Hey hustlers, I hope you're doing well and welcome to 𝗛𝘂𝘀𝘁𝗹𝗲𝗿𝘀 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝘀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 challenge.

So today is day 0 and I'm going to share what this challenge is all about and what things is going to cover.

So for this challenge, we are going to cover all skills and tools required for a data analyst role from scratch to the advanced level, with a focus on a focus on hands-on practice and some small-scale projects also going to be covered.
 
In this challenge, as I already told you all, we are going to work not only on the learning part but also on making us mentally, emotionally, and physically stronger over time.
 
Today is just the introduction of this challenge. Nothing much for the learning side, but for this challenge, I'll try my best to give you as many free learning resources as possible so you don't have to spend more money on anything.
 
But for this challenge, there are a few things that are needed, which are either a laptop or a desktop with Internet connectivity. These two are the most important, and if you have only a phone with internet connectivity, then that's that's also okay, because I can understand that not all of us can have these resources.
 
The other part of challenge is how we are going to work on ourselves to make us mentally, emotionally, and physically stronger. So for this there are things we have to do every single day, and I know this will not be as easy as it seems right now, but over time you'll be able to do it without any excuses because we're hustlers who don't give excuses, right??
 
Things apart from learning we have to do are:
1. Taking enough amount sleep of 6–8 hours daily

2. Eating only healthy and home-made food and trying to avoid junk food as much as possible to eliminate sugary and packaged food

3. Daily 30 minutes of exercise. Here, you don't need to join any gym; you can do just walking and some yoga to make us stronger. 

4. Drinking 3–4 litres of water

5. At least 2 minutes of deep breathing will make us calm and focus on only positive things. This will eliminate all your anxiety and negativity.

6. Try to give a minimum 1 hour for learning new skills, which can be helpful for getting a better job or making money.
 
You can add extra things as per your needs.


I hope you're with me for this challenge, right? Show your support and enthusiasm for reacting to this message.

Cognizant is hiring!
Position: Business Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 5 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore; Hyderabad, India (Hybrid)

📌Apply Now: https://careers.cognizant.com/global-en/jobs/00058279963/business-analyst-cards-and-payments-iso8583-product-management/

https://careers.cognizant.com/india-en/jobs/00058304611/business-analyst-payments-domain/


WM is hiring for Junior Analyst, Process
Expected Salary: 3-4 LPA
Apply here:
https://emcm.fa.us2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/WMCareers/job/2320463?src=SNS-10005


Associate Analyst - Client Data Management - USI - - 187118
https://usijobs.deloitte.com/careersUSI/JobDetail/USI-EH-FY25-EA-MF-AU-FSS-Client-Data-Management-Associate-Analyst/187118#


Genpact is hiring Freshers!
Position: Associate, Data Analyst
Qualification: Bachelor's/ Master's/ MBA
Salary: 5 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore/ Gurugram

📌Apply Now: https://genpact.taleo.net/careersection/sgy_external_career_section/jobdetail.ftl?src=DS-10971&job=1357260

https://genpact.taleo.net/careersection/sgy_external_career_section/jobdetail.ftl?src=DS-10971&job=1383219


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👉Telegram Link: https://t.me/dataanalyticsbuddy

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All the best 👍👍

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀🔥🔥🔥

https://www.linkedin.com/posts/mandarmpatil_machinelearning-ml2024-careergrowth-activity-7214817787301548032-ZDhG

Hey hustlers welcome to 𝗛𝘂𝘀𝘁𝗹𝗲𝗿𝘀 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝘀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 challenge.

Today is day 1 and today's challenge is all about learning what is Data Analyst? what Data Analyst actually does? What Skills and tools are required for getting into Data Analytics domain.

As Data Analyst we should have these skills:

Programming: Python(recommended), R(Optional if you choose Python)

Data Base: SQL

Data Visualization: Microsoft Power BI(Mostly recommended in Industry) or Tableau

Microsoft Excel

Communication skills (the most important of all)


So I'm sharing YouTube Tutorials for Understanding about Data Analytics, Roadmap so that you can go through it and get overview what we going to learn in this challenge

Hope you'll find interesting and don't forget to work on your health which is another essential part of this challenge

Do watch below tutorials(If you prefer English then do watch with Subtitles)

Data Analyst Roadmap: https://youtu.be/bCLBdxfe57o?si=Xecrxmijsd9NN37y

https://youtu.be/ZFZc8-PugcA?si=GqT8Mb4EZXttwr04

We are going to covering these topics in this challenge

Do checkout this detail Data Analyst Roadmap. Here you'll dedicated timing is given for each tool and skills

Hey everybody, whenever we're sharing any learning resources, job openings, or anything in our community, try to show your support by reacting to messages so that I'll get an understanding people are applying for various opportunities and are using resources. 
 
