Top Tweets for #30daysofDataScience
For 3 years now, I have been downloading files from the internet and saving them to my PC directly. With time, my Downloads folder was very disorganized.😭
While doing #30DaysofDataScience, I made a organizer file which has cleaned my Downloads folder.
I'm happy now 🩷
Day 15&16 of #30DaysOfDataScience
I noticed I was always struggling with my visualization and knowing when to use which. So, I went over to YouTube to make them stick better(see below)
Day 13&14 of #30DaysOfDataScience
📍Started module 7 on A/B testing.
📍Revisited null hypothesis and alternative hypothesis.
I can’t believe the number of statistics concepts that get thrown at you while learning ML😅
One more module to go.🥹🎉
Day 12 of #30DaysOfDataScience.
📍Completed module 6, the project was a way to introduce unsupervised learning.
📍Understood concepts like inertia and silhouette score and the mathematics behind them.
📍Learned the use of PCA in dimensionality reduction.
I’m back 🙈
I had a backlog of tasks to complete in June, so I had to slow down my learning and even pause the challenge.
I’ll be resuming fully from today.
Happy new month🎉
Day 10&11 of #30DaysOfDataScience
I’m still on the same module/project about predicting bankruptcy
📍Over the weekend, I read more on random-forest and hyper-parameter tuning using grid search.
📍Learned about gradient boosting, precision and recall.
Day9 of #30DaysOfDataScience
📍Continued the course but I had a hard time understanding cross-validation& gridsearchCV so I referenced external resources for clarity.
I’d appreciate recommendations for resources that explain the theory behind ML concepts.
Day8 of #30DaysOfDataScience
📍Learned to use context handler to load json files(both compressed/non-compressed) into a data frame
📍Addressed an imbalanced dataset using resampling(see explanation below)
📍Used pickle to save our trained model to a file
Day6&7 of #30daysofDataScience
📍Completed the classification task previously mentioned.
The goal of the task was to predict the top 3 villages (out of 50) that require solar panels the most, based on certain features.
📍Began Module5 lectures on @worldquantu
Day6&7 of #30daysofDataScience
📍Completed the classification task previously mentioned.
The goal of the task was to predict the top 3 villages (out of 50) that require solar panels the most, based on certain features.
📍Began Module5 lectures on @worldquantu
Day5 of #30DaysOfDataScience
I found and read this well-detailed article on linear regression: https://t.co/FegTGVjP3V
That’s pretty much it😀
Day5 of #30DaysOfDataScience
I found and read this well-detailed article on linear regression: https://t.co/FegTGVjP3V
That’s pretty much it😀
Day4 of #30DaysOfDataScience
I’m currently working on a classification task with a clean dataset(no missing values, outliers)
I also had to revisit the differences between MinMaxScaler and StandardScaler and learned new feature engineering tips.
Day4 of #30DaysOfDataScience
I’m currently working on a classification task with a clean dataset(no missing values, outliers)
I also had to revisit the differences between MinMaxScaler and StandardScaler and learned new feature engineering tips.
Day3 of #30DaysOfDataScience
I couldn’t post yesterday due to network issues, but I’m finally done with module4😁.
The goal of this project was to predict if a building would suffer severe earthquake damages.
👇See thread for what I learned
Day3 of #30DaysOfDataScience
I couldn’t post yesterday due to network issues, but I’m finally done with module4😁.
The goal of this project was to predict if a building would suffer severe earthquake damages.
👇See thread for what I learned
Day2 of #30DaysOfDataScience
I’m still on module 4 on WQU’s data science lab.
Today’s lesson was all about decision trees and building decision tree models to classify earthquake damage.
Day2 of #30DaysOfDataScience
I’m still on module 4 on WQU’s data science lab.
Today’s lesson was all about decision trees and building decision tree models to classify earthquake damage.
