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
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