Forwarded from Python for Data Analysts
๐ฒ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ต๐ฒ ๐ ๐ผ๐๐ ๐๐ป-๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐๐
๐ Want to future-proof your career without spending a single rupee?๐ต
These 6 free online courses from top institutions like Google, Harvard, IBM, Stanford, and Cisco will help you master high-demand tech skills in 2025 โ from Data Analytics to Machine Learning๐๐งโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4fbDejW
Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careersโ ๏ธ
๐ Want to future-proof your career without spending a single rupee?๐ต
These 6 free online courses from top institutions like Google, Harvard, IBM, Stanford, and Cisco will help you master high-demand tech skills in 2025 โ from Data Analytics to Machine Learning๐๐งโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4fbDejW
Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careersโ ๏ธ
โค1
Top 5 data analysis interview questions with answers ๐๐
Question 1: How would you approach a new data analysis project?
Ideal answer:
I would approach a new data analysis project by following these steps:
Understand the business goals. What is the purpose of the data analysis? What questions are we trying to answer?
Gather the data. This may involve collecting data from different sources, such as databases, spreadsheets, and surveys.
Clean and prepare the data. This may involve removing duplicate data, correcting errors, and formatting the data in a consistent way.
Explore the data. This involves using data visualization and statistical analysis to understand the data and identify any patterns or trends.
Build a model or hypothesis. This involves using the data to develop a model or hypothesis that can be used to answer the business questions.
Test the model or hypothesis. This involves using the data to test the model or hypothesis and see how well it performs.
Interpret and communicate the results. This involves explaining the results of the data analysis to stakeholders in a clear and concise way.
Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer:
One of the biggest challenges I have faced in previous data analysis projects is dealing with missing data. I have overcome this challenge by using a variety of techniques, such as imputation and machine learning.
Another challenge I have faced is dealing with large datasets. I have overcome this challenge by using efficient data processing techniques and by using cloud computing platforms.
Question 3: Can you describe a time when you used data analysis to solve a business problem?
Ideal answer:
In my previous role at a retail company, I was tasked with identifying the products that were most likely to be purchased together. I used data analysis to identify patterns in the purchase data and to develop a model that could predict which products were most likely to be purchased together. This model was used to improve the company's product recommendations and to increase sales.
Question 4: What are some of your favorite data analysis tools and techniques?
Ideal answer:
Some of my favorite data analysis tools and techniques include:
Programming languages such as Python and R
Data visualization tools such as Tableau and Power BI
Statistical analysis tools such as SPSS and SAS
Machine learning algorithms such as linear regression and decision trees
Question 5: How do you stay up-to-date on the latest trends and developments in data analysis?
Ideal answer:
I stay up-to-date on the latest trends and developments in data analysis by reading industry publications, attending conferences, and taking online courses. I also follow thought leaders on social media and subscribe to newsletters.
By providing thoughtful and well-informed answers to these questions, you can demonstrate to your interviewer that you have the analytical skills and knowledge necessary to be successful in the role.
Like this post if you want more interview questions with detailed answers to be posted in the channel ๐โค๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Question 1: How would you approach a new data analysis project?
Ideal answer:
I would approach a new data analysis project by following these steps:
Understand the business goals. What is the purpose of the data analysis? What questions are we trying to answer?
Gather the data. This may involve collecting data from different sources, such as databases, spreadsheets, and surveys.
Clean and prepare the data. This may involve removing duplicate data, correcting errors, and formatting the data in a consistent way.
Explore the data. This involves using data visualization and statistical analysis to understand the data and identify any patterns or trends.
Build a model or hypothesis. This involves using the data to develop a model or hypothesis that can be used to answer the business questions.
Test the model or hypothesis. This involves using the data to test the model or hypothesis and see how well it performs.
Interpret and communicate the results. This involves explaining the results of the data analysis to stakeholders in a clear and concise way.
Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer:
One of the biggest challenges I have faced in previous data analysis projects is dealing with missing data. I have overcome this challenge by using a variety of techniques, such as imputation and machine learning.
Another challenge I have faced is dealing with large datasets. I have overcome this challenge by using efficient data processing techniques and by using cloud computing platforms.
Question 3: Can you describe a time when you used data analysis to solve a business problem?
Ideal answer:
In my previous role at a retail company, I was tasked with identifying the products that were most likely to be purchased together. I used data analysis to identify patterns in the purchase data and to develop a model that could predict which products were most likely to be purchased together. This model was used to improve the company's product recommendations and to increase sales.
Question 4: What are some of your favorite data analysis tools and techniques?
Ideal answer:
Some of my favorite data analysis tools and techniques include:
Programming languages such as Python and R
Data visualization tools such as Tableau and Power BI
Statistical analysis tools such as SPSS and SAS
Machine learning algorithms such as linear regression and decision trees
Question 5: How do you stay up-to-date on the latest trends and developments in data analysis?
