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 youโre 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.
Iโve 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 donโt 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 youโre 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.
Iโve 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 donโt become overly dependent on these tools.
๐ฅ2
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:
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 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.
โพDay 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.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 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.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 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.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 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.
๐Additional 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!
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 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.
โพDay 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.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 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.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 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.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 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.
๐Additional 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!
โค4๐2๐1
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://t.iss.one/sqlproject
ENJOY LEARNING ๐๐
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://t.iss.one/sqlproject
ENJOY LEARNING ๐๐
๐3
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 youโre 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 youโre ready to learn with structured weekly goals, free resources, and hands-on assignments, this roadmap is a great place to start!
๐9
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:
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 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.
โพDay 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.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 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.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 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.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 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.
๐Additional 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 LEARNING๐๐
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 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.
โพDay 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.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 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.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 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.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 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.
๐Additional 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 LEARNING๐๐
โค2๐2
Useful Telegram Channels to boost your career ๐๐
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 ๐๐
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 ๐๐
โค4๐4
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ
๐ญ. ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐: Master Python, SQL, and R for data manipulation and analysis.
๐ฎ. ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ถ๐ฝ๐๐น๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
๐ฏ. ๐๐ฎ๐๐ฎ ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
๐ฐ. ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐ ๐ฎ๐๐ต๐ฒ๐บ๐ฎ๐๐ถ๐ฐ๐: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
๐ฑ. ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
๐ฒ. ๐๐ถ๐ด ๐๐ฎ๐๐ฎ ๐ง๐ผ๐ผ๐น๐: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
๐ณ. ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
๐ด. ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ง๐ผ๐ผ๐น๐: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
๐ต. ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ: Manage resources using Jupyter Notebooks and Power BI.
๐ญ๐ฌ. ๐๐ฎ๐๐ฎ ๐๐ผ๐๐ฒ๐ฟ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐๐๐ต๐ถ๐ฐ๐: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
๐ญ๐ญ. ๐๐น๐ผ๐๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
๐ญ๐ฎ. ๐๐ฎ๐๐ฎ ๐ช๐ฟ๐ฎ๐ป๐ด๐น๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐น๐ฒ๐ฎ๐ป๐ถ๐ป๐ด: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
I have curated Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐ญ. ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐: Master Python, SQL, and R for data manipulation and analysis.
๐ฎ. ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ถ๐ฝ๐๐น๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
๐ฏ. ๐๐ฎ๐๐ฎ ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
๐ฐ. ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐ ๐ฎ๐๐ต๐ฒ๐บ๐ฎ๐๐ถ๐ฐ๐: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
๐ฑ. ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
๐ฒ. ๐๐ถ๐ด ๐๐ฎ๐๐ฎ ๐ง๐ผ๐ผ๐น๐: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
๐ณ. ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
๐ด. ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ง๐ผ๐ผ๐น๐: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
๐ต. ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ: Manage resources using Jupyter Notebooks and Power BI.
๐ญ๐ฌ. ๐๐ฎ๐๐ฎ ๐๐ผ๐๐ฒ๐ฟ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐๐๐ต๐ถ๐ฐ๐: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
๐ญ๐ญ. ๐๐น๐ผ๐๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
๐ญ๐ฎ. ๐๐ฎ๐๐ฎ ๐ช๐ฟ๐ฎ๐ป๐ด๐น๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐น๐ฒ๐ฎ๐ป๐ถ๐ป๐ด: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
I have curated Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โค5๐1
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.
๐ฅ1
Don't Limit Yourself to Just One Title, "๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ" in Your Job Search!
Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles 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 title 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 titles 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 titles, filter out based on your interests and skills!
After all, ๐๐จ๐ ๐๐๐ฌ๐๐ซ๐ข๐ฉ๐ญ๐ข๐จ๐ง ๐ฆ๐๐ญ๐ญ๐๐ซ๐ฌ ๐ฆ๐จ๐ซ๐ ๐ญ๐ก๐๐ง ๐ญ๐ก๐ ๐ญ๐ข๐ญ๐ฅ๐!! ๐
I have curated best 80+ top-notch Data Analytics Resources
๐๐
https://t.iss.one/DataSimplifier
Hope this helps you ๐
Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles 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 title 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 titles 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 titles, filter out based on your interests and skills!
