If you’re just starting out in Data Analytics, it’s super important to build the right habits early.

Here’s a simple plan for beginners to grow both technical and problem-solving skills together:

If You Just Started Learning Data Analytics, Focus on These 5 Baby Steps:

1. Don’t Just Watch Tutorials — Build Small Projects

After learning a new tool (like SQL or Excel), create mini-projects:

- Analyze your expenses

- Explore a free dataset (like Netflix movies, COVID data)


2. Ask Business-Like Questions Early

Whenever you see a dataset, practice asking:

- What problem could this data solve?

- Who would care about this insight?


3. Start a ‘Data Journal’

Every day, note down:

- What you learned

- One business question you could answer with data (Helps you build real-world thinking!)


4. Practice the Basics 100x

Get very comfortable with:

- SELECT, WHERE, GROUP BY (SQL)

- Pivot tables and charts (Excel)

- Basic cleaning (Power Query / Python pandas)


_Mastering basics > learning 50 fancy functions._

5. Learn to Communicate Early

Explain your mini-projects like this:

- What was the business goal?

- What did you find?

- What should someone do based on it?

React with ❤️ for more

ENJOY LEARNING 👍👍

𝗧𝗵𝗲 𝟰 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝗯 (𝗘𝘃𝗲𝗻 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲) 💼

Recruiters don’t want to see more certificates—they want proof you can solve real-world problems. That’s where the right projects come in. Not toy datasets, but projects that demonstrate storytelling, problem-solving, and impact.

Here are 4 killer projects that’ll make your portfolio stand out 👇

🔹 1. Exploratory Data Analysis (EDA) on Real-World Dataset

Pick a messy dataset from Kaggle or public sources. Show your thought process.

✅ Clean data using Pandas
✅ Visualize trends with Seaborn/Matplotlib
✅ Share actionable insights with graphs and markdown

Bonus: Turn it into a Jupyter Notebook with detailed storytelling

🔹 2. Predictive Modeling with ML

Solve a real problem using machine learning. For example:

✅ Predict customer churn using Logistic Regression
✅ Predict housing prices with Random Forest or XGBoost
✅ Use scikit-learn for training + evaluation

Bonus: Add SHAP or feature importance to explain predictions

🔹 3. SQL-Powered Business Dashboard

Use real sales or ecommerce data to build a dashboard.

✅ Write complex SQL queries for KPIs
✅ Visualize with Power BI or Tableau
✅ Show trends: Revenue by Region, Product Performance, etc.

Bonus: Add filters & slicers to make it interactive

🔹 4. End-to-End Data Science Pipeline Project

Build a complete pipeline from scratch.

✅ Collect data via web scraping (e.g., IMDb, LinkedIn Jobs)
✅ Clean + Analyze + Model + Deploy
✅ Deploy with Streamlit/Flask + GitHub + Render

Bonus: Add a blog post or LinkedIn write-up explaining your approach

🎯 One solid project > 10 certificates.

Make it visible. Make it valuable. Share it confidently.

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👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

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♾️ 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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Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

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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

Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape

🔘Pro is currently the #1 open-source model worldwide
🔘Lite (2B parameters) outperforms Sora v1.
🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21.

Useful links
🔘Full leaderboard: LM Arena
🔘Kandinsky 5.0 details: technical report
🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

📈 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

Tap ♥️ for more!

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 Denoscription
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 :)