Roadmap to become a Data Scientist:
📂 Learn Python & R
∟📂 Learn Statistics & Probability
∟📂 Learn SQL & Data Handling
∟📂 Learn Data Cleaning & Preprocessing
∟📂 Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
∟📂 Learn Machine Learning (Supervised, Unsupervised)
∟📂 Learn Deep Learning (Neural Nets, CNNs, RNNs)
∟📂 Learn Model Deployment (Flask, Streamlit, FastAPI)
∟📂 Build Real-world Projects & Case Studies
∟✅ Apply for Jobs & Internships
React ❤️ for more
Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
📂 Learn Python & R
∟📂 Learn Statistics & Probability
∟📂 Learn SQL & Data Handling
∟📂 Learn Data Cleaning & Preprocessing
∟📂 Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
∟📂 Learn Machine Learning (Supervised, Unsupervised)
∟📂 Learn Deep Learning (Neural Nets, CNNs, RNNs)
∟📂 Learn Model Deployment (Flask, Streamlit, FastAPI)
∟📂 Build Real-world Projects & Case Studies
∟✅ Apply for Jobs & Internships
React ❤️ for more
Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
👍2
Forwarded from Artificial Intelligence
𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Feeling like your resume could use a boost? 🚀
Let’s make that happen with Microsoft Azure certifications that are not only perfect for beginners but also completely free!🔥💯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iVRmiQ
Essential skills for today’s tech-driven world✅️
Feeling like your resume could use a boost? 🚀
Let’s make that happen with Microsoft Azure certifications that are not only perfect for beginners but also completely free!🔥💯
𝐋𝐢𝐧𝐤👇:-
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Essential skills for today’s tech-driven world✅️
👍1
Essential NumPy Functions for Data Analysis
Array Creation:
np.array() - Create an array from a list.
np.zeros((rows, cols)) - Create an array filled with zeros.
np.ones((rows, cols)) - Create an array filled with ones.
np.arange(start, stop, step) - Create an array with a range of values.
Array Operations:
np.sum(array) - Calculate the sum of array elements.
np.mean(array) - Compute the mean.
np.median(array) - Calculate the median.
np.std(array) - Compute the standard deviation.
Indexing and Slicing:
array[start:stop] - Slice an array.
array[row, col] - Access a specific element.
array[:, col] - Select all rows for a column.
Reshaping and Transposing:
array.reshape(new_shape) - Reshape an array.
array.T - Transpose an array.
Random Sampling:
np.random.rand(rows, cols) - Generate random numbers in [0, 1).
np.random.randint(low, high, size) - Generate random integers.
Mathematical Operations:
np.dot(A, B) - Compute the dot product.
np.linalg.inv(A) - Compute the inverse of a matrix.
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Array Creation:
np.array() - Create an array from a list.
np.zeros((rows, cols)) - Create an array filled with zeros.
np.ones((rows, cols)) - Create an array filled with ones.
np.arange(start, stop, step) - Create an array with a range of values.
Array Operations:
np.sum(array) - Calculate the sum of array elements.
np.mean(array) - Compute the mean.
np.median(array) - Calculate the median.
np.std(array) - Compute the standard deviation.
Indexing and Slicing:
array[start:stop] - Slice an array.
array[row, col] - Access a specific element.
array[:, col] - Select all rows for a column.
Reshaping and Transposing:
array.reshape(new_shape) - Reshape an array.
array.T - Transpose an array.
Random Sampling:
np.random.rand(rows, cols) - Generate random numbers in [0, 1).
np.random.randint(low, high, size) - Generate random integers.
Mathematical Operations:
np.dot(A, B) - Compute the dot product.
np.linalg.inv(A) - Compute the inverse of a matrix.
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍1
Forwarded from Python Projects & Resources
𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱 — 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍
📌 Preparing for Python Interviews in 2025?🗣
If you’re aiming for roles in data analysis, backend development, or automation, Python is your key weapon—and so is preparing with the right questions.💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZbAtrW
Crack your next Python interview✅️
📌 Preparing for Python Interviews in 2025?🗣
If you’re aiming for roles in data analysis, backend development, or automation, Python is your key weapon—and so is preparing with the right questions.💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZbAtrW
Crack your next Python interview✅️
👍1
Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
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𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗦𝘁𝗮𝗿𝘁 𝗪𝗶𝘁𝗵😍
💻 Want to Learn Coding but Don’t Know Where to Start?🎯
Whether you’re a student, career switcher, or complete beginner, this curated list is your perfect launchpad into tech💻🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/437ow7Y
All The Best 🎊
💻 Want to Learn Coding but Don’t Know Where to Start?🎯
Whether you’re a student, career switcher, or complete beginner, this curated list is your perfect launchpad into tech💻🚀
𝐋𝐢𝐧𝐤👇:-
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All The Best 🎊
𝟰 𝗙𝗿𝗲𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗦𝗵𝗮𝗿𝗽𝗲𝗻 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
🎯 Want to Sharpen Your Data Analytics Skills with Hands-On Practice?📊
Watching tutorials can only take you so far—practical application is what truly builds confidence and prepares you for the real world🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GQGR1B
Start practicing what actually gets you hired✅️
🎯 Want to Sharpen Your Data Analytics Skills with Hands-On Practice?📊
Watching tutorials can only take you so far—practical application is what truly builds confidence and prepares you for the real world🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GQGR1B
Start practicing what actually gets you hired✅️
Data science interview questions 👇
𝗦𝗤𝗟
- How do you write a query to fetch the top 5 highest salaries in each department?
- What’s the difference between the HAVING and WHERE clauses in SQL?