After all, ๐๐จ๐ ๐๐๐ฌ๐๐ซ๐ข๐ฉ๐ญ๐ข๐จ๐ง ๐ฆ๐๐ญ๐ญ๐๐ซ๐ฌ ๐ฆ๐จ๐ซ๐ ๐ญ๐ก๐๐ง ๐ญ๐ก๐ ๐ญ๐ข๐ญ๐ฅ๐!! ๐
I have curated best 80+ top-notch Data Analytics Resources
๐๐
https://t.iss.one/DataSimplifier
Hope this helps you ๐
๐11โค1๐1
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. โคต๏ธ
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 โคต๏ธ
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 โคต๏ธ
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
๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
(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. โคต๏ธ
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 โคต๏ธ
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 โคต๏ธ
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
๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
๐9โค3
Your ultimate guide to data analytics jobs ๐๐
https://medium.com/write-a-catalyst/your-ultimate-guide-to-data-analytics-jobs-f7fd3d55844c?sk=3740c46ec74bbc8ef830c01e0df30a17
Like for more โค๏ธ
https://medium.com/write-a-catalyst/your-ultimate-guide-to-data-analytics-jobs-f7fd3d55844c?sk=3740c46ec74bbc8ef830c01e0df30a17
Like for more โค๏ธ
โค2๐2๐1
This is a very COMMON issue that I observe in the projects of aspiring candidates
They download a DATASET from Kaggle or any other website
Export it to a Data Analysis TOOL
And START the project with data cleaning
After cleaning the data, they PLUG it into a dashboard
In the dashboard, they put EVERY column into the visuals
Also they APPLY the filters of top/bottom 10
Once done, they crack their KNUCKLES
And put this project in a list of SUCCESSFULLY completed projects
Over time, I have REVIEWED so many portfolio projects
And I see this ISSUES almost every time
When I go to their portfolio, for every project there is a DASHBOARD
But WHAT should I do after seeing a dashboard?
What is it trying to SAY?
What should I do after SEEING top or bottom 10 cities, states or products?
Every dashboard lacks CONTEXT
And why NOT?
Because they DON'T even know the business problem or problem statement
So the dashboard you created is of NO use
Your job is not just to create DASHBOARDS
Your job would be to create DASHBOARDS to take out important INSIGHTS
And from those insights, you will build RECOMMENDATIONS
And these recommendations will be given to stakeholders as a SOLUTION to their business problem
If they implemented your IDEAS and the problem gets solved
Now you can say your work is DONE
If you are SHOWING bottom 10 states, then what?
You should write the INSIGHTS too
For example, the sales of North India zone are FALLING
The insights can be used like this
Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states
And this might be the REASON why our North India sales are DROPPING so hard
This is just a RANDOM example showing how your charts become UNDERSTANDABLE
Well, everyone can EXTRACT insights from charts
Even a KID can do this after looking at the tallest and smallest bar
The real task is to give RECOMMENDATIONS to solve the BUSINESS problem
And I have NEVER seen this in anyone's portfolio
If you are doing this, then you are easily STANDING out in the crowd
In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations
Even in the bullet point of projects in my resume, I included RECOMMENDATIONS
Now this is what you can call a STRONG portfolio
Because your analysis skills are the SAME as those used in the real life by a Data Analyst
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like if it helps ๐
They download a DATASET from Kaggle or any other website
Export it to a Data Analysis TOOL
And START the project with data cleaning
After cleaning the data, they PLUG it into a dashboard
In the dashboard, they put EVERY column into the visuals
Also they APPLY the filters of top/bottom 10
Once done, they crack their KNUCKLES
And put this project in a list of SUCCESSFULLY completed projects
Over time, I have REVIEWED so many portfolio projects
And I see this ISSUES almost every time
When I go to their portfolio, for every project there is a DASHBOARD
But WHAT should I do after seeing a dashboard?
What is it trying to SAY?
What should I do after SEEING top or bottom 10 cities, states or products?
Every dashboard lacks CONTEXT
And why NOT?
Because they DON'T even know the business problem or problem statement
So the dashboard you created is of NO use
Your job is not just to create DASHBOARDS
Your job would be to create DASHBOARDS to take out important INSIGHTS
And from those insights, you will build RECOMMENDATIONS
And these recommendations will be given to stakeholders as a SOLUTION to their business problem
If they implemented your IDEAS and the problem gets solved
Now you can say your work is DONE
If you are SHOWING bottom 10 states, then what?
You should write the INSIGHTS too
For example, the sales of North India zone are FALLING
The insights can be used like this
Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states
And this might be the REASON why our North India sales are DROPPING so hard
This is just a RANDOM example showing how your charts become UNDERSTANDABLE
Well, everyone can EXTRACT insights from charts
Even a KID can do this after looking at the tallest and smallest bar
The real task is to give RECOMMENDATIONS to solve the BUSINESS problem
And I have NEVER seen this in anyone's portfolio
If you are doing this, then you are easily STANDING out in the crowd
In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations
Even in the bullet point of projects in my resume, I included RECOMMENDATIONS
Now this is what you can call a STRONG portfolio
Because your analysis skills are the SAME as those used in the real life by a Data Analyst
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like if it helps ๐
๐10โค1
Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:
โข Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.
