Top 5 data analysis interview questions with answers 😄👇
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 👍❤️
Share with credits: https://news.1rj.ru/str/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 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
🚀𝗧𝗼𝗽 𝟯 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲-𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍
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✅ Future You Will Thank You!
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✅ Future You Will Thank You!
❤1
📢 We’re Hiring: Senior Data Scientist (SDS) | 2.5–3.5 Years of Experience
We’re looking for high-caliber Senior Data Scientists to join our team at Sigmoid Analytics — 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!
✅ What We’re Looking For:
2.5–3.5 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
🎓 From Tier-1 / Tier-2 Engineering Colleges only
📍 Location: Whitefield, Bangalore
🚫 Note: Candidates who interviewed with Sigmoid in the last 6 months are not eligible.
📬 To Apply:
Email your CV and the following details to anu.s@sigmoidanalytics.com
Years of experience
Current CTC
Expected CTC
Notice period
We’re looking for high-caliber Senior Data Scientists to join our team at Sigmoid Analytics — 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!
✅ What We’re Looking For:
2.5–3.5 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
🎓 From Tier-1 / Tier-2 Engineering Colleges only
📍 Location: Whitefield, Bangalore
🚫 Note: Candidates who interviewed with Sigmoid in the last 6 months are not eligible.
📬 To Apply:
Email your CV and the following details to anu.s@sigmoidanalytics.com
Years of experience
Current CTC
Expected CTC
Notice period
❤4👎1
Forwarded from Python for Data Analysts
𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝟯𝟬-𝗗𝗮𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍
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Meet 30 Days of Data Science — a free and beginner-friendly GitHub repository that guides you through the core fundamentals of data science in just one month🧑🎓📌
𝐋𝐢𝐧𝐤👇:-
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Simply bookmark the page, pick Day 1, and begin your journey✅️
📊 If I had to restart my Data Science journey in 2025, this is where I’d begin✨️
Meet 30 Days of Data Science — a free and beginner-friendly GitHub repository that guides you through the core fundamentals of data science in just one month🧑🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4mfNdXR
Simply bookmark the page, pick Day 1, and begin your journey✅️
❤1
Forwarded from Python for Data Analysts
𝟳 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗠𝗮𝘀𝘁𝗲𝗿😍
If you’re serious about becoming a data analyst, there’s no skipping SQL. It’s not just another technical skill — it’s the core language for data analytics.📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learn✅️
If you’re serious about becoming a data analyst, there’s no skipping SQL. It’s not just another technical skill — it’s the core language for data analytics.📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learn✅️
❤1
Some essential concepts every data scientist should understand:
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Denoscriptive 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 👍👍
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Denoscriptive 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 👍👍
❤2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗧𝗼𝗽 𝗝𝗼𝗯𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Start your journey with this FREE Generative AI course offered by Microsoft and LinkedIn🧑🎓✨️
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❤2
Struggling with Machine Learning algorithms? 🤖
Then you better stay with me! 🤓
We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! 👇🏻
1️⃣ 𝗟𝗢𝗚𝗜𝗦𝗧𝗜𝗖 𝗥𝗘𝗚𝗥𝗘𝗦𝗦𝗜𝗢𝗡
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.
2️⃣ 𝗛𝗢𝗪 𝗧𝗢 𝗖𝗢𝗠𝗣𝗨𝗧𝗘 𝗜𝗧?
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.
3️⃣ 𝗛𝗢𝗪 𝗧𝗢 𝗗𝗘𝗙𝗜𝗡𝗘 𝗧𝗛𝗘 𝗕𝗘𝗦𝗧 𝗙𝗜𝗧?
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! 😉)
✚ 𝗕𝗢𝗡𝗨𝗦 - 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! 🤓
We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! 👇🏻
1️⃣ 𝗟𝗢𝗚𝗜𝗦𝗧𝗜𝗖 𝗥𝗘𝗚𝗥𝗘𝗦𝗦𝗜𝗢𝗡
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.
2️⃣ 𝗛𝗢𝗪 𝗧𝗢 𝗖𝗢𝗠𝗣𝗨𝗧𝗘 𝗜𝗧?
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.
3️⃣ 𝗛𝗢𝗪 𝗧𝗢 𝗗𝗘𝗙𝗜𝗡𝗘 𝗧𝗛𝗘 𝗕𝗘𝗦𝗧 𝗙𝗜𝗧?
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! 😉)
✚ 𝗕𝗢𝗡𝗨𝗦 - FROM LINEAR TO LOGISTIC REGRESSION
To obtain the sigmoid function, we can derive it from the Linear Regression equation.
❤1
American Express is hiring Analyst 🚀
Min. Experience : 1 Year
Location : Gurugram
Apply link : https://aexp.eightfold.ai/careers/job/30504056?hl=en&utm_source=linkedin&domain=aexp.com
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Min. Experience : 1 Year
Location : Gurugram
Apply link : https://aexp.eightfold.ai/careers/job/30504056?hl=en&utm_source=linkedin&domain=aexp.com
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
👍1
Gartner is hiring Associate Data Scientist 🚀
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
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
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
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
👍1
Dell Technologies is hiring!