Also, try to show your support on LinkedIn posts too, because whatever I'm sharing is only visible to people who know about me, and others don't get resources. But if you show your support, you're not only helping yourself but others too. Because of your support, things will reach out to many people, and you're also making your active presence on LinkedIn. Sometimes job hiring might get closed if you don't apply on time, but if you're supporting, then you can get more opportunities for sure. 

So from going forward try to show your support by reacting to each things that are shared in the community by either 👍, 👏, 🔥, ❤

𝗙𝗿𝗲𝘀𝗵𝗲𝗿𝘀 𝗵𝗶𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 🔥🔥🔥

https://www.linkedin.com/posts/mandarmpatil_interested-dataanalyst-datascience-activity-7214983924681216000-zmqn

Gartner is hiring Data Analysts to join their Data Modelling & Analytics team

Location: Gurgaon
Experience: 6 months to 2 years of experience.
Key Skills: Advanced excel, Python, SQL and Power BI.

You can go through the detailed JD and apply on the link:
https://gartner.wd5.myworkdayjobs.com/EXT/job/Gurgaon/Operations-Specialist--Advanced-excel--Python--SQL---Power-BI-required-_89996



https://www.linkedin.com/posts/pankhuri-mohan_operations-specialist-advanced-excel-python-activity-7214867669043269633-o9kq

Data Analyst hiring Freshers 🔥

1. American Express Analyst-Data Science
Exp: 0-3 Year
https://www.linkedin.com/jobs/view/3962967350

2. Cognizant is hiring Business Analyst
Experience: Freshers/ Experienced
https://careers.cognizant.com/india-en/jobs/00058304611/business-analyst-payments-domain/

3. Genpact is hiring Associate, Data Analyst
Experience: Freshers/ Experienced
https://genpact.taleo.net/careersection/sgy_external_career_section/jobdetail.ftl?src=DS-10971&job=1357260

https://genpact.taleo.net/careersection/sgy_external_career_section/jobdetail.ftl?src=DS-10971&job=1383219

4. WM is hiring for Junior Analyst
https://emcm.fa.us2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/WMCareers/job/2320463

5. Mika Tech Data Scientist (Remote)
Exp: Fresher
https://internshala.com/job/details/fresher-remote-data-scientist-job-at-mika-tech1720071765

WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46

Telegram Link: https://t.me/dataanalyticsbuddy


6. Sunflow Technology Data Analytics Executive (Remote)
Exp: 0-1 Year
https://internshala.com/job/details/fresher-remote-part-time-data-analytics-executive-job-at-sunflow-technology1720089674

7. Money Honey Financial Services Private Limited Business Analyst
Exp: 0-1 Year
https://internshala.com/job/details/fresher-business-analyst-job-in-mumbai-at-money-honey-financial-services-private-limited1719903843

8. Sunflow Technology Big Data Executive
Exp: 0-1 Year
https://internshala.com/job/details/fresher-remote-part-time-big-data-executive-job-at-sunflow-technology1719829655

9. Systemic Altruism AI Engineer (Remote)
Exp: Fresher
https://internshala.com/job/details/fresher-remote-ai-engineer-job-at-systemic-altruism1719982599

10. Nestlima Canada Data Scientist (Remote)
Exp: 0-4 Year
https://internshala.com/job/details/remote-data-scientist-job-at-nestlima-canada1719810697

11. Haptik Data Analyst / Data Scientist
Exp: Fresher
https://www.linkedin.com/jobs/view/3964358871


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👉Telegram Link: https://t.me/dataanalyticsbuddy

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All the best 👍👍

Hiring for: BI Developer-
Experience - 2-5 Years
Locations - Indore, Noida (WFO)
Primary Skill - Any BI Tool (Tableau, Qlik, Power BI) + SQL + TSQL + ETL

Immediate joiners only, please share resumes at "aditi.jain@intellicus.com

Shiprocket is seeking talented Data Scientists and Sr. Data Scientists to drive impactful insights and AI solutions.

