Forwarded from Artificial Intelligence
๐๐๐ซ๐ง ๐
๐๐๐ ๐๐ซ๐๐๐ฅ๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐๐๐๐ โ ๐๐ฅ๐จ๐ฎ๐, ๐๐ & ๐๐๐ญ๐!๐
Oracleโs Race to Certification is here โ your chance to earn globally recognized certifications for FREE!๐ฅ
๐ก Choose from in-demand certifications in:
โ๏ธ Cloud
๐ค AI
๐ Data
โฆand more!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lx2tin
โกBut hurry โ spots are limited, and the clock is ticking!โ ๏ธ
Oracleโs Race to Certification is here โ your chance to earn globally recognized certifications for FREE!๐ฅ
๐ก Choose from in-demand certifications in:
โ๏ธ Cloud
๐ค AI
๐ Data
โฆand more!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lx2tin
โกBut hurry โ spots are limited, and the clock is ticking!โ ๏ธ
๐1
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฟ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ ๐๐ผ ๐๐ต๐ฎ๐ฝ๐ฒ ๐๐ผ๐๐ฟ ๐ฐ๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ: ๐
-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean scripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You canโt call yourself a data scientist if you donโt understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problemsโdonโt just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
๐ฌ๐ผ๐ ๐ฑ๐ผ๐ปโ๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ฒ๐ฐ๐.
๐ฌ๐ผ๐ ๐ท๐๐๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฐ๐ผ๐ป๐๐ถ๐๐๐ฒ๐ป๐.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
Hope this helps you ๐
-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean scripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You canโt call yourself a data scientist if you donโt understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problemsโdonโt just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
๐ฌ๐ผ๐ ๐ฑ๐ผ๐ปโ๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ฒ๐ฐ๐.
๐ฌ๐ผ๐ ๐ท๐๐๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฐ๐ผ๐ป๐๐ถ๐๐๐ฒ๐ป๐.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
Hope this helps you ๐
๐3
๐ฏ ๐๐ฎ๐บ๐ฒ-๐๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ๐
Want to break into Data Science or Tech?
Python is the #1 skill you need โ and starting is easier than you think.๐งโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JemBIt
Your career upgrade starts today โ no excuses!โ ๏ธ
Want to break into Data Science or Tech?
Python is the #1 skill you need โ and starting is easier than you think.๐งโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JemBIt
Your career upgrade starts today โ no excuses!โ ๏ธ
๐1
๐ ๐๐๐ฌ๐ญ ๐๐จ๐ฐ๐๐ซ ๐๐ ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ ๐ข๐ง ๐๐๐๐ ๐ญ๐จ ๐๐ค๐ฒ๐ซ๐จ๐๐ค๐๐ญ ๐๐จ๐ฎ๐ซ ๐๐๐ซ๐๐๐ซ๐
In todayโs data-driven world, Power BI has become one of the most in-demand tools for businessesใฝ๏ธ๐
The best part? You donโt need to spend a fortuneโthere are free and affordable courses available online to get you started.๐ฅ๐งโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mDvgDj
Start learning today and position yourself for success in 2025!โ ๏ธ
In todayโs data-driven world, Power BI has become one of the most in-demand tools for businessesใฝ๏ธ๐
The best part? You donโt need to spend a fortuneโthere are free and affordable courses available online to get you started.๐ฅ๐งโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mDvgDj
Start learning today and position yourself for success in 2025!โ ๏ธ
๐1
๐ Top 10 Data Analytics Concepts Everyone Should Know ๐
1๏ธโฃ Data Cleaning ๐งน
Removing duplicates, fixing missing or inconsistent data.
๐ Tools: Excel, Python (Pandas), SQL
2๏ธโฃ Descriptive Statistics ๐
Mean, median, mode, standard deviationโbasic measures to summarize data.
๐ Used for understanding data distribution
3๏ธโฃ Data Visualization ๐
Creating charts and dashboards to spot patterns.
๐ Tools: Power BI, Tableau, Matplotlib, Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA) ๐
Identifying trends, outliers, and correlations through deep data exploration.
๐ Step before modeling
5๏ธโฃ SQL for Data Extraction ๐๏ธ
Querying databases to retrieve specific information.
๐ Focus on SELECT, JOIN, GROUP BY, WHERE
6๏ธโฃ Hypothesis Testing โ๏ธ
Making decisions using sample data (A/B testing, p-value, confidence intervals).
๐ Useful in product or marketing experiments
7๏ธโฃ Correlation vs Causation ๐
Just because two things are related doesnโt mean one causes the other!
8๏ธโฃ Data Modeling ๐ง
Creating models to predict or explain outcomes.
๐ Linear regression, decision trees, clustering
9๏ธโฃ KPIs & Metrics ๐ฏ
Understanding business performance indicators like ROI, retention rate, churn.
๐ Storytelling with Data ๐ฃ๏ธ
Translating raw numbers into insights stakeholders can act on.
๐ Use clear visuals, simple language, and real-world impact
โค๏ธ React for more
1๏ธโฃ Data Cleaning ๐งน
Removing duplicates, fixing missing or inconsistent data.
๐ Tools: Excel, Python (Pandas), SQL
2๏ธโฃ Descriptive Statistics ๐
Mean, median, mode, standard deviationโbasic measures to summarize data.
๐ Used for understanding data distribution
3๏ธโฃ Data Visualization ๐
Creating charts and dashboards to spot patterns.
๐ Tools: Power BI, Tableau, Matplotlib, Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA) ๐
Identifying trends, outliers, and correlations through deep data exploration.
๐ Step before modeling
5๏ธโฃ SQL for Data Extraction ๐๏ธ
Querying databases to retrieve specific information.
๐ Focus on SELECT, JOIN, GROUP BY, WHERE
6๏ธโฃ Hypothesis Testing โ๏ธ
Making decisions using sample data (A/B testing, p-value, confidence intervals).
๐ Useful in product or marketing experiments
7๏ธโฃ Correlation vs Causation ๐
Just because two things are related doesnโt mean one causes the other!
8๏ธโฃ Data Modeling ๐ง
Creating models to predict or explain outcomes.
๐ Linear regression, decision trees, clustering
9๏ธโฃ KPIs & Metrics ๐ฏ
Understanding business performance indicators like ROI, retention rate, churn.
๐ Storytelling with Data ๐ฃ๏ธ
Translating raw numbers into insights stakeholders can act on.
๐ Use clear visuals, simple language, and real-world impact
โค๏ธ React for more
๐1