10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
❤1
Forwarded from Artificial Intelligence
𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified🎓️
🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified🎓️
❤1
𝗙𝗥𝗘𝗘 𝗧𝗔𝗧𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽😍
Gain Real-World Data Analytics Experience with TATA – 100% Free!
This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst — no experience required!
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FyjDgp
Enroll For FREE & Get Certified🎓️
Gain Real-World Data Analytics Experience with TATA – 100% Free!
This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst — no experience required!
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FyjDgp
Enroll For FREE & Get Certified🎓️
❤1
5 misconceptions about data analytics (and what's actually true):
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
❤2
Forwarded from Power BI & Tableau Resources
𝟮𝟳 𝗥𝗲𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗟𝗶𝗸𝗲 𝗜𝗕𝗠, 𝗖𝗮𝗽𝗴𝗲𝗺𝗶𝗻𝗶 & 𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲😍
This blog brings you 27 real Power BI interview questions asked by top companies like IBM, Capgemini, Deloitte, and more🗣📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4dFem3o
Most important—interview questions✅️
This blog brings you 27 real Power BI interview questions asked by top companies like IBM, Capgemini, Deloitte, and more🗣📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4dFem3o
Most important—interview questions✅️
❤1
𝟴 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍
🎓 Learn Data Science for Free from the World’s Best Universities🚀
Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hfpwjc
All The Best 👍
🎓 Learn Data Science for Free from the World’s Best Universities🚀
Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hfpwjc
All The Best 👍
Power BI interview questions and answers 😄👇
1. Question: What is Power BI?
Answer: Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.
2. Question: Differentiate between Power BI Desktop, Power BI Service, and Power BI Mobile.
Answer: Power BI Desktop is used for creating reports, Power BI Service (or Power BI Online) is the cloud service for sharing and collaborating on reports, and Power BI Mobile allows users to access reports on mobile devices.
3. Question: Explain the role of Power Query in Power BI.
Answer: Power Query is used for data transformation and shaping. It allows users to connect to various data sources, clean and transform data before loading it into Power BI for analysis.
4. Question: What is DAX in Power BI, and why is it important?
Answer: DAX (Data Analysis Expressions) is a formula language used for creating custom calculations in Power BI. It is important as it enables users to create sophisticated measures and calculated columns.
5. Question: How do you create relationships between tables in Power BI?
Answer: In Power BI Desktop, go to the "Model" view, drag and drop fields from one table to another to create relationships based on common keys.
6. Question: What is the difference between a calculated column and a measure in Power BI?
Answer: A calculated column is a column added to a table, computed row by row, while a measure is a formula applied to a set of data, providing a dynamic calculation based on the context.
7. Question: How can you implement row-level security in Power BI?
Answer: Row-level security in Power BI can be implemented by creating roles in Power BI Desktop and defining filters at the row level based on user roles.
8. Question: Explain the purpose of the Power BI Gateway.
Answer: The Power BI Gateway allows for a secure connection between Power BI services and on-premises data sources. It facilitates refreshing datasets and running scheduled refreshes.
9. Question: What is a Power BI dashboard?
Answer: A Power BI dashboard is a single-page, interactive view of your data that provides a consolidated and visualized summary of key metrics. It can include visuals, images, and live data.
10. Question: How can you share a Power BI report with others?
Answer: Power BI reports can be shared through the Power BI service. Publish the report to the Power BI service, and then share it with specific users or distribute it widely within an organization.
1. Question: What is Power BI?
Answer: Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.
2. Question: Differentiate between Power BI Desktop, Power BI Service, and Power BI Mobile.
Answer: Power BI Desktop is used for creating reports, Power BI Service (or Power BI Online) is the cloud service for sharing and collaborating on reports, and Power BI Mobile allows users to access reports on mobile devices.
3. Question: Explain the role of Power Query in Power BI.
Answer: Power Query is used for data transformation and shaping. It allows users to connect to various data sources, clean and transform data before loading it into Power BI for analysis.
4. Question: What is DAX in Power BI, and why is it important?
Answer: DAX (Data Analysis Expressions) is a formula language used for creating custom calculations in Power BI. It is important as it enables users to create sophisticated measures and calculated columns.
5. Question: How do you create relationships between tables in Power BI?
Answer: In Power BI Desktop, go to the "Model" view, drag and drop fields from one table to another to create relationships based on common keys.
