Tweet Teratas untuk #100DaysofDataScience
Day 15 of #100DaysOfDataScience
Started learning Statistics today.
The deeper I go into Data Science,
the more I realize:
Machine Learning is mostly about understanding data properly.
Covered:
Variance
Standard Deviation
Z-Score
Probability
Outliers
Strong fundamentals first.
Day14 #100DaysOfDataScience π
13 days of consistency.
Learned:
β’Python,NumPy,Pandas
β’Matplotlib,Seaborn,Plotly
β’EDA & Data Visualization
β’Built IPL & Netflix projects
β’Started my first Data Science internship
Next:
Statistics
MachineLearning
Consistency > Motivation.
Day 99/100
Applied data visualization on real datasets today
Bringing everything together, models, DAX & dashboards
Data - insights - decisions
#100DaysOfDataScience #PowerBI #DataAnalytics
Day 96/100
Continued DAX today
Worked on mathematical calculations & core operations
Turning numbers into insights
#100DaysOfDataScience #PowerBI #DAX
Day 88/100
Worked with Date & Time in Power BI π
Extracted day, week, quarter & explored fuzzy matching
Time-based data unlocks deeper insights π
#100DaysOfDataScience #PowerBI #DataAnalytics
Day 25 of #100DaysOfDataScience π
Student Performance Analysis using K-Means
Data preprocessing & feature selection
Feature scaling
Applied K-Means clustering
Identified 3 groups: Struggling, Average & Top Performers
Cluster analysis & insights
#DataScience #MachineLearning #AI
Day 24 of #100DaysOfDataScience π
Started Diabetes Prediction using Logistic Regression π©Ί
Data cleaning & preprocessing
Feature scaling
Model training & evaluation
Saved model using joblib
#DataScience #MachineLearning #Python #LogisticRegression #AI
Day 80/100
Milestone Day, 20 days to goππ
Started data transformation in Power BI
Clean data = better insights
Power Query is powerful β‘
#100DaysOfDataScience #PowerBI #DataAnalytics
Day 20 of #100DaysOfDataScience οΏ½οΏ½οΏ½
Built Complete Logistic Regression model on Titanic dataset π’
Model training & prediction
Saved & loaded model using joblib
Evaluated using accuracy & confusion matrix
#DataScience #MachineLearning #Python #LogisticRegression #AI
Day 75/100
Started Power BI today π
Moving from SQL to data visualization & dashboards.
New phase, same consistency πͺ
#100DaysOfDataScience #PowerBI #DataAnalytics
Day 19 of #100DaysOfDataScience π
Started Titanic Survival Prediction using Logistic Regression π’
Data preprocessing
Handled missing values
Preparing dataset for model
#DataScience #MachineLearning #Python #LogisticRegression
Day 18 of #100DaysOfDataScience π
Visualized Linear Regression model performance π
Check out the code execution and output in the video π
#DataScience #MachineLearning #Python #LinearRegression #AI #CodingJourney
GitHub : https://t.co/27SP2zkV0F
Day 18 of #100DaysOfDataScience π
Analyzed Linear Regression model performance π
Checked feature coefficients
Compared actual vs predicted values
Visualized results using scatter plot
#DataScience #MachineLearning #Python #LinearRegression #AI
Day 71/100
Focused on INNER JOIN today.
Learned how to combine tables and return only matching records.
Getting better at working with relational data.
#100DaysOfDataScience #SQL #DataAnalytics
Day 16 of #100DaysOfDataScience π
Worked on an Advertising dataset and built a Linear Regression model to predict sales. π
Cleaned dataset
Checked missing values
Analyzed statistical summary
Explored correlation between features
#DataScience #ML #Python #LinearRegression
Day 67/100
Practiced SQL multi-table queries using the Ecommerce dataset.
Goal: Get customer info, Count total orders & Calculate total amount.
Used: LEFT JOIN, COUNT & SUM, Arithmetic operations, GROUP BY & ORDER BY
Turning raw data into insights.
#100DaysOfDataScience #SQL
Day 66/100
Practiced SQL joins across multiple tables.
Goal:
- Get order details
- Add product name
- Add category name
Used LEFT JOIN to connect order_details, products and categories.
Learning how to structure multi-table queries well.
#100DaysOfDataScience #SQL #DataAnalytics
Day 60/100
Continued practicing SQL single-table queries using the Ecommerce dataset.
Today I worked with: HAVING clause, CASE with single conditions, CASE with multiple conditions.
Learning how to do better filtering to SQL queries.
#100DaysOfDataScience #SQL #DataAnalytics
Day 53/100 β SQL: Querying Single Tables
Today I learned how to query data from a single table in SQL.
Focused on:
β’ Retrieving specific columns
β’ Filtering data
β’ Understanding table structure
Building a stronger SQL foundation.
#100DaysOfDataScience #SQL #DataAnalytics
Day 50/100 β Introduction to SQL
Major Milestoneππ
Today was just theory.
Focused on:
β’ What SQL is
β’ How databases work
β’ Core commands
β’ Why SQL matters in data
Halfway through the challenge.
Consistency > Motivation.
#100DaysOfDataScience #SQL
Tagar Terakhir Terlihat di Sotwe
ensestkizkardes
Dilihat dari Austria
minichat
Dilihat dari Netherlands
minichat((((((((((((((((()))))))))))))))))* filter:videos since:2026-09-15
Dilihat dari Japan
snapchat
ΩΩΩΨͺΩΩ
omegle porno
Dilihat dari Turkey
minichat
Dilihat dari United Kingdom
εεΏε°θ§ι’
Dilihat dari Argentina
momson
Dilihat dari United States
Tren untuk Anda
Pengguna Paling Populer
Elon Musk 
@elonmusk
241.7M pengikut
Barack Obama
@barackobama
119M pengikut
Cristiano Ronaldo
@cristiano
114.4M pengikut
Donald J. Trump
@realdonaldtrump
111.9M pengikut
Narendra Modi
@narendramodi
107.2M pengikut
Rihanna
@rihanna
98.7M pengikut
NASA
@nasa
92.4M pengikut
Justin Bieber
@justinbieber
91.8M pengikut
KATY PERRY
@katyperry
90M pengikut
Taylor Swift
@taylorswift13
83.9M pengikut
Lady Gaga
@ladygaga
75.4M pengikut
Virat Kohli
@imvkohli
73.4M pengikut
Kim Kardashian
@kimkardashian
70.9M pengikut
YouTube
@youtube
68.8M pengikut
Neymar Jr
@neymarjr
66.4M pengikut
Bill Gates
@billgates
65.2M pengikut
Selena Gomez
@selenagomez
63.1M pengikut
The Ellen Show
@theellenshow
62.3M pengikut
CNN
@cnn
61.8M pengikut
X
@x
60.7M pengikut