Do you have a passion for solving complex problems and turning data into actionable strategies? This is your chance to join a dynamic, fast-growing company that is revolutionizing the commerce logistics industry.

📍 Location: Gurgaon (WFO)
🔍 Role: Data Scientist (2-3 years), Sr. Data Scientist (3+ years)

📧 Interested? Reach out to adit.mishra@shiprocket.com to apply and be a part of our innovative team!

Blinkit is hiring for Data Analyst
Expected Salary: 15-25 LPA
Apply here:
https://linkedin.com/jobs/view/3967861517/?alternateChannel=search

Goldman Sachs Graduate Hiring Program 2024-25
Job Role: Graduate Trainee
Qualifications: Non-engineering Bachelor’s Degree
Salary: Up to 16 LPA (Expected)
Batch: 2024/ 2025/ 2026
Experience: Freshers
Location: Bengaluru; Hyderabad, India

📌Apply Now: https://www.goldmansachs.com/careers/students/programs/india/graduate-campus-hiring-program.html




👉WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46

👉Telegram Link: https://t.me/dataanalyticsbuddy

Like for more ❤️

All the best 👍👍

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀🔥🔥🔥

https://www.linkedin.com/posts/mandarmpatil_machine-learning-interview-questions-activity-7215180165813792768-tYVJ

Machine Learning Interview Questions🔥🔥🔥

React ❤️ if you want more content like this

Machine Learning Interview Questions for Entry-Level Machine Learning Engineers

1. What is Machine Learning?
Explain the basic concept and types of machine learning (supervised, unsupervised, and reinforcement learning).

2. Difference between AI, Machine Learning, and Deep Learning?
Define each term and describe how they are related to each other.

3. What is Overfitting and Underfitting?
Describe the concepts and how to address them.

4. Explain the Bias-Variance Tradeoff.
Discuss the tradeoff and its significance in model performance.

5. What is Cross-Validation?
Explain different cross-validation techniques and their importance.

6. Describe the process of Feature Engineering.
Discuss techniques used to create and select features for a model.

7. What are Confusion Matrix and its components?
Define and explain precision, recall, F1 score, and accuracy.

8. Explain Regularization and its types.
Discuss L1 (Lasso) and L2 (Ridge) regularization.

9. What is Gradient Descent?
Describe the algorithm and its variants (batch, stochastic, mini-batch).

10. Difference between Classification and Regression?
Provide examples of each type of problem.

11. Explain Principal Component Analysis (PCA).
Discuss how PCA is used for dimensionality reduction.

12. What is a Support Vector Machine (SVM)?
Describe the algorithm and its applications.

13. Explain K-Nearest Neighbors (KNN).
Discuss how KNN works and its pros and cons.

14. Describe Decision Trees and Random Forests.
Explain the algorithms and how Random Forests improve performance.

15. What is a Neural Network?
Define the basic components (neurons, layers, activation functions).

16. Difference between Bagging and Boosting?
Describe each technique and their use cases.

17. Explain Hyperparameter Tuning.
Discuss methods such as grid search and random search.

18. What are Ensemble Methods?
Explain how ensemble methods like stacking and voting improve model performance.

19. What is a ROC Curve?
Describe how it is used to evaluate model performance.

20. Explain the concept of Clustering.
Discuss different clustering algorithms (K-means, hierarchical, DBSCAN).

21. What are Recommender Systems?
Explain the types and how they work.

22. How do you handle Missing Data?
Describe different strategies for dealing with missing values.

23. What is a Time Series Forecasting?
Explain how time series data is different and the techniques used for forecasting.

24. What are Autoencoders?
Describe their architecture and use cases.

25. Explain the term 'Feature Scaling'.
Discuss different methods of scaling features.

26. What is a Confusion Matrix?
Describe its importance in evaluating classification models.

27. Difference between Parametric and Non-Parametric Models?
Define each and provide examples.

28. What are ROC and AUC?
Explain how they are used to measure model performance.

29. Describe Transfer Learning.
Explain how pre-trained models are used in transfer learning.

30. What is an Outlier?
Discuss how to detect and handle outliers in a dataset.

Practical Scenario-Based Machine Learning Interview Questions for Entry-Level Machine Learning Engineers

1. Scenario: Customer Churn Prediction

How would you approach building a model to predict customer churn for a subscription-based service?
What features would you consider, and how would you handle imbalanced classes?