Day1 of #30DaysOfDataScience
I’m currently taking the Data Science Lab course by @worldquantu
Today, I:
📌Learned to load SQL data into pandas
📌Understood the different SQL joins(explanation below)
📌Built a logistic Reg model to predict earthquake damage
Day1 of #30DaysOfDataScience
I’m currently taking the Data Science Lab course by @worldquantu
Today, I:
📌Learned to load SQL data into pandas
📌Understood the different SQL joins(explanation below)
📌Built a logistic Reg model to predict earthquake damage
Starting today, I’m taking on a 30days of Data science challenge.
For the next 30days, I’ll
📍Learn and solidify my DS knowledge.
📍Build projects.
📍Transition into learning ML.
I’ll be sharing my journey and what I learn here.
This should be fun😄
#30DaysOfDataScience
Starting today, I’m taking on a 30days of Data science challenge.
For the next 30days, I’ll
📍Learn and solidify my DS knowledge.
📍Build projects.
📍Transition into learning ML.
I’ll be sharing my journey and what I learn here.
This should be fun😄
#30DaysOfDataScience
Day 36/30 of #30DaysOfDataScience
With about 10% of the new dataset collected, I've started combining the files and performing some initial data cleaning and feature engineering. The scraping for this dataset continues in the background.
Day 35/30 of #30DaysOfDataScience
I have written my first medium article on 'Evaluating an Estimator (Bias and Variance)' and continued making my digital note. Also, the scraping for the new dataset continues in the background.
Link: https://t.co/P7aqzYGfkM
Going to start with #30DaysOfDataScience today💫
I've almost completed Andrew Ng's ML course, learned NumPy, Pandas, and Matplotlib basics. I've also done 4 Kaggle notebooks and 3 datasets. Drop down some resources from where you have learnt data science.
Day 34/30 of #30DaysOfDataScience
I have started reading different articles and documentation on ML and making a digital note of it which I will share once it is completed and also, the scraping for the new dataset continues in the background.
Going to start with #30DaysOfDataScience today💫
I've almost completed Andrew Ng's ML course, learned NumPy, Pandas, and Matplotlib basics. I've also done 4 Kaggle notebooks and 3 datasets. Drop down some resources from where you have learnt data science.
Day 33/30 of #30DaysOfDataScience
I have started reading different articles on ML and making a digital note of it which I will share once it is completed. I tackled 1 LC problem and also, the scraping for the new dataset continues in the background.
Going to start with #30DaysOfDataScience today💫
I've almost completed Andrew Ng's ML course, learned NumPy, Pandas, and Matplotlib basics. I've also done 4 Kaggle notebooks and 3 datasets. Drop down some resources from where you have learnt data science.
Day 32/30 of #30DaysOfDataScience
I have finished working on the New York City Taxi Fare Prediction dataset on Kaggle and tackled 1 LC problem. Also, the scraping for the new dataset continues in the background.
Going to start with #30DaysOfDataScience today💫
I've almost completed Andrew Ng's ML course, learned NumPy, Pandas, and Matplotlib basics. I've also done 4 Kaggle notebooks and 3 datasets. Drop down some resources from where you have learnt data science.
Day 31/30 of #30DaysOfDataScience
I have continued working on the New York City Taxi Fare Prediction dataset on Kaggle. Also, the scraping for the new dataset continues in the background.
Day 30/30 of #30DaysOfDataScience
I have continued working on the New York City Taxi Fare Prediction dataset on Kaggle and tackled 1 LC problem. Also, the scraping for the new dataset continues in the background.
Going to start with #30DaysOfDataScience today💫
I've almost completed Andrew Ng's ML course, learned NumPy, Pandas, and Matplotlib basics. I've also done 4 Kaggle notebooks and 3 datasets. Drop down some resources from where you have learnt data science.
Day 29/30 of #30DaysOfDataScience
I have started working on the New York City Taxi Fare Prediction dataset on Kaggle. Also, the scraping for the new dataset continues in the background.
Going to start with #30DaysOfDataScience today💫
I've almost completed Andrew Ng's ML course, learned NumPy, Pandas, and Matplotlib basics. I've also done 4 Kaggle notebooks and 3 datasets. Drop down some resources from where you have learnt data science.
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