โข Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.
โข Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.
But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.
No matter where your path leads, the key is to start now.
โข Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.
โข Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.
โข Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.
But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.
No matter where your path leads, the key is to start now.
๐1
Breaking into Data Analysis can be very confusing in 2024!
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
โค9
Guide to Become a Data Analyst!
๐ Foundation: Build Your Basics
1. Understanding Data Fundamentals: Dive into the basics of data types, structures, and formats.
2. Learn Data Tools: Familiarize yourself with popular tools like Excel, SQL, and Python.
3. Master Data Visualization: Develop skills in creating insightful charts and graphs to communicate findings effectively.
4. Introduction to Statistics: Get comfortable with key statistical concepts like mean, median, and standard deviation.
๐ Intermediate: Deepen Your Skills
5. Advanced Data Manipulation: Level up your data wrangling abilities with techniques like pivot tables and data cleaning.
6. Statistical Analysis: Dive deeper into hypothesis testing, regression analysis, and probability distributions.
7. Machine Learning Basics: Explore the fundamentals of machine learning algorithms and their applications in data analysis.
8. Data Storytelling: Hone your ability to craft compelling narratives from data insights.
๐ Advanced: Specialize and Excel
9. Specialize in a Domain: Choose a niche area such as marketing analytics, financial analysis, or healthcare data.
10. Advanced Machine Learning: Deepen your understanding of complex algorithms like neural networks and ensemble methods.
11. Big Data Technologies: Explore tools and platforms for handling large-scale datasets such as Hadoop and Spark.
12. Ethics and Privacy: Understand the ethical considerations and legal implications of handling sensitive data.
๐ Foundation: Build Your Basics
1. Understanding Data Fundamentals: Dive into the basics of data types, structures, and formats.
2. Learn Data Tools: Familiarize yourself with popular tools like Excel, SQL, and Python.
3. Master Data Visualization: Develop skills in creating insightful charts and graphs to communicate findings effectively.
4. Introduction to Statistics: Get comfortable with key statistical concepts like mean, median, and standard deviation.
๐ Intermediate: Deepen Your Skills
5. Advanced Data Manipulation: Level up your data wrangling abilities with techniques like pivot tables and data cleaning.
6. Statistical Analysis: Dive deeper into hypothesis testing, regression analysis, and probability distributions.
7. Machine Learning Basics: Explore the fundamentals of machine learning algorithms and their applications in data analysis.
8. Data Storytelling: Hone your ability to craft compelling narratives from data insights.
๐ Advanced: Specialize and Excel
9. Specialize in a Domain: Choose a niche area such as marketing analytics, financial analysis, or healthcare data.
10. Advanced Machine Learning: Deepen your understanding of complex algorithms like neural networks and ensemble methods.
11. Big Data Technologies: Explore tools and platforms for handling large-scale datasets such as Hadoop and Spark.
12. Ethics and Privacy: Understand the ethical considerations and legal implications of handling sensitive data.
๐4
Learn these to become a
1. Data analyst:
๐Excel
๐SQL
๐Data viz tool (Power BI/Tableau)
2. Data engineer:
๐SQL
๐Python + Spark
๐Cloud platform (AWS/Azure/GCP)
3. Data scientist:
๐SQL
๐Python/R
๐Statistics/machine learning
1. Data analyst:
๐Excel
๐SQL
๐Data viz tool (Power BI/Tableau)
2. Data engineer:
๐SQL
๐Python + Spark
๐Cloud platform (AWS/Azure/GCP)
3. Data scientist:
๐SQL
๐Python/R
๐Statistics/machine learning
โค6๐ฅ1
Knowing Excel, SQL, PowerBI, Python is great.
But if you donโt know how to "sell" your analysis there's a high chance you'll fail.
Here's what to do:
- Come up with questions to investigate.
- Create easy-to-understand answers.
- Explain what to do next.
It's that simple.
But if you donโt know how to "sell" your analysis there's a high chance you'll fail.
Here's what to do:
- Come up with questions to investigate.
- Create easy-to-understand answers.
- Explain what to do next.
It's that simple.
