Capgemini hiring Data Scientist
Apply link: https://careers.capgemini.com/job/Kolkata-Data-Scientist-C/1208045701/?feedId=388933&utm_source=LinkedInJobPostings
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
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All the best 👍👍
Apply link: https://careers.capgemini.com/job/Kolkata-Data-Scientist-C/1208045701/?feedId=388933&utm_source=LinkedInJobPostings
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
👍2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗙𝗮𝘀𝘁.𝗮𝗶 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 – 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍
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𝐋𝐢𝐧𝐤👇:-
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Start building your AI skillset today—without spending a single rupee!✅️
Look no further! The Fast.ai Deep Learning Course is one of the most beginner-friendly and practical resources available — and the best part? It’s completely FREE📊📌
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Start building your AI skillset today—without spending a single rupee!✅️
❤1
Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst 👇
✅ They document every step of their analysis
➝ Clear notes make their work reproducible and trustworthy.
✅ They check data quality before the analysis begins
➝ Garbage in = garbage out. Always validate first.
✅ They use version control religiously
➝ Every code change is tracked. Nothing gets lost.
✅ They explore data thoroughly before diving in
➝ Understanding context prevents costly misinterpretations.
✅ They create automated noscripts for repetitive tasks
➝ Efficiency isn’t a luxury—it’s a necessity.
✅ They maintain a reusable code library
➝ Smart analysts never solve the same problem twice.
✅ They test assumptions with multiple validation methods
➝ One test isn’t enough; they triangulate confidence.
✅ They organize project files logically
➝ Their work is navigable by anyone, not just themselves.
✅ They seek peer reviews on critical work
➝ Fresh eyes catch blind spots.
✅ They continuously absorb industry knowledge
➝ Learning never stops. Trends change too quickly.
✅ They prioritize business-impacting projects
➝ Every analysis must drive real decisions.
✅ They explain complex findings simply
➝ Technical brilliance is useless without clarity.
✅ They write readable, well-commented code
➝ Their work is accessible to others, long after they're gone.
✅ They maintain robust backup systems
➝ Data loss is never an option.
✅ They learn from analytical mistakes
➝ Errors become stepping stones, not roadblocks.
✅ They build strong stakeholder relationships
➝ Data is only valuable when people use it.
✅ They break complex projects into manageable chunks
➝ Progress happens through disciplined, incremental work.
✅ They handle sensitive data with proper security
➝ Compliance isn’t optional—it’s foundational.
✅ They create visualizations that tell clear stories
➝ A chart without a narrative is just decoration.
✅ They actively seek evidence against their conclusions
➝ Confirmation bias is their biggest enemy.
The best analysts aren’t the ones with the most tools—they’re the ones with the most rigorous practices.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst 👇
✅ They document every step of their analysis
➝ Clear notes make their work reproducible and trustworthy.
✅ They check data quality before the analysis begins
➝ Garbage in = garbage out. Always validate first.
✅ They use version control religiously
➝ Every code change is tracked. Nothing gets lost.
✅ They explore data thoroughly before diving in
➝ Understanding context prevents costly misinterpretations.
✅ They create automated noscripts for repetitive tasks
➝ Efficiency isn’t a luxury—it’s a necessity.
✅ They maintain a reusable code library
➝ Smart analysts never solve the same problem twice.
✅ They test assumptions with multiple validation methods
➝ One test isn’t enough; they triangulate confidence.
✅ They organize project files logically
➝ Their work is navigable by anyone, not just themselves.
✅ They seek peer reviews on critical work
➝ Fresh eyes catch blind spots.
✅ They continuously absorb industry knowledge
➝ Learning never stops. Trends change too quickly.
✅ They prioritize business-impacting projects
➝ Every analysis must drive real decisions.
✅ They explain complex findings simply
➝ Technical brilliance is useless without clarity.
✅ They write readable, well-commented code
➝ Their work is accessible to others, long after they're gone.
✅ They maintain robust backup systems
➝ Data loss is never an option.
✅ They learn from analytical mistakes
➝ Errors become stepping stones, not roadblocks.
✅ They build strong stakeholder relationships
➝ Data is only valuable when people use it.
✅ They break complex projects into manageable chunks
➝ Progress happens through disciplined, incremental work.
✅ They handle sensitive data with proper security
➝ Compliance isn’t optional—it’s foundational.
✅ They create visualizations that tell clear stories
➝ A chart without a narrative is just decoration.
✅ They actively seek evidence against their conclusions
➝ Confirmation bias is their biggest enemy.
The best analysts aren’t the ones with the most tools—they’re the ones with the most rigorous practices.
❤1
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
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Microsoft :- https://pdlink.in/4iq8QlM
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Cisco :- https://pdlink.in/4fYr1xO
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Apple hiring Machine Learning Engineer
Apply link: https://jobs.apple.com/en-us/details/200604647/machine-learning-engineering-manager-photos
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All the best 👍👍
Apply link: https://jobs.apple.com/en-us/details/200604647/machine-learning-engineering-manager-photos
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𝗙𝗥𝗘𝗘 𝗧𝗔𝗧𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽😍
Gain Real-World Data Analytics Experience with TATA – 100% Free!