Ideal answer:
I stay up-to-date on the latest trends and developments in data analysis by reading industry publications, attending conferences, and taking online courses. I also follow thought leaders on social media and subscribe to newsletters.
By providing thoughtful and well-informed answers to these questions, you can demonstrate to your interviewer that you have the analytical skills and knowledge necessary to be successful in the role.
Like this post if you want more interview questions with detailed answers to be posted in the channel ๐โค๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐๐ง๐ผ๐ฝ ๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐ผ๐ด๐น๐ฒ-๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to boost your tech career? Learn Python for FREE with Google-certified courses!
Perfect for beginnersโno expensive bootcamps needed.
๐ฅ Learn Python for AI, Data, Automation & More!
๐๐ฆ๐๐ฎ๐ฟ๐ ๐ก๐ผ๐๐
https://pdlink.in/42okGqG
โ Future You Will Thank You!
Want to boost your tech career? Learn Python for FREE with Google-certified courses!
Perfect for beginnersโno expensive bootcamps needed.
๐ฅ Learn Python for AI, Data, Automation & More!
๐๐ฆ๐๐ฎ๐ฟ๐ ๐ก๐ผ๐๐
https://pdlink.in/42okGqG
โ Future You Will Thank You!
โค1
๐ข Weโre Hiring: Senior Data Scientist (SDS) | 2.5โ3.5 Years of Experience
Weโre looking for high-caliber Senior Data Scientists to join our team at Sigmoid Analytics โ individuals with a passion for solving real-world business problems using scalable machine learning solutions.
If you thrive in a high-performance environment and have experience building data products end-to-end, we want to hear from you!
โ What Weโre Looking For:
2.5โ3.5 years of hands-on experience in data science & machine learning
Proficient in Python and ML libraries
Experience in building and deploying ML models in production
Excellent communication skills & business understanding
๐ From Tier-1 / Tier-2 Engineering Colleges only
๐ Location: Whitefield, Bangalore
๐ซ Note: Candidates who interviewed with Sigmoid in the last 6 months are not eligible.
๐ฌ To Apply:
Email your CV and the following details to anu.s@sigmoidanalytics.com
Years of experience
Current CTC
Expected CTC
Notice period
Weโre looking for high-caliber Senior Data Scientists to join our team at Sigmoid Analytics โ individuals with a passion for solving real-world business problems using scalable machine learning solutions.
If you thrive in a high-performance environment and have experience building data products end-to-end, we want to hear from you!
โ What Weโre Looking For:
2.5โ3.5 years of hands-on experience in data science & machine learning
Proficient in Python and ML libraries
Experience in building and deploying ML models in production
Excellent communication skills & business understanding
๐ From Tier-1 / Tier-2 Engineering Colleges only
๐ Location: Whitefield, Bangalore
๐ซ Note: Candidates who interviewed with Sigmoid in the last 6 months are not eligible.
๐ฌ To Apply:
Email your CV and the following details to anu.s@sigmoidanalytics.com
Years of experience
Current CTC
Expected CTC
Notice period
โค4๐1
Forwarded from Python for Data Analysts
๐ง๐ต๐ฒ ๐๐ฒ๐๐ ๐๐ฟ๐ฒ๐ฒ ๐ฏ๐ฌ-๐๐ฎ๐ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ ๐๐ผ ๐ฆ๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐๐ฟ๐ป๐ฒ๐๐
๐ If I had to restart my Data Science journey in 2025, this is where Iโd beginโจ๏ธ
Meet 30 Days of Data Science โ a free and beginner-friendly GitHub repository that guides you through the core fundamentals of data science in just one month๐งโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mfNdXR
Simply bookmark the page, pick Day 1, and begin your journeyโ ๏ธ
๐ If I had to restart my Data Science journey in 2025, this is where Iโd beginโจ๏ธ
Meet 30 Days of Data Science โ a free and beginner-friendly GitHub repository that guides you through the core fundamentals of data science in just one month๐งโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mfNdXR
Simply bookmark the page, pick Day 1, and begin your journeyโ ๏ธ
โค1
Forwarded from Python for Data Analysts
๐ณ ๐ ๐๐๐-๐๐ป๐ผ๐ ๐ฆ๐ค๐ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐ ๐๐๐ฒ๐ฟ๐ ๐๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐
If youโre serious about becoming a data analyst, thereโs no skipping SQL. Itโs not just another technical skill โ itโs the core language for data analytics.๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learnโ ๏ธ
If youโre serious about becoming a data analyst, thereโs no skipping SQL. Itโs not just another technical skill โ itโs the core language for data analytics.๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learnโ ๏ธ
โค1
Some essential concepts every data scientist should understand:
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.
### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).
### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.
### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.
### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).
### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.
### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).
### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.
### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.
### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.
### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.
### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.
### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.
### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.
### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.
### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).
### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.
### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.
### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).
### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.
### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).
### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.
### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.
### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.
### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.
### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.
### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.
### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.
### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
โค2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐๐ฟ๐ฒ๐ฒ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ & ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ ๐๐ฎ๐ป๐ฑ ๐ง๐ผ๐ฝ ๐๐ผ๐ฏ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
๐ฏ Want to Land High-Paying AI Jobs in 2025?
Start your journey with this FREE Generative AI course offered by Microsoft and LinkedIn๐งโ๐โจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jY0cwB
This certification will boost your resume๐โ ๏ธ
๐ฏ Want to Land High-Paying AI Jobs in 2025?
Start your journey with this FREE Generative AI course offered by Microsoft and LinkedIn๐งโ๐โจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jY0cwB
This certification will boost your resume๐โ ๏ธ
โค2
Struggling with Machine Learning algorithms? ๐ค
Then you better stay with me! ๐ค
We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! ๐๐ป
1๏ธโฃ ๐๐ข๐๐๐ฆ๐ง๐๐ ๐ฅ๐๐๐ฅ๐๐ฆ๐ฆ๐๐ข๐ก
It is a binary classification model used to classify our input data into two main categories.
It can be extended to multiple classifications... but today we'll focus on a binary one.
Also known as Simple Logistic Regression.
2๏ธโฃ ๐๐ข๐ช ๐ง๐ข ๐๐ข๐ ๐ฃ๐จ๐ง๐ ๐๐ง?
The Sigmoid Function is our mathematical wand, turning numbers into neat probabilities between 0 and 1.
It's what makes Logistic Regression tick, giving us a clear 'probabilistic' picture.
3๏ธโฃ ๐๐ข๐ช ๐ง๐ข ๐๐๐๐๐ก๐ ๐ง๐๐ ๐๐๐ฆ๐ง ๐๐๐ง?
For every parametric ML algorithm, we need a LOSS FUNCTION.
It is our map to find our optimal solution or global minimum.
(hoping there is one! ๐)
โ ๐๐ข๐ก๐จ๐ฆ - FROM LINEAR TO LOGISTIC REGRESSION
To obtain the sigmoid function, we can derive it from the Linear Regression equation.
Then you better stay with me! ๐ค
We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! ๐๐ป
1๏ธโฃ ๐๐ข๐๐๐ฆ๐ง๐๐ ๐ฅ๐๐๐ฅ๐๐ฆ๐ฆ๐๐ข๐ก
It is a binary classification model used to classify our input data into two main categories.
It can be extended to multiple classifications... but today we'll focus on a binary one.
Also known as Simple Logistic Regression.
2๏ธโฃ ๐๐ข๐ช ๐ง๐ข ๐๐ข๐ ๐ฃ๐จ๐ง๐ ๐๐ง?
The Sigmoid Function is our mathematical wand, turning numbers into neat probabilities between 0 and 1.
It's what makes Logistic Regression tick, giving us a clear 'probabilistic' picture.
3๏ธโฃ ๐๐ข๐ช ๐ง๐ข ๐๐๐๐๐ก๐ ๐ง๐๐ ๐๐๐ฆ๐ง ๐๐๐ง?
For every parametric ML algorithm, we need a LOSS FUNCTION.
It is our map to find our optimal solution or global minimum.
(hoping there is one! ๐)
โ ๐๐ข๐ก๐จ๐ฆ - FROM LINEAR TO LOGISTIC REGRESSION
To obtain the sigmoid function, we can derive it from the Linear Regression equation.
โค1
American Express is hiring Analyst ๐
Min. Experience : 1 Year
Location : Gurugram
Apply link : https://aexp.eightfold.ai/careers/job/30504056?hl=en&utm_source=linkedin&domain=aexp.com
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Min. Experience : 1 Year
Location : Gurugram
Apply link : https://aexp.eightfold.ai/careers/job/30504056?hl=en&utm_source=linkedin&domain=aexp.com
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
๐1
Gartner is hiring Associate Data Scientist ๐
Experience : 0-3 Years
Location : Gurugram
Apply link : https://gartner.wd5.myworkdayjobs.com/EXT/job/Gurgaon/Associate-Data-Scientist_101739-1/apply?source=JB-10120
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Experience : 0-3 Years
Location : Gurugram
Apply link : https://gartner.wd5.myworkdayjobs.com/EXT/job/Gurgaon/Associate-Data-Scientist_101739-1/apply?source=JB-10120
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
๐1
Dell Technologies is hiring!
