Complete Data Analytics Mastery: From Basics to Advanced 🚀
Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials
Grasp these essentials in just a week to build a solid foundation in data analytics.
Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics
Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.
Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics
These advanced concepts can be mastered in a couple of weeks with focused study and practice.
Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets
Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.
Best platforms to learn:
- Intro to Data Analysis
- Udacity's Data Analyst Nanodegree
- Intro to Data Visualisation
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Resume Checklist
- SQL FREE Resources
Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩💻👨💻
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials
Grasp these essentials in just a week to build a solid foundation in data analytics.
Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics
Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.
Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics
These advanced concepts can be mastered in a couple of weeks with focused study and practice.
Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets
Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.
Best platforms to learn:
- Intro to Data Analysis
- Udacity's Data Analyst Nanodegree
- Intro to Data Visualisation
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Resume Checklist
- SQL FREE Resources
Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩💻👨💻
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
👍3
𝟱 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Looking to break into data analytics but don’t know where to start?👋
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In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.
Here are some scenarios where using multiple scalers can be helpful in a data science project:
1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.
2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.
3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.
4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.
5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.
When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.
Here are some scenarios where using multiple scalers can be helpful in a data science project:
1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.
2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.
3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.
4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.
5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.
When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.
👍1
𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗙𝗥𝗘𝗘 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍
Whether you want to become an AI Engineer, Data Scientist, or ML Researcher, this course gives you the foundational skills to start your journey.
𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
Whether you want to become an AI Engineer, Data Scientist, or ML Researcher, this course gives you the foundational skills to start your journey.
𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
👍2
Want to practice for your next interview?
Then use this prompt and ask Chat GPT to act as an interviewer 😄👇 (Tap to copy)
Now see how it goes. All the best for your preparation
Like this post if you need more content like this👍❤️
Then use this prompt and ask Chat GPT to act as an interviewer 😄👇 (Tap to copy)
I want you to act as an interviewer. I will be the
candidate and you will ask me the
interview questions for the position position. I
want you to only reply as the interviewer.
Do not write all the conservation at once. I
want you to only do the interview with me.
Ask me the questions and wait for my answers.
Do not write explanations. Ask me the
questions one by one like an interviewer does
and wait for my answers. My first
sentence is "Hi"Now see how it goes. All the best for your preparation
Like this post if you need more content like this👍❤️
👍2
𝗟𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
Want to break into AI, UI/UX, or project management? 🚀
These 5 beginner-friendly FREE courses will help you develop in-demand skills and boost your resume in 2025!🎊
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✨ No cost, no catch—just pure learning from anywhere!
Want to break into AI, UI/UX, or project management? 🚀
These 5 beginner-friendly FREE courses will help you develop in-demand skills and boost your resume in 2025!🎊
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Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more 😄❤️
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more 😄❤️
👍2❤1
𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺😍
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𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
Want hands-on experience from a top global company without leaving your home?
These FREE virtual internship by JPMorgan on Forage let you explore careers in
✅ Software Engineering
✅ Investment Banking
✅ Quantitative Research
𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
4 ways to run LLMs like DeepSeek-R1 locally on your computer:
Running LLMs locally is like having a superpower:
- Cost savings
- Privacy: Your data stays on your computer
- Plus, it's incredibly fun
Let us explore some of the best methods to achieve this.
1️⃣ *Ollama*
* Running a model through Ollama is as simple as executing a command: ollama run deepseek-r1
* You can also install Ollama with a single command: curl -fsSL https:// ollama. com/install .sh | sh
2️⃣ *LMStudio*
* Install LMStudio can be installed as an app on your computer.
* It offers a ChatGPT-like interface, allowing you to load and eject models as if you were handling tapes in a tape recorder.
3️⃣ *vLLM*
* vLLM is a fast and easy-to-use library for LLM inference and serving.
* It has State-of-the-art serving throughput ⚡️
* A few lines of code and you can locally run DeepSeek as an OpenAI compatible server with reasoning enabled.
4️⃣ *LlamaCPP (the OG)*
* LlamaCPP enables LLM inference with minimal setup and state-of-the-art performance.
Running LLMs locally is like having a superpower:
- Cost savings
- Privacy: Your data stays on your computer
- Plus, it's incredibly fun
Let us explore some of the best methods to achieve this.
1️⃣ *Ollama*
* Running a model through Ollama is as simple as executing a command: ollama run deepseek-r1
* You can also install Ollama with a single command: curl -fsSL https:// ollama. com/install .sh | sh
2️⃣ *LMStudio*
* Install LMStudio can be installed as an app on your computer.
* It offers a ChatGPT-like interface, allowing you to load and eject models as if you were handling tapes in a tape recorder.
3️⃣ *vLLM*
* vLLM is a fast and easy-to-use library for LLM inference and serving.
* It has State-of-the-art serving throughput ⚡️
* A few lines of code and you can locally run DeepSeek as an OpenAI compatible server with reasoning enabled.
4️⃣ *LlamaCPP (the OG)*
* LlamaCPP enables LLM inference with minimal setup and state-of-the-art performance.
👍1
𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁!😍
Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ld6F7Y
No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️
Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ld6F7Y
No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️
Don't waste your lot of time when learning data analysis.
Here's how you may start your Data analysis journey
1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.
This advice might seem strange coming from a former software engineer, so let me explain.
The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.
In this scenario, nobody cares about how the analysis was completed.
Only the results matter.
Also, the analysis methods (e.g., code) are rarely shared in this scenario.
Like for next steps
#dataanalysis
Here's how you may start your Data analysis journey
1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.
This advice might seem strange coming from a former software engineer, so let me explain.
The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.
In this scenario, nobody cares about how the analysis was completed.
Only the results matter.
Also, the analysis methods (e.g., code) are rarely shared in this scenario.
Like for next steps
#dataanalysis
👍7
𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
Master Python, Machine Learning, SQL, and Data Visualization with hands-on tutorials & real-world datasets? 🎯
This 100% FREE resource from Kaggle will help you build job-ready skills—no fluff, no fees, just pure learning!
𝐋𝐢𝐧𝐤👇:-
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Perfect for Beginners ✅️
Master Python, Machine Learning, SQL, and Data Visualization with hands-on tutorials & real-world datasets? 🎯
This 100% FREE resource from Kaggle will help you build job-ready skills—no fluff, no fees, just pure learning!
𝐋𝐢𝐧𝐤👇:-
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Perfect for Beginners ✅️
SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.
Let’s dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
Let’s dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
❤3
𝗧𝗼𝗽 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍
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Want to work on real industry tasks, develop in-demand skills, and boost your resume—all for FREE?
Your dream career starts with real experience—grab this opportunity today!
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4bCyUIM
💡 No experience required—just learn, upskill & build your portfolio! 🚀
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𝟱 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.
𝐋𝐢𝐧𝐤👇:-
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All The Best 🎊
Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3DPkrga
All The Best 🎊
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