2. Scenario: Recommendation System

Describe how you would design a recommendation system for an e-commerce platform.
What algorithms might you use, and how would you evaluate the performance of your recommendation system?

3. Scenario: Fraud Detection

How would you build a model to detect fraudulent transactions in a financial dataset?
What techniques would you use to handle class imbalance and feature selection?

4. Scenario: Image Classification

Explain how you would create a model to classify images into different categories.
What pre-processing steps would you take, and how would you handle overfitting in your model?

5. Scenario: Sentiment Analysis

How would you build a sentiment analysis model for customer reviews?
Describe the process of text preprocessing and the choice of algorithms for this task.

6. Scenario: Predictive Maintenance

Discuss how you would develop a predictive maintenance model for industrial machinery.
What types of data would you collect, and how would you implement and evaluate your model?

7. Scenario: Time Series Forecasting

How would you approach forecasting sales data for the next quarter?
What models are suitable for time series forecasting, and how would you handle seasonality and trends?

8. Scenario: Anomaly Detection

Describe how you would build an anomaly detection system for network security.
What techniques would you use, and how would you evaluate the effectiveness of your system?

9. Scenario: Natural Language Processing

How would you build a chatbot that can handle customer service inquiries?
What NLP techniques would you use, and how would you ensure the chatbot understands and responds accurately?

10. Scenario: Feature Engineering

Given a dataset with various features, how would you approach feature engineering to improve model performance?
Provide examples of techniques you would use for feature creation and selection.

11. Scenario: Model Deployment

After training a machine learning model, how would you deploy it into a production environment?
What steps would you take to monitor and maintain the model’s performance?

12. Scenario: Model Interpretability

How would you ensure that your machine learning model is interpretable and explainable to stakeholders?
Discuss techniques and tools you would use for model interpretability.

13. Scenario: Hyperparameter Tuning

Explain how you would perform hyperparameter tuning for a machine learning model.
What methods would you use, and how would you validate the results?

14. Scenario: Handling Missing Data

In a dataset with missing values, how would you handle the missing data before building a model?
Discuss different strategies and their impact on model performance.

15. Scenario: Model Evaluation

How would you evaluate the performance of a machine learning model in a real-world scenario?
Discuss different evaluation metrics and their relevance to specific problems.

Data Science and Data Analst Job Hiring Updates

Company: AKS ProTalent
Role: Data or Business Analyst
Exp: Fresher
Apply Now: https://lnkd.in/dh2fTVtp

Company: ConTecHub
Role: Data Analyst
Exp: Fresher
Apply Now: https://lnkd.in/dZyYHNKv

Company: Birdeye Australia
Role: Data Scientist
Exp: Fresher
Apply Now: https://lnkd.in/dsgrVRw2

Company: FieldAssist
Role: Data Analyst (PowerBI)
Exp: Fresher
Apply Now: https://lnkd.in/dY9ydz7N

WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46

Company: Consulting And Beyond
Role: Junior Data Analyst
Exp: 0-1 yr
Apply Now: https://lnkd.in/d9BePWDc

Company: IDFC FIRST Bharat
Role: Data Analyst
Exp: 0-2 yrs
Apply Now: https://lnkd.in/dkSqAXcm

Telegram Link: https://t.me/dataanalyticsbuddy


Company: AtliQ Technologies
Role: Data Scientist
Exp: 1-3 yrs
Apply Now: https://lnkd.in/dp7f_2FT

Company: Black Piano
Role: Data Engineer
Exp: 1+ yrs
Apply Now: https://lnkd.in/dDJYwBWZ

Company: ProcDNA
Role: Data Engineer
Exp: 2+ yrs
Apply Now: https://lnkd.in/ddcrFztY

Company – Ai Unika
Role – Artificial Intelligence
Engineer Exp. – 2 yr
Apply Here – https://lnkd.in/d9xqQwkq



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👉Telegram Link: https://t.me/dataanalyticsbuddy

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All the best 👍👍