Position: Data Scientist
Qualifications: Bachelorโs/ Master's Degree/ PhD
Salary: 7 - 19 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore/ Hyderabad
๏ปฟ
๐Apply Now: https://jobs.dell.com/en/job/bengaluru/data-scientist/375/84532530736
๐ WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐ Telegram Channel: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best! ๐๐
Position: Data Scientist
Qualifications: Bachelorโs/ Master's Degree/ PhD
Salary: 7 - 19 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore/ Hyderabad
๏ปฟ
๐Apply Now: https://jobs.dell.com/en/job/bengaluru/data-scientist/375/84532530736
๐ WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐ Telegram Channel: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best! ๐๐
โค3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฒ ๐๐ฟ๐ฒ๐ฒ ๐๐๐น๐น ๐ง๐ฒ๐ฐ๐ต ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฌ๐ผ๐ ๐๐ฎ๐ป ๐ช๐ฎ๐๐ฐ๐ต ๐ฅ๐ถ๐ด๐ต๐ ๐ก๐ผ๐๐
Ready to level up your tech game without spending a rupee? These 6 full-length courses are beginner-friendly, 100% free, and packed with practical knowledge๐๐งโ๐
Whether you want to code in Python, hack ethically, or build your first Android app โ these videos are your shortcut to real tech skills๐ฑ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42V73k4
Save this list and start crushing your tech goals today!โ ๏ธ
Ready to level up your tech game without spending a rupee? These 6 full-length courses are beginner-friendly, 100% free, and packed with practical knowledge๐๐งโ๐
Whether you want to code in Python, hack ethically, or build your first Android app โ these videos are your shortcut to real tech skills๐ฑ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42V73k4
Save this list and start crushing your tech goals today!โ ๏ธ
โค1
EY is hiring!
Position: Data Analytics - Associate
Qualifications: Bachelor's/ Master's Degree
Salary: 6 - 13 LPA (Expected)
Experience: Freshers/ Experienced
Location: Across India
๐Apply Now: https://careers.ey.com/ey/job/Kochi-Reporting-and-Data-Analytics-Associate-KL-682303/1232867801/
๐ WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐ Telegram Channel: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best! ๐๐
Position: Data Analytics - Associate
Qualifications: Bachelor's/ Master's Degree
Salary: 6 - 13 LPA (Expected)
Experience: Freshers/ Experienced
Location: Across India
๐Apply Now: https://careers.ey.com/ey/job/Kochi-Reporting-and-Data-Analytics-Associate-KL-682303/1232867801/
๐ WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐ Telegram Channel: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best! ๐๐
โค1
Role: Tableau + SQL Developer
๐ Location: Bangalore / Hyderabad
๐ง Experience: 3 โ 7 Years
๐ Skills:
Strong expertise in Tableau and SQL
๐ Good to have: Experience in Tableau to Power BI migration
๐ข Preferred: Candidates from Tier-1 companies
If you're looking to work on impactful projects and be part of a high-performance team โ we want to hear from you!
Please share your Resume to divyam@psrtek.com
๐ Location: Bangalore / Hyderabad
๐ง Experience: 3 โ 7 Years
๐ Skills:
Strong expertise in Tableau and SQL
๐ Good to have: Experience in Tableau to Power BI migration
๐ข Preferred: Candidates from Tier-1 companies
If you're looking to work on impactful projects and be part of a high-performance team โ we want to hear from you!
Please share your Resume to divyam@psrtek.com
โค1
Forwarded from Python for Data Analysts
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Want to earn free certificates and badges from Microsoft? ๐
These courses are your golden ticket to mastering in-demand tech skills while boosting your resume with official Microsoft credentials๐งโ๐ป๐
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These certifications will help you stand out in interviews and open new career opportunities in techโ ๏ธ
โค1
Hiring: Data Scientist & Senior Data Scientist ๐
Join our dynamic team at KreditBee, one of Indiaโs fastest-growing fintech platforms!
Location: Bangalore (Work from Office, 5 days a week)
Qualification: B.E / B.Tech in Computer Science
Experience: 2 to 6 years
Key Skills: Python, SQL, AWS, Credit risk modeling
Domain: Fintech background preferred
Notice Period: Immediate joiners or up to 30 days
Note: Finance Knowledge for model building
If youโre interested, please share your CV at
deekshitha.s@kreditbee.in
Join our dynamic team at KreditBee, one of Indiaโs fastest-growing fintech platforms!
Location: Bangalore (Work from Office, 5 days a week)
Qualification: B.E / B.Tech in Computer Science
Experience: 2 to 6 years
Key Skills: Python, SQL, AWS, Credit risk modeling
Domain: Fintech background preferred
Notice Period: Immediate joiners or up to 30 days
Note: Finance Knowledge for model building
If youโre interested, please share your CV at
deekshitha.s@kreditbee.in
โค2