♾️ New Microsoft cloud updates support Indonesia’s long-term AI goals

✏️ Indonesia’s push into AI-led growth is gaining momentum as more local organisations look for ways to build their own applications, update their systems, and strengthen data oversight.

✏️ The country now has broader access to cloud and AI tools after Microsoft expanded the services available in the Indonesia Central cloud region, which first went live six months ago.

✏️ The expansion gives businesses, public bodies, and developers more options to run AI workloads inside the country instead of overseas data centres.

Open Source Machine Learning - OpenDataScience

An open ML course balancing theory and practice: exploratory analysis, feature engineering, supervised/unsupervised models, ensembles, and time series. Kaggle-style assignments and Jupyter notebooks foster hands-on skills in heterogeneous data (text/images/geo).

📚 30+ lessons with videos, articles, and Kaggle tasks
⏰ Duration: 6 months
🏃‍♂️ Self Paced
Created by 👨‍🏫: OpenDataScience (Yury Kashnitsky)
🔗 Course Link

3 Common Questions About Data and Analytics

Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills:

1. Analysis of Sales Data:

(https://www.kaggle.com/kyanyoga/sample-sales-data)

2. HR Analytics:

(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)

3. Social Media Analytics:

(https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels)

4. Financial Data Analysis:

(https://www.kaggle.com/datasets/nitindatta/finance-data)

5. Healthcare Data Analysis:

(https://www.kaggle.com/cdc/mortality)

6. Customer Relationship Management:

(https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data)

7. Web Analytics:

(https://www.kaggle.com/zynicide/wine-reviews)

8. E-commerce Analysis:

(https://www.kaggle.com/olistbr/brazilian-ecommerce)

9. Supply Chain Management:

(https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis)

10. Inventory Management:

(https://www.kaggle.com/datasets?search=inventory+management)

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The best fine-tuning guide you'll find on arXiv this year.

Covers:
> NLP basics
> PEFT/LoRA/QLoRA techniques
> Mixture of Experts
> Seven-stage fine-tuning pipeline

Source: https://arxiv.org/pdf/2408.13296v1

📈 Data Visualisation Cheatsheet: 13 Must-Know Chart Types ✅

1️⃣ Gantt Chart
Tracks project schedules over time.
🔹 Advantage: Clarifies timelines & tasks
🔹 Use case: Project management & planning

2️⃣ Bubble Chart
Shows data with bubble size variations.
🔹 Advantage: Displays 3 data dimensions
🔹 Use case: Comparing social media engagement

3️⃣ Scatter Plots
Plots data points on two axes.
🔹 Advantage: Identifies correlations & clusters
🔹 Use case: Analyzing variable relationships

4️⃣ Histogram Chart
Visualizes data distribution in bins.
🔹 Advantage: Easy to see frequency
🔹 Use case: Understanding age distribution in surveys

5️⃣ Bar Chart
Uses rectangular bars to visualize data.
🔹 Advantage: Easy comparison across groups
🔹 Use case: Comparing sales across regions

6️⃣ Line Chart
Shows trends over time with lines.
🔹 Advantage: Clear display of data changes
🔹 Use case: Tracking stock market performance

7️⃣ Pie Chart
Represents data in circular segments.
🔹 Advantage: Simple proportion visualization
🔹 Use case: Displaying market share distribution

8️⃣ Maps
Geographic data representation on maps.
🔹 Advantage: Recognizes spatial patterns
🔹 Use case: Visualizing population density by area

9️⃣ Bullet Charts
Measures performance against a target.
🔹 Advantage: Compact alternative to gauges
🔹 Use case: Tracking sales vs quotas

🔟 Highlight Table
Colors tabular data based on values.
🔹 Advantage: Quickly identifies highs & lows
🔹 Use case: Heatmapping survey responses

1️⃣1️⃣ Tree Maps
Hierarchical data with nested rectangles.
🔹 Advantage: Efficient space usage
🔹 Use case: Displaying file system usage

1️⃣2️⃣ Box & Whisker Plot
Summarizes data distribution & outliers.
🔹 Advantage: Concise data spread representation
🔹 Use case: Comparing exam scores across classes

1️⃣3️⃣ Waterfall Charts / Walks
Visualizes sequential cumulative effect.
🔹 Advantage: Clarifies source of final value
🔹 Use case: Understanding profit & loss components

💡 Use the right chart to tell your data story clearly.

Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

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Data Analyst Roadmap 📊

📂 Python Basics
∟📂 Numpy & Pandas
∟📂 Data Cleaning
∟📂 Data Visualization (Matplotlib, Seaborn)
∟📂 SQL for Data Analysis
∟📂 Excel & Google Sheets
∟📂 Statistics for Analysis
∟📂 BI Tools (Power BI / Tableau)
∟📂 Real-World Projects
∟✅ Apply for Data Analyst Roles

❤️ React for More!

Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

Join @datasciencefun to learning important data science and machine learning concepts

ENJOY LEARNING 👍👍

📈 Want to Excel at Data Analytics? Master These Essential Skills! ☑️

Core Concepts:
• Statistics & Probability – Understand distributions, hypothesis testing
• Excel – Pivot tables, formulas, dashboards

Programming:
• Python – NumPy, Pandas, Matplotlib, Seaborn
• R – Data analysis & visualization
• SQL – Joins, filtering, aggregation

Data Cleaning & Wrangling:
• Handle missing values, duplicates
• Normalize and transform data

Visualization:
• Power BI, Tableau – Dashboards
• Plotly, Seaborn – Python visualizations
• Data Storytelling – Present insights clearly

Advanced Analytics:
• Regression, Classification, Clustering
• Time Series Forecasting
• A/B Testing & Hypothesis Testing

ETL & Automation:
• Web Scraping – BeautifulSoup, Scrapy
• APIs – Fetch and process real-world data
• Build ETL Pipelines

Tools & Deployment:
• Jupyter Notebook / Colab
• Git & GitHub
• Cloud Platforms – AWS, GCP, Azure
• Google BigQuery, Snowflake

Hope it helps :)

SQL vs Python Programming: Quick Comparison ✍

📌 SQL Programming

• Query data from databases
• Filter, join, aggregate rows

Best fields
• Data Analytics
• Business Intelligence
• Reporting and MIS
• Entry-level Data Engineering

Job titles
• Data Analyst
• Business Analyst
• BI Analyst
• SQL Developer

Hiring reality
• Asked in most analyst interviews
• Used daily in analyst roles

India salary range
• Fresher: 4–8 LPA
• Mid-level: 8–15 LPA

Real tasks
• Monthly sales report
• Top customers by revenue
• Duplicate removal

📌 Python Programming

• Clean and analyze data
• Automate workflows
• Build models

Where you work
• Notebooks
• Scripts
• ML pipelines

Best fields
• Data Science
• Machine Learning
• Automation
• Advanced Analytics

Job titles
• Data Scientist
• ML Engineer
• Analytics Engineer
• Python Developer

Hiring reality
• Common in mid to senior roles
• Strong demand in AI teams

India salary range
• Fresher: 6–10 LPA
• Mid-level: 12–25 LPA

Real tasks
• Churn prediction
• Report automation
• File handling CSV, Excel, JSON

⚔️ Quick comparison

• Data source
SQL stays inside databases
Python pulls data from anywhere

• Speed
SQL runs fast on large tables
Python slows with raw big data

• Learning
SQL is beginner-friendly
Python needs coding basics

🎯 Role-based choice

• Data Analyst
SQL required
Python adds value

• Data Scientist
Python required
SQL used to fetch data

• Business Analyst
SQL works for most roles
Python helps automate work

• Data Engineer
SQL for pipelines
Python for processing

✅ Best career move
• Learn SQL first for entry
• Add Python for growth
• Use both in real projects

Which one do you prefer?

SQL 👍
Python ❤️
Both 🙏
None 😮

🚀 Startup Accelerator Roadmap: Sber500 Batch 7 📊

📌 Who Should Apply

• Startups with MVP and early traction
• DeepTech teams in:
🔹 GenAI & Applied AI for Scientific Research
🔹 Robotics & Autonomous Transport Systems
🔹 Advanced Materials & Photonics
🔹 Quantum Computing
🔹 Earth Remote Sensing (Space & Ground-based)
• International founders exploring the Russian market

📌 Program Structure

1️⃣ Stage 1: Online Bootcamp
• 150 teams selected
• Strengthen product strategy & business model
• Identify market use cases
• Assess collaboration with Sber ecosystem

2️⃣ Stage 2: Intensive Mentorship
• 25 best teams selected
• Work with international mentors (Europe, US, Asia, Middle East)
• Access to actively investing funds
• Direct discussions with corporate customers

3️⃣ Stage 3: Demo Day
• Moscow Startup Summit, Fall 2026
• Present to wider audience
• In 2024 & 2025, every 5th startup was international

📌 What You Get
✅ 12-week online program in English
✅ International mentors (serial founders, VC partners, corporate executives)
✅ Access to investors & corporations
✅ Long-term community (work continues after program ends)

📌 Results That Speak
📈 Revenue grows 4x on average after program
🚀 Some teams scale up to 1,000x
🤝 10,900+ contracts and pilots with corporations (6 seasons)

📌 Previous International Teams From:
India, South Korea, Armenia, China, Turkey, Algeria

📌 Key Details
📅 Deadline: 10 April 2026
⏱️ Duration: Up to 12 weeks
🌐 Format: Online
💬 Language: English
💰 Participation: Free of charge

👉 Apply via the link

⚔️ Quick Comparison: Why Apply?

• Without Accelerator
🔹 Find mentors on your own
🔹 Pitch investors individually
🔹 Build corporate connections from scratch

• With Sber500
🔹 Access to curated mentor network
🔹 Demo Day with active investors
🔹 Direct path to corporate pilots

🎯 Best For:
• Data Science Startups → AI/ML solutions
• Analytics Teams → Enterprise data products
• DeepTech Founders → Science-intensive technology

Which stage interests you most?
Bootcamp 👌
Mentorship 🤝
Demo Day 👍

ℹ️ Learn More

Tap ♥️ for more startup resources!