1.3M
SWIf I was a beginner learning to code, I would use this Python roadmap step by step for beginners 💪 #coding #codingforbeginners #learntocode #codingtips #cs #python #computerscience #usemassive
@swerikcodes
#Data Analysis With Eda Python
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18Трендовые Reels
(12)
3.7K
DASimple exploratory data analysis in python.
More #python3 and #MachineLearning
In my page and YouTube channel. 📽️
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#pythoncodesnippets #pythonbeginner #pythontips #girlswhocode #womenincode #java #html #js #girlsintech #dataviz #whomenwhocode #linux #eda #datascientists
@data_science.py
35.6K
SOGetting started in quantitative analysis 😎
The yfinance package in Python allows you to grab historical financial data straight from yahoo finance. If you can search for a company on yahoo finance, you can get historical data from it!
Historical data is the foundation of machine learning/predictive models in the financial world. Patterns tend to repeat themselves, but picking out the significant patterns can be tricky!
Follow for more free coding resources ✅
#code #coding #learntocode #data #tech
@softwarewithnick
171.6K
PRHere are 5 most important things to keep in mind before going for a Data Analyst interview as a fresher:
Know the Basics Thoroughly - Be clear with SQL (joins, group by, aggregates), Excel formulas (VLOOKUP, Pivot tables), and Python libraries (Pandas, NumPy). These are almost always tested. Don’t miss your chance, because basics he clear nahi toh impression kharab within seconds.
Prepare for Case-Based Questions - Employers may ask you to analyze sales, customer, or product data. Practice interpreting charts, spotting trends, and making recommendations.
Understand Business + Data Link - Be ready to explain how data helps in decision-making (e.g., identifying customer segments, reducing costs, improving sales). Don’t forget to add insights and recommendations.
Have a Strong Project/Portfolio Story - Highlight academic projects, internships, or personal projects (like sales data analysis, dashboards, or EDA). Explain your process clearly. The way you explain things, clearly explains your understanding, so be very well prepared because this can make it or break it.
Soft Skills & Mindset - Show curiosity, problem-solving attitude, communication skills (explain technical findings in simple terms), and willingness to learn.
Comment for more 1:1 guidance and tips. ✨
Data analyst, data analytics, data science, roadmap, career, freshers, interviews, exploratory data analysis, business analyst, technical jobs, fin tech company, maang, faang, google, amazon, explore, fyp, guidance, Gurgaon, corporate girlie, life as a junior data analyst
#dataanalyst #datasciencejobs #fresher #guidance #gurgaon #careerintech #dataanalytics #freshersjobs #btech
@prernaa.py
838.0K
SURepost to share with friends ♻️ Here’s how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it down to 3 minutes because instagram. One part that got cut off was the job market. Should I post a part 2?
what are other skills that would you add to the list??
#dataanalysis #dataanalyst #sql #python
@sundaskhalidd
40.9K
SHPython Data Visualization for Exploratory Analysis
Good data analysis starts with asking the right questions, and visualization helps you answer them faster.
This cheat-sheet style guide brings together essential Python visualization techniques used during exploratory data analysis. It covers patterns at a single-variable level, relationships between variables, multivariate insights, time-based trends, text exploration, and plot customization. These are the exact visual checks analysts rely on before modeling or reporting.
Whether you work with business data, research datasets, or real-world production data, strong visuals help you spot outliers, understand distributions, compare categories, and communicate insights clearly.
Save it for reference and revisit it whenever you start exploring a new dataset.
[python,data visualization,exploratory data analysis,eda,matplotlib,seaborn,pandas,data analysis,analytics,data science,charts,plots,statistical analysis,univariate analysis,bivariate analysis,multivariate analysis,time series,text analysis,data insights,data storytelling,correlation,distribution,outliers,trend analysis,heatmap,scatter plot,box plot,violin plot,histogram,kde plot,regression plot,pair plot,data preparation,data workflow,python for analytics,data visualization best practices]
#Python #DataVisualization #EDA #DataAnalysis #DataScience
@she_explores_data
1.1K
CH💥 Exploratory Data analytics with python pandas cheatsheet 🚀
#python
#eda
#pandas
#cheat
@chetan_raut_20
213
DA!! New Project Uploaded !!
🚀 Real Project – 14 | Salary Data Analysis Using Python | Python Coding for Data Science
Watch on YouTube (copy-paste) - https://youtu.be/TLIotspGcng
You will learn the complete data analysis workflow, just like in real industry projects:
✅ Data Understanding & Exploration (EDA)
✅ Data Cleaning & Handling Duplicates
✅ Outlier Detection using IQR Method
✅ Data Visualization using Matplotlib & Seaborn
✅ Business Questions & Insights
✅ Correlation Analysis
✅ Advanced Charts (Scatter, Line, Histogram, Dashboard)
✅ Final Mini Dashboard
✅ Portfolio-Ready Project
#datasciencewithrg #datasciencelovers #python #project #dataanalysis #dataanalytics #coding #salarydataanalysis #pythonproject
#datascience #dataanalytics #datavisualization #datasciencelovers
@data_science_lovers
3.1K
MAPython for Data Analysis pt 7: Data Cleaning
Full video on my TikTok!
#dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #python #pythoncode #coding #programming
@marytheanalyst
312.7K
AAMost Important Python Topics for Data Analyst Interview📄
➡️ BasicsOfPython:
1. Data Types
2. Lists
3. Dictionaries
4. Control Structures:
• if-elif-else
• Loops
5. Functions
Practice Basic FAQs:
• How to reverse a string in Python?
• How to find the largest/smallest number in a list?
• How to remove duplicates from a list?
• How to count the occurrences of each element in a list?
• How to check if a string is a palindrome?
➡️ Pandas:
1. Pandas Data Structures (Series, DataFrame)
2. Creating and Manipulating DataFrames
3. Filtering and Selecting Data
4. Grouping and Aggregating Data
5. Handling Missing Values
6. Merging and Joining DataFrames
7. Adding and Removing Columns
➡️ Exploratory Data Analysis (EDA):
• Descriptive Statistics
• Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)
• Correlation and Covariance
• Handling Duplicates
• Data Transformation
➡️ Numpy:
1. NumPy Arrays
2. Array Operations:
• Creating Arrays
• Slicing and Indexing
• Arithmetic Operations
➡️ IntegrationWithOtherLibraries:
1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)
➡️ KeyConceptsToRevise:
1. Data Manipulation with Pandas and NumPy
2. Data Cleaning Techniques
3. File Handling (reading and writing CSV files, JSON files)
4. Handling Missing and Duplicate Values
5. Data Transformation (scaling, normalization)
6. Data Aggregation and Group Operations
7. Combining and Merging Datasets
Best of Luck 🤞
Keep learning, growing, and exploring new opportunities!
💬 Comment Python for the full list 📃
If you need help with assignments or projects, just DM us! 🚀
👍 Like, 💬 comment, 💾 save, and ↗️ share if you found this helpful!
Don’t forget to follow @aasifcodes for more such content.
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#DataAnalytics #Python #Interview #Pandas #NumPy #DataScience #job #hiring #excel #sql #machinelearning #artificialintelligence #chatgpt #jobhunt #aasifcodes #vibecoding
@aasifcodes
752.2K
SHMost Important Python Topics for Data Analyst Interview:
#Basics of Python:
1. Data Types
2. Lists
3. Dictionaries
4. Control Structures:
- if-elif-else
- Loops
5. Functions
6. Practice basic FAQs questions, below mentioned are few examples:
- How to reverse a string in Python?
- How to find the largest/smallest number in a list?
- How to remove duplicates from a list?
- How to count the occurrences of each element in a list?
- How to check if a string is a palindrome?
#Pandas:
1. Pandas Data Structures (Series, DataFrame)
2. Creating and Manipulating DataFrames
3. Filtering and Selecting Data
4. Grouping and Aggregating Data
5. Handling Missing Values
6. Merging and Joining DataFrames
7. Adding and Removing Columns
8. Exploratory Data Analysis (EDA):
- Descriptive Statistics
- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)
- Correlation and Covariance
- Handling Duplicates
- Data Transformation
#Numpy:
1. NumPy Arrays
2. Array Operations:
- Creating Arrays
- Slicing and Indexing
- Arithmetic Operations
#Integration with Other Libraries:
1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)
#Key Concepts to Revise:
1. Data Manipulation with Pandas and NumPy
2. Data Cleaning Techniques
3. File Handling (reading and writing CSV files, JSON files)
4. Handling Missing and Duplicate Values
5. Data Transformation (scaling, normalization)
6. Data Aggregation and Group Operations
7. Combining and Merging Datasets
#dataanalytics #job
#hiring #interview
@shakra.shamim
9.1K
LOThis Python EDA framework literally changed how I analyze data 📊
I used to spend hours just staring at datasets trying to figure out where to even start. Then I built this system and now every e-commerce analysis takes me like 30 minutes max.
Here’s the exact order I run everything:
Dataset overview first. Descriptive stats, data types, missing values, date ranges. You need to know what you’re working with before you do anything else.
Sales by category next. Group by product category, calculate revenue and AOV, then visualize it. This shows you where the money actually is.
Temporal patterns are huge. Resample by month to catch seasonality. Monthly revenue, order volume, active customers. You’ll spot patterns you didn’t even know existed.
RFM segmentation is where it gets really good. Recency, frequency, monetary value. Then bucket your customers into VIP, loyal, active, and at risk. Game changer for targeting.
Top performing products ranked by revenue and units sold. Calculate contribution percentage so you know what’s actually moving the needle.
Geographic distribution shows you which markets are crushing it and where you’re leaving money on the table.
Then wrap it all up in a summary dashboard. Month over month growth, retention metrics, revenue per customer. The stuff that actually matters.
Comment “CODE” and I’ll send you the full code. Save this so you stop winging your analysis every single time 🎯
#PythonForDataScience #ExploratoryDataAnalysis #DataAnalyticsTutorial #PythonProjects
@loresowhat
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