๐4
โ๏ธ ๐๐จ๐๐๐ฆ๐๐ฉ ๐ญ๐จ ๐๐๐๐จ๐ฆ๐ข๐ง๐ ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ
๐. ๐๐ฑ๐๐๐ฅ: ๐๐จ๐ฎ๐ซ ๐๐จ๐ซ๐ ๐๐จ๐จ๐ฅ
Master Excel skills for effective data analysis by focusing on:
ใปCleaning and organizing data
ใปUsing pivot tables for summaries
ใปAdvanced functions like VLOOKUP, INDEX, and MATCH
ใปDesigning impactful visualizations
๐. ๐๐ฎ๐ข๐ฅ๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง
Statistics are essential for interpreting data. Learn:
ใปDescriptive statistics (mean, median, mode)
ใปProbability distributions
ใปHypothesis testing and confidence intervals
๐. ๐๐จ๐ฆ๐ข๐ง๐๐ญ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐จ๐ซ ๐
Choose Python or R to boost your analysis game:
ใปClean and structure datasets
ใปCreate visualizations (Matplotlib, Seaborn, or Tidyverse)
ใปLeverage powerful libraries for in-depth analysis
๐. ๐๐๐ฌ๐ญ๐๐ซ ๐๐๐
SQL is vital for working with databases. Hone these skills:
ใปQuery writing for data extraction
ใปCombining data with JOINS
ใปUsing aggregate functions
ใปOptimizing query performance
๐. ๐๐ฑ๐๐๐ฅ ๐๐ญ ๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
Transform data into stories with tools like Power BI or Tableau:
ใปBuild insightful dashboards
ใปCreate interactive visualizations
ใปCraft compelling, data-driven narratives
๐. ๐๐๐ซ๐๐๐๐ญ ๐๐๐ญ๐ ๐๐ฅ๐๐๐ง๐ข๐ง๐
Data cleaning ensures accurate results. Learn to:
ใปHandle missing values
ใปDetect and manage outliers
ใปNormalize and format data for analysis
๐. ๐๐๐ญ ๐๐๐ง๐๐ฌ-๐๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฅ-๐๐จ๐ซ๐ฅ๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ
Practical experience is key! Work on:
ใปMarket or business data analysis
ใปFinancial or sales dashboards
ใปCustomer segmentation
๐. ๐๐ก๐๐ซ๐ฉ๐๐ง ๐๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
Translate data insights into actionable recommendations:
ใปWrite clear, concise reports
ใปPresent to non-technical audiences
ใปDeliver impactful, data-backed decisions
๐. ๐๐ฑ๐๐๐ฅ: ๐๐จ๐ฎ๐ซ ๐๐จ๐ซ๐ ๐๐จ๐จ๐ฅ
Master Excel skills for effective data analysis by focusing on:
ใปCleaning and organizing data
ใปUsing pivot tables for summaries
ใปAdvanced functions like VLOOKUP, INDEX, and MATCH
ใปDesigning impactful visualizations
๐. ๐๐ฎ๐ข๐ฅ๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง
Statistics are essential for interpreting data. Learn:
ใปDescriptive statistics (mean, median, mode)
ใปProbability distributions
ใปHypothesis testing and confidence intervals
๐. ๐๐จ๐ฆ๐ข๐ง๐๐ญ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐จ๐ซ ๐
Choose Python or R to boost your analysis game:
ใปClean and structure datasets
ใปCreate visualizations (Matplotlib, Seaborn, or Tidyverse)
ใปLeverage powerful libraries for in-depth analysis
๐. ๐๐๐ฌ๐ญ๐๐ซ ๐๐๐
SQL is vital for working with databases. Hone these skills:
ใปQuery writing for data extraction
ใปCombining data with JOINS
ใปUsing aggregate functions
ใปOptimizing query performance
๐. ๐๐ฑ๐๐๐ฅ ๐๐ญ ๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
Transform data into stories with tools like Power BI or Tableau:
ใปBuild insightful dashboards
ใปCreate interactive visualizations
ใปCraft compelling, data-driven narratives
๐. ๐๐๐ซ๐๐๐๐ญ ๐๐๐ญ๐ ๐๐ฅ๐๐๐ง๐ข๐ง๐
Data cleaning ensures accurate results. Learn to:
ใปHandle missing values
ใปDetect and manage outliers
ใปNormalize and format data for analysis
๐. ๐๐๐ญ ๐๐๐ง๐๐ฌ-๐๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฅ-๐๐จ๐ซ๐ฅ๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ
Practical experience is key! Work on:
ใปMarket or business data analysis
ใปFinancial or sales dashboards
ใปCustomer segmentation
๐. ๐๐ก๐๐ซ๐ฉ๐๐ง ๐๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
Translate data insights into actionable recommendations:
ใปWrite clear, concise reports
ใปPresent to non-technical audiences
ใปDeliver impactful, data-backed decisions
๐8โค2
Useful websites to practice and enhance your Data Analytics skills
๐๐
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.iss.one/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING ๐๐
๐๐
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.iss.one/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING ๐๐
๐6