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Enroll For FREE & Get Certified🎓️
Gain Real-World Data Analytics Experience with TATA – 100% Free!
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Enroll For FREE & Get Certified🎓️
Stylumia is hiring Machine Learning Engineering Intern
For 2025 grads
Location: Bangalore
https://apply.workable.com/stylumia/j/6D2519412E/
For 2025 grads
Location: Bangalore
https://apply.workable.com/stylumia/j/6D2519412E/
Workable
Stylumia Intelligence Technology
Stylumia is dedicated to augmenting human intelligence in retail for a better world. With relevant data and the right technology, people and businesses in fashion and lifestyle retail can solve challenging problems and improve the world!!
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified🎓️
🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
𝐋𝐢𝐧𝐤👇:-
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Enroll For FREE & Get Certified🎓️
Forwarded from Jobs | Internships | Placement | Interviews
Google hiring Data Scientist
Apply link: https://careers.google.com/jobs/results/79726425535324870-data-scientist/
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Apply link: https://careers.google.com/jobs/results/79726425535324870-data-scientist/
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Statistics Interview Questions
Topics to Cover:
• Denoscriptive statistics
• Probability
• Hypothesis testing
• Regression analysis
Questions and Answers:
1 Q: What is the difference between denoscriptive and inferential statistics?
A: Denoscriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.
2 Q: Define p-value in hypothesis testing.
A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.
3 Q: What is the central limit theorem?
A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.
4 Q: Explain the concept of correlation.
A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.
5 Q: What is linear regression?
A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Topics to Cover:
• Denoscriptive statistics
• Probability
• Hypothesis testing
• Regression analysis
Questions and Answers:
1 Q: What is the difference between denoscriptive and inferential statistics?
A: Denoscriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.
2 Q: Define p-value in hypothesis testing.
A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.
3 Q: What is the central limit theorem?
A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.
4 Q: Explain the concept of correlation.
A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.
5 Q: What is linear regression?
A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Like if it helps :)
❤5
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟴 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍
🎓 Learn Data Science for Free from the World’s Best Universities🚀
Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hfpwjc
All The Best 👍
🎓 Learn Data Science for Free from the World’s Best Universities🚀
Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍
𝐋𝐢𝐧𝐤👇:-
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All The Best 👍
❤1
Data Analyst vs Data Scientist: Must-Know Differences
Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing denoscriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.
Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.
Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.
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Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing denoscriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.
Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.
Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.
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❤3
Forwarded from Python for Data Analysts
𝟰 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗙𝗿𝗲𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗔𝗜/𝗠𝗟 & 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 😍
Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners🚀
Learning tech doesn’t have to be overwhelming—especially when you have a roadmap to guide you!📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/45wfx2V
Enjoy Learning ✅️
Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners🚀
Learning tech doesn’t have to be overwhelming—especially when you have a roadmap to guide you!📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/45wfx2V
Enjoy Learning ✅️
WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
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❤5
Forwarded from Python for Data Analysts
𝟱 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to build job-ready tech skills from top companies — all for free?👨🎓
These 5 virtual experience programs offer hands-on learning, beginner-friendly modules, and certificates that strengthen your resume and LinkedIn profile 📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jnOv16
All The Best 🎊
Want to build job-ready tech skills from top companies — all for free?👨🎓
These 5 virtual experience programs offer hands-on learning, beginner-friendly modules, and certificates that strengthen your resume and LinkedIn profile 📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jnOv16
All The Best 🎊
Reality check on Data Analytics jobs:
⟶ Most recruiters & employers are open to different backgrounds
⟶ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
⟶ Excel - every job needs it
⟶ SQL - data retrieval and manipulation
⟶ Data Visualization - Tableau, Power BI, or Excel (Advanced)
⟶ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
⟶ Communication
⟶ Teamwork & Collaboration
⟶ Problem Solver
⟶ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
⟶ Most recruiters & employers are open to different backgrounds
⟶ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
⟶ Excel - every job needs it
⟶ SQL - data retrieval and manipulation
⟶ Data Visualization - Tableau, Power BI, or Excel (Advanced)
⟶ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
⟶ Communication
⟶ Teamwork & Collaboration
⟶ Problem Solver
⟶ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
❤2
Forwarded from Jobs | Internships | Placement | Interviews
Atlassian hiring Senior Data Scientist
Apply link: https://globalcareers-atlassian.icims.com/jobs/19662/senior-data-scientist/job
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Apply link: https://globalcareers-atlassian.icims.com/jobs/19662/senior-data-scientist/job
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
❤3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires 💫
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires 💫
❤2
Essential Python Libraries for Data Science
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
❤4
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 😍
Companies Hiring:- Revvity
Role :- Data Analyst - Intern
Location:- Mumbai
Qualification:- Students/Graduates
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸👇:-
https://pdlink.in/4mGclrF
Apply before the link expires 💫
Companies Hiring:- Revvity
Role :- Data Analyst - Intern
Location:- Mumbai
Qualification:- Students/Graduates
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸👇:-
https://pdlink.in/4mGclrF
Apply before the link expires 💫
❤2🔥1