6. Question: What is the difference between a calculated column and a measure in Power BI?
Answer: A calculated column is a column added to a table, computed row by row, while a measure is a formula applied to a set of data, providing a dynamic calculation based on the context.
7. Question: How can you implement row-level security in Power BI?
Answer: Row-level security in Power BI can be implemented by creating roles in Power BI Desktop and defining filters at the row level based on user roles.
8. Question: Explain the purpose of the Power BI Gateway.
Answer: The Power BI Gateway allows for a secure connection between Power BI services and on-premises data sources. It facilitates refreshing datasets and running scheduled refreshes.
9. Question: What is a Power BI dashboard?
Answer: A Power BI dashboard is a single-page, interactive view of your data that provides a consolidated and visualized summary of key metrics. It can include visuals, images, and live data.
10. Question: How can you share a Power BI report with others?
Answer: Power BI reports can be shared through the Power BI service. Publish the report to the Power BI service, and then share it with specific users or distribute it widely within an organization.
❤1👍1
Forwarded from Python Projects & Resources
𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗼𝗮𝗱𝗺𝗮𝗽😍
🎯 Want to Master Data Science in Just 3 Months?📊
Feeling overwhelmed by the sheer volume of resources and don’t know where to start? You’re not alone🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/43uHPrX
This FREE GitHub roadmap is a game-changer for anyone✅️
🎯 Want to Master Data Science in Just 3 Months?📊
Feeling overwhelmed by the sheer volume of resources and don’t know where to start? You’re not alone🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/43uHPrX
This FREE GitHub roadmap is a game-changer for anyone✅️
Data Analyst vs Data Engineer vs Data Scientist ✅
Skills required to become a Data Analyst 👇
- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic noscripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.
Skills required to become a Data Engineer: 👇
- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.
Skills required to become a Data Scientist: 👇
- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.
Bonus Skills Across All Roles:
- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Skills required to become a Data Analyst 👇
- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic noscripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.
Skills required to become a Data Engineer: 👇
- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.
Skills required to become a Data Scientist: 👇
- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.
Bonus Skills Across All Roles:
- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤3
Forwarded from Artificial Intelligence
𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires 💫
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires 💫
30 Days Python Roadmap for Data Analysts 👆
❤3
𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to Boost Your Resume with In-Demand Python Skills?👨💻
In today’s tech-driven world, Python is one of the most in-demand programming languages across data science, software development, and machine learning📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hnx3wh
Enjoy Learning ✅️
Want to Boost Your Resume with In-Demand Python Skills?👨💻
In today’s tech-driven world, Python is one of the most in-demand programming languages across data science, software development, and machine learning📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hnx3wh
Enjoy Learning ✅️
Hey guys!
I’ve been getting a lot of requests from you all asking for solid Data Analytics projects that can help you boost resume and build real skills.
So here you go —
These aren’t just “for practice,” they’re portfolio-worthy projects that show recruiters you’re ready for real-world work.
1. Sales Performance Dashboard
Tools: Excel / Power BI / Tableau
You’ll take raw sales data and turn it into a clean, interactive dashboard. Show key metrics like revenue, profit, top products, and regional trends.
Skills you build: Data cleaning, slicing & filtering, dashboard creation, business storytelling.
2. Customer Churn Analysis
Tools: Python (Pandas, Seaborn)
Work with a telecom or SaaS dataset to identify which customers are likely to leave and why.
Skills you build: Exploratory data analysis, visualization, correlation, and basic machine learning.
3. E-commerce Product Insights using SQL
Tools: SQL + Power BI
Analyze product categories, top-selling items, and revenue trends from a sample e-commerce dataset.
Skills you build: Joins, GROUP BY, aggregation, data modeling, and visual storytelling.
4. HR Analytics Dashboard
Tools: Excel / Power BI
Dive into employee data to find patterns in attrition, hiring trends, average salaries by department, etc.
Skills you build: Data summarization, calculated fields, visual formatting, DAX basics.
5. Movie Trends Analysis (Netflix or IMDb Dataset)
Tools: Python (Pandas, Matplotlib)
Explore trends across genres, ratings, and release years. Great for people who love entertainment and want to show creativity.
Skills you build: Data wrangling, time-series plots, filtering techniques.
6. Marketing Campaign Analysis
Tools: Excel / Power BI / SQL
Analyze data from a marketing campaign to measure ROI, conversion rates, and customer engagement. Identify which channels or strategies worked best and suggest improvements.
