential Tools and Libraries for Data Science Students
1. Programming Languages:
Python
R
SQL
2. Python Libraries:
NumPy: For numerical computations.
Pandas: For data manipulation and analysis.
Matplotlib: For basic data visualization.
Seaborn: For statistical data visualization.
Scikit-learn: For machine learning models.
TensorFlow: For deep learning.
PyTorch: For advanced neural networks.
3. R Libraries:
ggplot2: For data visualization.
dplyr: For data manipulation.
caret: For machine learning.
shiny: For building interactive web apps.
4. Data Visualization Tools:
Tableau
Power BI
Google Data Studio
5. Big Data Tools:
Apache Hadoop
Apache Spark
6. Cloud Platforms:
AWS (Amazon Web Services)
Google Cloud Platform (GCP)
Microsoft Azure
7. Statistical Software:
SAS
SPSS
8. Version Control System:
Git
9. Notebook Tools:
Jupyter Notebook
Google Colab
10. Data Sources for Practice:
Kaggle Datasets
UCI Machine Learning Repository
GitHub Repositories
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
1. Programming Languages:
Python
R
SQL
2. Python Libraries:
NumPy: For numerical computations.
Pandas: For data manipulation and analysis.
Matplotlib: For basic data visualization.
Seaborn: For statistical data visualization.
Scikit-learn: For machine learning models.
TensorFlow: For deep learning.
PyTorch: For advanced neural networks.
3. R Libraries:
ggplot2: For data visualization.
dplyr: For data manipulation.
caret: For machine learning.
shiny: For building interactive web apps.
4. Data Visualization Tools:
Tableau
Power BI
Google Data Studio
5. Big Data Tools:
Apache Hadoop
Apache Spark
6. Cloud Platforms:
AWS (Amazon Web Services)
Google Cloud Platform (GCP)
Microsoft Azure
7. Statistical Software:
SAS
SPSS
8. Version Control System:
Git
9. Notebook Tools:
Jupyter Notebook
Google Colab
10. Data Sources for Practice:
Kaggle Datasets
UCI Machine Learning Repository
GitHub Repositories
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍8❤2👏1
7 Free Kaggle Micro-Courses for Data Science Beginners with Certification
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
#datascienceprojects #kaggle
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
#datascienceprojects #kaggle
👍6❤2
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
👍8❤5