👉✔️Here are Data Analytics-related questions along with their answers:
1.Question: What is the purpose of exploratory data analysis (EDA)?
Answer: EDA is used to analyze and summarize data sets, often through visual methods, to understand patterns, relationships, and potential outliers.
2. Question: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data to discover patterns without explicit guidance.
3.Question: Explain the concept of normalization in the context of data preprocessing.
Answer: Normalization scales numeric features to a standard range, preventing certain features from dominating due to their larger scales.
4. Question: What is the purpose of a correlation coefficient in statistics?
Answer: A correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.
5. Question: What is the role of a decision tree in machine learning?
Answer: A decision tree is a predictive model that maps features to outcomes by recursively splitting data based on feature conditions.
6. Question: Define precision and recall in the context of classification models.
Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.
7. Question: What is the purpose of cross-validation in machine learning?
Answer: Cross-validation assesses a model's performance by dividing the dataset into multiple subsets, training the model on some, and testing it on others, helping to evaluate its generalization ability.
8. Question: Explain the concept of a data warehouse.
Answer: A data warehouse is a centralized repository that stores, integrates, and manages large volumes of data from different sources, providing a unified view for analysis and reporting.
9. Question: What is the difference between structured and unstructured data?
Answer: Structured data is organized and easily searchable (e.g., databases), while unstructured data lacks a predefined structure (e.g., text documents, images).
10. Question: What is clustering in machine learning?
Answer: Clustering is a technique that groups similar data points together based on certain features, helping to identify patterns or relationships within the data.
1.Question: What is the purpose of exploratory data analysis (EDA)?
Answer: EDA is used to analyze and summarize data sets, often through visual methods, to understand patterns, relationships, and potential outliers.
2. Question: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data to discover patterns without explicit guidance.
3.Question: Explain the concept of normalization in the context of data preprocessing.
Answer: Normalization scales numeric features to a standard range, preventing certain features from dominating due to their larger scales.
4. Question: What is the purpose of a correlation coefficient in statistics?
Answer: A correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.
5. Question: What is the role of a decision tree in machine learning?
Answer: A decision tree is a predictive model that maps features to outcomes by recursively splitting data based on feature conditions.
6. Question: Define precision and recall in the context of classification models.
Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.
7. Question: What is the purpose of cross-validation in machine learning?
Answer: Cross-validation assesses a model's performance by dividing the dataset into multiple subsets, training the model on some, and testing it on others, helping to evaluate its generalization ability.
8. Question: Explain the concept of a data warehouse.
Answer: A data warehouse is a centralized repository that stores, integrates, and manages large volumes of data from different sources, providing a unified view for analysis and reporting.
9. Question: What is the difference between structured and unstructured data?
Answer: Structured data is organized and easily searchable (e.g., databases), while unstructured data lacks a predefined structure (e.g., text documents, images).
10. Question: What is clustering in machine learning?
Answer: Clustering is a technique that groups similar data points together based on certain features, helping to identify patterns or relationships within the data.
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Pandas Cheatsheet ✅
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Can you use Chat GPT as a data analyst?
The answer to this question is yes, but you need to be cautious about using Chat GPT on the job (and even while learning analytics) for the following reasons.
1. Chat GPT gets things wrong. A lot.
If you use Chat GPT to write code, you better know that coding language extremely well, because you gotta be able to fact check and alter the response you get from Chat GPT.
For this reason, I would recommend staying away from Chat GPT when you’re learning SQL, Python, etc so you thoroughly learn the code without becoming dependent on AI.
2. You absolutely CANNOT paste company data into Chat GPT
As data analysts we work with highly confidential data that we must exercise great caution to protect.
For this reason, no matter how secure Chat GPT says it is, you must never paste company data into the application.
3. Some companies and bosses may not allow the use of Chat GPT
This is a reality in the world of tech and data since the avalanche of AI tools and features over the last couple years.
I’ve heard of some companies that block Chat GPT altogether, and some managers who advise against using it out of fears for security and other reasons.
Given all three of these reasons, feel free to play around with Chat GPT and AI and learn about them, but don’t become overly dependent on these tools.
The answer to this question is yes, but you need to be cautious about using Chat GPT on the job (and even while learning analytics) for the following reasons.
1. Chat GPT gets things wrong. A lot.
If you use Chat GPT to write code, you better know that coding language extremely well, because you gotta be able to fact check and alter the response you get from Chat GPT.
For this reason, I would recommend staying away from Chat GPT when you’re learning SQL, Python, etc so you thoroughly learn the code without becoming dependent on AI.
