#Virallink Transcriptomic Data Analysis

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#Virallink Transcriptomic Data Analysis Reel by @novitechresearchanddevelopment - COMMENT " DA " FOR REGISTRATION LINK ❤️‍🔥❤️‍🔥 . . . . . . . . #dataanalytics #novitech #novians #futureopportunities #tamilcourse #certified #freeco
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@novitechresearchanddevelopment
COMMENT " DA " FOR REGISTRATION LINK ❤️‍🔥❤️‍🔥 . . . . . . . . #dataanalytics #novitech #novians #futureopportunities #tamilcourse #certified #freecourse #freecertificate #masterclass #tamilcourse #onlineclasses
#Virallink Transcriptomic Data Analysis Reel by @mahmoudossmana - ازاي تبقي محلل بيانات ازاي تتعلم تحليل البيانات Learn data Analysis How to be data analyst . . . . #Excel #explore #business #work #study #company #
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@mahmoudossmana
ازاي تبقي محلل بيانات ازاي تتعلم تحليل البيانات Learn data Analysis How to be data analyst . . . . #Excel #explore #business #work #study #company #finance #tutorials #dataanalytics #exceltips #exceltricks #learning #fyp #viral
#Virallink Transcriptomic Data Analysis Reel by @mominarahim20 (verified account) - Turn YouTube into instant study notes for Data Analysis! Excited to collaborate with RecCloud AI 🤝 Now you can convert long YouTube lectures into cle
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@mominarahim20
Turn YouTube into instant study notes for Data Analysis! Excited to collaborate with RecCloud AI 🤝 Now you can convert long YouTube lectures into clean, structured notes in seconds perfect for Data Analysis, Excel, Power BI & analytics students. Save time. Learn smarter. #DataAnalysis #RecCloudAI #YouTubeNotes #Excel #reccloud
#Virallink Transcriptomic Data Analysis Reel by @prernaa.py (verified account) - DATA ANALYTICS ROADMAP (0 → Job Ready) Reality check: You don't need coding mastery, fancy degrees, or 10 tools. You need strong basics + projects + s
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@prernaa.py
DATA ANALYTICS ROADMAP (0 → Job Ready) Reality check: You don’t need coding mastery, fancy degrees, or 10 tools. You need strong basics + projects + storytelling + consistency. PHASE 0: Mindset & Setup (1 Week) What to understand first Data Analytics ≠ Data Science Your job is to answer business questions using data Tools are secondary, thinking is primary Setup Laptop Google account Install: Excel / Google Sheets MySQL / PostgreSQL VS Code or Jupyter Notebook Power BI (free version) PHASE 1: EXCEL (Foundation Tool) – 2 to 3 Weeks 80% companies still test Excel in interviews What to learn (IN THIS ORDER) Basics Rows, columns, formatting Functions SUM, AVERAGE, COUNT IF, AND, OR VLOOKUP / XLOOKUP INDEX + MATCH Data Cleaning Remove duplicates Text to columns TRIM, CLEAN Pivot Tables Grouping Filters Charts Bar, Line, Pie Mini Project 👉 Sales Analysis Dashboard in Excel Monthly sales Top products Region-wise revenue 📌 This becomes Project 1 PHASE 2: SQL (MOST IMPORTANT) – 3 to 4 Weeks SQL is a job gatekeeper. No SQL = No shortlist. What to learn Basics SELECT, WHERE, ORDER BY Filtering AND, OR, IN, BETWEEN, LIKE Aggregations COUNT, SUM, AVG GROUP BY, HAVING Joins INNER LEFT RIGHT Subqueries Window Functions ROW_NUMBER RANK DENSE_RANK Practice Write daily 5–10 queries Explain your logic in words Project 👉 E-commerce Database Analysis Top customers Repeat orders Revenue trends 📌 Project 2 PHASE 3: PYTHON (Only What You Need) – 3 Weeks You are not becoming a Python developer What to learn Basics Variables Loops Conditions Libraries NumPy Pandas Matplotlib / Seaborn Data Tasks Read CSV Handle missing values Filter & sort data Simple EDA Project 👉 Diwali Sales / Zomato / Netflix Data Analysis Clean data Insights Visualizations 📌 Project 3 PHASE 4: POWER BI / TABLEAU – 2 Weeks This is where you look job-ready What to learn Data Import Relationships DAX Basics SUM CALCULATE FILTER Dashboards Storytelling Project 👉 Business Performance Dashboard KPIs Trends Insights slide 📌 Project 4 Comment for complete roadmap and resources✨
#Virallink Transcriptomic Data Analysis Reel by @onseventhsky (verified account) - Here is your guide to data analysis 🧐 . . . . . . [data analytics, corporate, education, job switch]
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@onseventhsky
Here is your guide to data analysis 🧐 . . . . . . [data analytics, corporate, education, job switch]
#Virallink Transcriptomic Data Analysis Reel by @datawithashok (verified account) - "AI in Analytics - Data Extraction Edition" Save & Share this reel. Follow @datawithashok for more!