Position: Data Scientist
Qualifications: Bachelor’s/ Master's Degree/ PhD
Salary: 7 - 19 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore/ Hyderabad
📌Apply Now: https://jobs.dell.com/en/job/bengaluru/data-scientist/375/84532530736
👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉 Telegram Channel: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best! 👍👍
Position: Data Scientist
Qualifications: Bachelor’s/ Master's Degree/ PhD
Salary: 7 - 19 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore/ Hyderabad
📌Apply Now: https://jobs.dell.com/en/job/bengaluru/data-scientist/375/84532530736
👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉 Telegram Channel: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best! 👍👍
❤3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟲 𝗙𝗿𝗲𝗲 𝗙𝘂𝗹𝗹 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗪𝗮𝘁𝗰𝗵 𝗥𝗶𝗴𝗵𝘁 𝗡𝗼𝘄😍
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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 knowledge📚🧑🎓
Whether you want to code in Python, hack ethically, or build your first Android app — these videos are your shortcut to real tech skills📱💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42V73k4
Save this list and start crushing your tech goals today!✅️
❤1
EY is hiring!
Position: Data Analytics - Associate
Qualifications: Bachelor's/ Master's Degree
Salary: 6 - 13 LPA (Expected)
Experience: Freshers/ Experienced
Location: Across India
📌Apply Now: https://careers.ey.com/ey/job/Kochi-Reporting-and-Data-Analytics-Associate-KL-682303/1232867801/
👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉 Telegram Channel: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best! 👍👍
Position: Data Analytics - Associate
Qualifications: Bachelor's/ Master's Degree
Salary: 6 - 13 LPA (Expected)
Experience: Freshers/ Experienced
Location: Across India
📌Apply Now: https://careers.ey.com/ey/job/Kochi-Reporting-and-Data-Analytics-Associate-KL-682303/1232867801/
👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉 Telegram Channel: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best! 👍👍
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Role: Tableau + SQL Developer
📍 Location: Bangalore / Hyderabad
🧠 Experience: 3 – 7 Years
🔑 Skills:
Strong expertise in Tableau and SQL
🔄 Good to have: Experience in Tableau to Power BI migration
🏢 Preferred: Candidates from Tier-1 companies
If you're looking to work on impactful projects and be part of a high-performance team — we want to hear from you!
Please share your Resume to divyam@psrtek.com
📍 Location: Bangalore / Hyderabad
🧠 Experience: 3 – 7 Years
🔑 Skills:
Strong expertise in Tableau and SQL
🔄 Good to have: Experience in Tableau to Power BI migration
🏢 Preferred: Candidates from Tier-1 companies
If you're looking to work on impactful projects and be part of a high-performance team — we want to hear from you!
Please share your Resume to divyam@psrtek.com
❤1
Forwarded from Python for Data Analysts
𝟯 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to earn free certificates and badges from Microsoft? 🚀
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❤1
Hiring: Data Scientist & Senior Data Scientist 🚀
Join our dynamic team at KreditBee, one of India’s 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 you’re interested, please share your CV at
deekshitha.s@kreditbee.in
Join our dynamic team at KreditBee, one of India’s 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 you’re interested, please share your CV at
deekshitha.s@kreditbee.in
❤2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟲 𝗙𝗿𝗲𝗲 𝗙𝘂𝗹𝗹 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗪𝗮𝘁𝗰𝗵 𝗥𝗶𝗴𝗵𝘁 𝗡𝗼𝘄😍
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 knowledge📚🧑🎓
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Save this list and start crushing your tech goals today!✅️
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 knowledge📚🧑🎓
Whether you want to code in Python, hack ethically, or build your first Android app — these videos are your shortcut to real tech skills📱💻
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❤2
🚀 We’re Hiring: Senior Data Engineer | Remote | First American (India)
Do you thrive on building scalable data platforms and solving complex data challenges ?
Join First American (India) as a Senior Data Engineer and play a key role in:
🔹 Operationalizing our Databricks Lakehouse on Azure
🔹 Designing robust data pipelines using Python, Scala, Java, or C#
🔹 Working with tools like Spark, Kafka, Airflow, and more
🔹 Collaborating with cross-functional teams of analysts and engineers
🔹 Ensuring high data quality, consistency, and observability
We’re looking for someone with:
✅ 7+ years in data engineering
✅ Experience with Databricks, Snowflake, Big Query, Hive, and Spark
✅ Knowledge of containerized environments like Docker or Kubernetes
✅ A strong grasp of data orchestration tools like Apache Airflow
🔗 Apply Now or Reach Out to Me Directly!
https://firstam.wd1.myworkdayjobs.com/faicareers/job/IND-Karnataka-Bangalore/Data-Engineering--SSEII_R051271
Do you thrive on building scalable data platforms and solving complex data challenges ?
Join First American (India) as a Senior Data Engineer and play a key role in:
🔹 Operationalizing our Databricks Lakehouse on Azure
🔹 Designing robust data pipelines using Python, Scala, Java, or C#
🔹 Working with tools like Spark, Kafka, Airflow, and more
🔹 Collaborating with cross-functional teams of analysts and engineers
🔹 Ensuring high data quality, consistency, and observability
We’re looking for someone with:
✅ 7+ years in data engineering
✅ Experience with Databricks, Snowflake, Big Query, Hive, and Spark
✅ Knowledge of containerized environments like Docker or Kubernetes
✅ A strong grasp of data orchestration tools like Apache Airflow
🔗 Apply Now or Reach Out to Me Directly!
https://firstam.wd1.myworkdayjobs.com/faicareers/job/IND-Karnataka-Bangalore/Data-Engineering--SSEII_R051271
❤1