2. You absolutely CANNOT paste company data into Chat GPT
As data analysts we work with highly confidential data that we must exercise great caution to protect.
For this reason, no matter how secure Chat GPT says it is, you must never paste company data into the application.
3. Some companies and bosses may not allow the use of Chat GPT
This is a reality in the world of tech and data since the avalanche of AI tools and features over the last couple years.
I’ve heard of some companies that block Chat GPT altogether, and some managers who advise against using it out of fears for security and other reasons.
Given all three of these reasons, feel free to play around with Chat GPT and AI and learn about them, but don’t become overly dependent on these tools.
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Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.
◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
🗓️Week 2: Intermediate Data Analytics Skills
◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
🗓️Week 3: Advanced Techniques and Tools
◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
🗓️Week 4: Projects and Practice
◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.
◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
🗓️Week 2: Intermediate Data Analytics Skills
◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
🗓️Week 3: Advanced Techniques and Tools
◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
🗓️Week 4: Projects and Practice
◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://news.1rj.ru/str/sqlproject
ENJOY LEARNING 👍👍
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://news.1rj.ru/str/sqlproject
ENJOY LEARNING 👍👍
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Data Analysis Roadmap.pdf
1001.3 KB
Data Analysis Roadmap!
Don't know where to start your Data Analyst journey? Worry not! Here is a 3 month roadmap that coverts everything a beginner needs, with no prior coding experience!
This roadmap covers:
- Technical Skills: Step-by-step guides for Excel, BI tools (Power BI/Tableau), SQL, Python & Pandas
- Soft Skills: Tips for networking, LinkedIn optimization, and business fundamentals
- Assignments and Projects: Real-world applications each week to build your portfolio
- Interview Prep: Practical resources and mock projects to get you job-ready
If you’re ready to learn with structured weekly goals, free resources, and hands-on assignments, this roadmap is a great place to start!
Don't know where to start your Data Analyst journey? Worry not! Here is a 3 month roadmap that coverts everything a beginner needs, with no prior coding experience!
This roadmap covers:
- Technical Skills: Step-by-step guides for Excel, BI tools (Power BI/Tableau), SQL, Python & Pandas
- Soft Skills: Tips for networking, LinkedIn optimization, and business fundamentals
- Assignments and Projects: Real-world applications each week to build your portfolio
- Interview Prep: Practical resources and mock projects to get you job-ready
If you’re ready to learn with structured weekly goals, free resources, and hands-on assignments, this roadmap is a great place to start!
👍9
Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.
◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
🗓️Week 2: Intermediate Data Analytics Skills
◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
🗓️Week 3: Advanced Techniques and Tools
◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
🗓️Week 4: Projects and Practice
◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.
◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
🗓️Week 2: Intermediate Data Analytics Skills
◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
🗓️Week 3: Advanced Techniques and Tools
◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
🗓️Week 4: Projects and Practice
◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
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Udemy Free Courses with Certificate
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ENJOY LEARNING 👍👍
Free Courses with Certificate
Web Development
Data Science & Machine Learning
Programming books
Python Free Courses
Data Analytics
Ethical Hacking & Cyber Security
English Speaking & Communication
Excel
ChatGPT Hacks
SQL
Tableau & Power BI
Coding Projects
Data Science Projects
Jobs & Internship Opportunities
Coding Interviews
Udemy Free Courses with Certificate
Data Analyst Interview
Data Analyst Jobs
Python Interview
ChatGPT Hacks
ENJOY LEARNING 👍👍
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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
I have curated Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
I have curated Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
❤5👍1
Data analysis is a gateway to becoming a:
- Data Scientist
- Business Analyst
- Data Engineer
- BI Engineer
- Analytics Engineer
And many other roles.
Learning the skills doesn't close doors, if anything, it opens many more.
- Data Scientist
- Business Analyst
- Data Engineer
- BI Engineer
- Analytics Engineer
And many other roles.
Learning the skills doesn't close doors, if anything, it opens many more.
🔥1
Don't Limit Yourself to Just One Title, "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭" in Your Job Search!
Don't get caught up in the confines of a single job noscript! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative noscripts for data analyst roles to broaden your search horizons:
1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst
Remember, the right opportunity might be hiding behind a different noscript than you expect. Keep an open mind and explore all avenues in your job search journey!
Also, there might be fewer applicants for these roles as many don't search for noscripts other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.
You don't have to try all the noscripts, filter out based on your interests and skills!
After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉
I have curated best 80+ top-notch Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier
Hope this helps you 😊
Don't get caught up in the confines of a single job noscript! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative noscripts for data analyst roles to broaden your search horizons:
1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst
Remember, the right opportunity might be hiding behind a different noscript than you expect. Keep an open mind and explore all avenues in your job search journey!
