𝗦𝗯𝗲𝗿𝟱𝟬𝟬 𝗕𝗮𝘁𝗰𝗵 𝟳 — 𝗙𝗿𝗲𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿 𝗳𝗼𝗿 𝗔𝗜 & 𝗗𝗲𝗲𝗽𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 🚀

Ready to scale your startup beyond local market?

Who should apply:
✅ Startups with MVP and early traction
✅ DeepTech: GenAI, robotics, advanced materials, photonics, quantum computing
✅ Applied AI for research, Earth remote sensing, autonomous transport
✅ International founders exploring the Russian market

What you'll get:
📍 12-week online program in English
📍 International mentors (Europe, US, Asia, Middle East)
📍 Access to investors & corporate customers
📍 Demo Day at Moscow Startup Summit (Fall 2026)

Results:
📈 Revenue grows 4x on average, up to 1,000x for some teams
🤝 10,900+ contracts and pilots with corporations (6 seasons)

Program stages:
1️⃣ Online bootcamp for 150 teams
2️⃣ 25 best teams → intensive mentorship
3️⃣ Demo Day presentation

Key details:
📅 Deadline: 10 April 2026
💰 Participation: Free of charge
🌐 Format: Online
💬 Language: English

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✅ Data Cleaning in Pandas 🐍🧹

👉 In real projects, 80% of the work = Data Cleaning

Because raw data is always messy 😅

🔹 1. Why Data Cleaning?

Real-world data may have:
❌ Missing values
❌ Duplicate records
❌ Wrong formats
❌ Extra spaces

👉 Cleaning makes data usable for analysis & ML.

🔥 2. Handling Missing Values

✅ Check Missing Values

df.isnull()
df.isnull().sum()

✅ Remove Missing Values
df.dropna()

✅ Fill Missing Values
df.fillna(0)

👉 Replace missing values with 0 or mean.

🔹 3. Remove Duplicates

df.drop_duplicates()

🔹 4. Rename Columns

df.rename(columns={"Name": "Full_Name"}, inplace=True)

🔹 5. Change Data Types

df["Age"] = df["Age"].astype(int)

🔹 6. Remove Extra Spaces

df["Name"] = df["Name"].str.strip()

🔹 7. Replace Values

df["City"] = df["City"].replace("NY", "New York")

🔹 8. Why This is Important?
✔ Clean data = better insights
✔ Clean data = better ML models
✔ Used in every real-world project

🎯 Today’s Goal
✔ Handle missing values
✔ Remove duplicates
✔ Fix data types
✔ Clean text data

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Open for all. No Coding Background Required

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🔥Deadline :- 12th April

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Whether you're a fresher or career-switcher, here’s how to prep step-by-step:

1️⃣ Understand the Role
Data scientists solve problems using data. Core responsibilities:
• Data cleaning & analysis
• Building predictive models
• Communicating insights
• Working with business/product teams

2️⃣ Core Skills Needed
✔️ Python (NumPy, Pandas, Matplotlib, Scikit-learn)
✔️ SQL
✔️ Statistics & probability
✔️ Machine Learning basics
✔️ Data storytelling & visualization (Power BI / Tableau / Seaborn)

3️⃣ Key Interview Areas

A. Python & Coding
• Write code to clean and analyze data
• Solve logic problems (e.g., reverse a list, group data by key)
• List vs Dict vs DataFrame usage

B. Statistics & Probability
• Hypothesis testing
• p-values, confidence intervals
• Normal distribution, sampling

C. Machine Learning Concepts
• Supervised vs unsupervised learning
• Overfitting, regularization, cross-validation
• Algorithms: Linear Regression, Decision Trees, KNN, SVM

D. SQL
• Joins, GROUP BY, subqueries
• Window functions
• Data aggregation and filtering

E. Business & Communication
• Explain model results to non-tech stakeholders
• What metrics would you track for [business case]?
• Tell me about a time you used data to influence a decision

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✅ Do projects like:
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