- How do you handle NULL values in SQL, and how do they affect aggregate functions?
𝗣𝘆𝘁𝗵𝗼𝗻
- How do you handle large datasets in Python, and which libraries would you use for performance?
- What are context managers in Python, and how do they help with resource management?
- How do you manage and log errors in Python-based ETL pipelines?
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
- Explain the difference between bias and variance in a machine learning model. How do you balance them?
- What is cross-validation, and how does it improve the performance of machine learning models?
- How do you deal with class imbalance in classification tasks, and what techniques would you apply?
𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
- What is the vanishing gradient problem in deep learning, and how can it be mitigated?
- Explain how a convolutional neural network (CNN) works and when you would use it.
- What is dropout in neural networks, and how does it help prevent overfitting?
𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴
- How would you handle outliers in a dataset, and when is it appropriate to remove or keep them?
- Explain how to merge two datasets in Python, and how would you handle duplicate or missing entries in the merged data?
- What is data normalization, and when should you apply it to your dataset?
𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - 𝗧𝗮𝗯𝗹𝗲𝗮𝘂
- How do you create a dual-axis chart in Tableau, and when would you use it?
- How would you filter data in Tableau to create a dynamic dashboard that updates based on user input?
- What are calculated fields in Tableau, and how would you use them to create a custom metric?
#datascience #interview
𝗦𝗤𝗟
- How do you write a query to fetch the top 5 highest salaries in each department?
- What’s the difference between the HAVING and WHERE clauses in SQL?
- How do you handle NULL values in SQL, and how do they affect aggregate functions?
𝗣𝘆𝘁𝗵𝗼𝗻
- How do you handle large datasets in Python, and which libraries would you use for performance?
- What are context managers in Python, and how do they help with resource management?
- How do you manage and log errors in Python-based ETL pipelines?
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
- Explain the difference between bias and variance in a machine learning model. How do you balance them?
- What is cross-validation, and how does it improve the performance of machine learning models?
- How do you deal with class imbalance in classification tasks, and what techniques would you apply?
𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
- What is the vanishing gradient problem in deep learning, and how can it be mitigated?
- Explain how a convolutional neural network (CNN) works and when you would use it.
- What is dropout in neural networks, and how does it help prevent overfitting?
𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴
- How would you handle outliers in a dataset, and when is it appropriate to remove or keep them?
- Explain how to merge two datasets in Python, and how would you handle duplicate or missing entries in the merged data?
- What is data normalization, and when should you apply it to your dataset?
𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - 𝗧𝗮𝗯𝗹𝗲𝗮𝘂
- How do you create a dual-axis chart in Tableau, and when would you use it?
- How would you filter data in Tableau to create a dynamic dashboard that updates based on user input?
- What are calculated fields in Tableau, and how would you use them to create a custom metric?
#datascience #interview
❤1👍1
𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍
📊 Want to Learn Data Analytics but Hate the High Price Tags?💰📌
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform💻🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iXNfS3
All The Best 🎊
📊 Want to Learn Data Analytics but Hate the High Price Tags?💰📌
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform💻🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iXNfS3
All The Best 🎊
👍1
Data Analyst INTERVIEW QUESTIONS AND ANSWERS
👇👇
1.Can you name the wildcards in Excel?
Ans: There are 3 wildcards in Excel that can ve used in formulas.
Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.
Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.
Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.
Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.
2.What is cascading filter in tableau?
Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.
3.What is the difference between .twb and .twbx extension?
Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.
4.What are the various Power BI versions?
Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subnoscription while workspaces are at Premium capacity.
ENJOY LEARNING 👍👍
👇👇
1.Can you name the wildcards in Excel?
Ans: There are 3 wildcards in Excel that can ve used in formulas.
Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.
Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.
Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.
Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.
2.What is cascading filter in tableau?
Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.
3.What is the difference between .twb and .twbx extension?
Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.
4.What are the various Power BI versions?
Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subnoscription while workspaces are at Premium capacity.
ENJOY LEARNING 👍👍
👍2
Forwarded from Coding & AI Resources
𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍
Top Companies Offering FREE Certification Courses To Upskill In 2025
Google:- https://pdlink.in/3YsujTV
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Enroll For FREE & Get Certified 🎓
Top Companies Offering FREE Certification Courses To Upskill In 2025
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/4jpmI0I
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Qualc :- https://pdlink.in/3YrFTyK
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Enroll For FREE & Get Certified 🎓
👍1
Useful Python for data science cheat sheets 👇
𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗧𝗼𝗽 𝗝𝗼𝗯𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Start your journey with this FREE Generative AI course offered by Microsoft and LinkedIn.
It’s part of their Career Essentials program designed to make you job-ready with real-world AI skills.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jY0cwB
This certification will boost your resume✅️
Start your journey with this FREE Generative AI course offered by Microsoft and LinkedIn.
It’s part of their Career Essentials program designed to make you job-ready with real-world AI skills.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jY0cwB
This certification will boost your resume✅️
👍1
Essential Skills to Master for Using Generative AI
1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.
3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.
5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.
6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.
8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.
9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! 🔥
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.
3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.
5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.
6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.
8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.
9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! 🔥
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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𝟱 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Whether you’re a beginner, career switcher, or just curious about data analytics, these 5 free online courses are your perfect starting point!🎯
𝐋𝐢𝐧𝐤👇:-
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Gain the skills to manage analytics projects✅️
Whether you’re a beginner, career switcher, or just curious about data analytics, these 5 free online courses are your perfect starting point!🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FdLMcv
Gain the skills to manage analytics projects✅️