Also, there might be fewer applicants for these roles as many don't search for noscripts other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.
You don't have to try all the noscripts, filter out based on your interests and skills!
After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉
I have curated best 80+ top-notch Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier
Hope this helps you 😊
👍11❤1👌1
The Real Truth About Junior Data Analytics Interviews DataAnalytics
(From someone who's interviewed 50+ analysts)
Let me save you hours of interview prep...
SQL Round
WHAT THEY SAY:
"Complex SQL knowledge"
WHAT THEY ACTUALLY TEST:
Can you clean messy data
Do you check for NULL values
How do you handle duplicates
Can you explain your logic
Do you verify results
REAL QUESTIONS:
"Find duplicate transactions"
"Calculate monthly sales"
"Show top customers"
That's it. Really. ⤵️
Excel Interview
WHAT THEY SAY:
"Advanced Excel skills"
WHAT THEY ACTUALLY TEST:
VLOOKUP/XLOOKUP usage
Pivot Table comfort
Basic formulas
Data cleaning approach
Problem-solving process
Business Case
WHAT THEY SAY:
"Data analysis presentation"
WHAT THEY REALLY WANT:
Can you explain simply
Do you ask good questions
Can you structure analysis
Do you focus on impact
Are you confident with data ⤵️
Common Scenarios
The "Messy Data" Test
They give you:
Inconsistent formats
Missing values
Duplicate records
They watch:
How you spot issues
What questions you ask
Your cleaning approach
The "Explain It" Challenge
They ask:
"Walk me through your analysis"
They assess:
Communication clarity
Technical understanding
Business thinking
Confidence level ⤵️
How to Actually Prepare
Practice Basics:
Simple SQL queries
Excel fundamentals
Clear explanation
Business Understanding:
Read company metrics
Understand industry
Know basic KPIs
Prepare good questions
Real Scenarios to Practice:
Monthly sales analysis
Customer segmentation
Product performance
Marketing campaign results
Reality Check:
They care more about:
How you think
How you communicate
How you solve problems
Than:
Perfect technical knowledge
Complex code
Advanced statistics
I have curated best 80+ top-notch Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for more content like this 👍♥️
(From someone who's interviewed 50+ analysts)
Let me save you hours of interview prep...
SQL Round
WHAT THEY SAY:
"Complex SQL knowledge"
WHAT THEY ACTUALLY TEST:
Can you clean messy data
Do you check for NULL values
How do you handle duplicates
Can you explain your logic
Do you verify results
REAL QUESTIONS:
"Find duplicate transactions"
"Calculate monthly sales"
"Show top customers"
That's it. Really. ⤵️
Excel Interview
WHAT THEY SAY:
"Advanced Excel skills"
WHAT THEY ACTUALLY TEST:
VLOOKUP/XLOOKUP usage
Pivot Table comfort
Basic formulas
Data cleaning approach
Problem-solving process
Business Case
WHAT THEY SAY:
"Data analysis presentation"
WHAT THEY REALLY WANT:
Can you explain simply
Do you ask good questions
Can you structure analysis
Do you focus on impact
Are you confident with data ⤵️
Common Scenarios
The "Messy Data" Test
They give you:
Inconsistent formats
Missing values
Duplicate records
They watch:
How you spot issues
What questions you ask
Your cleaning approach
The "Explain It" Challenge
They ask:
"Walk me through your analysis"
They assess:
Communication clarity
Technical understanding
Business thinking
Confidence level ⤵️
How to Actually Prepare
Practice Basics:
Simple SQL queries
Excel fundamentals
Clear explanation
Business Understanding:
Read company metrics
Understand industry
Know basic KPIs
Prepare good questions
Real Scenarios to Practice:
Monthly sales analysis
Customer segmentation
Product performance
Marketing campaign results
Reality Check:
They care more about:
How you think
How you communicate
How you solve problems
Than:
Perfect technical knowledge
Complex code
Advanced statistics
I have curated best 80+ top-notch Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for more content like this 👍♥️
👍9❤3
Your ultimate guide to data analytics jobs 👇👇
https://medium.com/write-a-catalyst/your-ultimate-guide-to-data-analytics-jobs-f7fd3d55844c?sk=3740c46ec74bbc8ef830c01e0df30a17
Like for more ❤️
https://medium.com/write-a-catalyst/your-ultimate-guide-to-data-analytics-jobs-f7fd3d55844c?sk=3740c46ec74bbc8ef830c01e0df30a17
Like for more ❤️
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