Skills you build: Data blending, KPI calculation, segmentation, and actionable insights.
7. Financial Expense Analysis & Budget Forecasting
Tools: Excel / Power BI / Python
Work on a company’s expense data to analyze spending patterns, categorize expenses, and create a forecasting model to predict future budgets.
Skills you build: Time series analysis, forecasting, budgeting, and financial storytelling.
Pick 2–3 projects. Don’t just show the final visuals — explain your process on LinkedIn or GitHub. That’s what sets you apart.
Like for more useful content ❤️
I’ve been getting a lot of requests from you all asking for solid Data Analytics projects that can help you boost resume and build real skills.
So here you go —
These aren’t just “for practice,” they’re portfolio-worthy projects that show recruiters you’re ready for real-world work.
1. Sales Performance Dashboard
Tools: Excel / Power BI / Tableau
You’ll take raw sales data and turn it into a clean, interactive dashboard. Show key metrics like revenue, profit, top products, and regional trends.
Skills you build: Data cleaning, slicing & filtering, dashboard creation, business storytelling.
2. Customer Churn Analysis
Tools: Python (Pandas, Seaborn)
Work with a telecom or SaaS dataset to identify which customers are likely to leave and why.
Skills you build: Exploratory data analysis, visualization, correlation, and basic machine learning.
3. E-commerce Product Insights using SQL
Tools: SQL + Power BI
Analyze product categories, top-selling items, and revenue trends from a sample e-commerce dataset.
Skills you build: Joins, GROUP BY, aggregation, data modeling, and visual storytelling.
4. HR Analytics Dashboard
Tools: Excel / Power BI
Dive into employee data to find patterns in attrition, hiring trends, average salaries by department, etc.
Skills you build: Data summarization, calculated fields, visual formatting, DAX basics.
5. Movie Trends Analysis (Netflix or IMDb Dataset)
Tools: Python (Pandas, Matplotlib)
Explore trends across genres, ratings, and release years. Great for people who love entertainment and want to show creativity.
Skills you build: Data wrangling, time-series plots, filtering techniques.
6. Marketing Campaign Analysis
Tools: Excel / Power BI / SQL
Analyze data from a marketing campaign to measure ROI, conversion rates, and customer engagement. Identify which channels or strategies worked best and suggest improvements.
Skills you build: Data blending, KPI calculation, segmentation, and actionable insights.
7. Financial Expense Analysis & Budget Forecasting
Tools: Excel / Power BI / Python
Work on a company’s expense data to analyze spending patterns, categorize expenses, and create a forecasting model to predict future budgets.
Skills you build: Time series analysis, forecasting, budgeting, and financial storytelling.
Pick 2–3 projects. Don’t just show the final visuals — explain your process on LinkedIn or GitHub. That’s what sets you apart.
Like for more useful content ❤️
❤5
Forwarded from Artificial Intelligence
𝗠𝗮𝘀𝘁𝗲𝗿 𝟲 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!👨💻
No need for expensive courses—start learning for FREE today!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Ddxd7P
Don’t miss this opportunity—start learning today and take your skills to the next level!✅️
Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!👨💻
No need for expensive courses—start learning for FREE today!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Ddxd7P
Don’t miss this opportunity—start learning today and take your skills to the next level!✅️
Data Analytics Projects for Beginners 👇
Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis
Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping
News Scraping
https://github.com/rohit-yadav/scraping-news-articles
Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz
Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk
IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration
https://www.youtube.com/watch?v=ur-v0dv0Qtw
https://www.youtube.com/watch?v=ur-v0dv0Qtw
Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP
Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg
Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction
Employee’s Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics
Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting
Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis
Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping
News Scraping
https://github.com/rohit-yadav/scraping-news-articles
Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz
Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk
IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration
https://www.youtube.com/watch?v=ur-v0dv0Qtw
https://www.youtube.com/watch?v=ur-v0dv0Qtw
Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP
Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg
Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction
Employee’s Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics
Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting
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𝗦𝗤𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍
SQL is the backbone of data analytics. Whether you’re cleaning data, generating reports, or exploring trends—SQL helps you turn raw information into actionable insights.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/43lI7CO
Use ChatGPT like a developer — not just a casual user✅️
SQL is the backbone of data analytics. Whether you’re cleaning data, generating reports, or exploring trends—SQL helps you turn raw information into actionable insights.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/43lI7CO
Use ChatGPT like a developer — not just a casual user✅️
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