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@datawithashok
“AI in Analytics - Data Extraction Edition” Save & Share this reel. Follow @datawithashok for more!
#Virallink Transcriptomic Data Analysis Reel by @mahmoud_elboghdadi (verified account) - ازاى تبدأ data analysis وايه اهم حاجه تتعلمها قبل ماتتعلم excel, sql, python
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@mahmoud_elboghdadi
ازاى تبدأ data analysis وايه اهم حاجه تتعلمها قبل ماتتعلم excel, sql, python
#Virallink Transcriptomic Data Analysis Reel by @aasifcodes (verified account) - Stop analyzing data manually 👀 This AI lets you analyze data, create presentations, generate documents, run research, and automate multi step workfl
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@aasifcodes
Stop analyzing data manually 👀 This AI lets you analyze data, create presentations, generate documents, run research, and automate multi step workflows for you. Build autonomous AI workers that connect with Gmail, Slack, Google Sheets, LinkedIn, Notion, and more to actually execute tasks, not just suggest them. 💬 Comment Kortix for the link
#Virallink Transcriptomic Data Analysis Reel by @codeandcrush - 🐍 Top 15 Python Libraries Every Data Analyst Must Know 📊 If you are starting your Data Analytics journey, the right Python libraries can save you h
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@codeandcrush
🐍 Top 15 Python Libraries Every Data Analyst Must Know 📊 If you are starting your Data Analytics journey, the right Python libraries can save you hours of effort and make your projects 10x more powerful. 🚀 Here’s a quick breakdown of the must-know libraries: ✅ Pandas → Data cleaning & manipulation ✅ NumPy → Fast numerical computing ✅ Matplotlib & Seaborn → Stunning visualizations ✅ Plotly → Interactive dashboards ✅ Scikit-learn → Easy machine learning ✅ Statsmodels & SciPy → Statistical analysis ✅ TensorFlow / PyTorch → Advanced AI & analytics ✅ OpenPyXL, Dask, BeautifulSoup, NLTK, SQLAlchemy → Excel automation, big data, web scraping, text analytics, and databases! 💡 Whether you’re preparing for a job, building projects, or just learning, these libraries are the backbone of Data Analytics. 👉 Save this reel for quick reference 🔖 👉 Share it with your data friends 🔄 👉 Follow @codeandcrush for more daily Data Analytics tips, tricks & career hacks 🚀 #python #dataanalytics #pythonlibraries #datascience #machinelearning #sql #powerbi #dataanalyst #learnpython #learnandgrow #careergoals #instagram #pythonprogramming #reelsi̇nstagram #trendings
#Virallink Transcriptomic Data Analysis Reel by @thegauravghai (verified account) - Stop paying to learn Data Analytics because I've prepared a free roadmap for you, including free resources, interview practice, projects to build, res
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@thegauravghai
Stop paying to learn Data Analytics because I’ve prepared a free roadmap for you, including free resources, interview practice, projects to build, resumes, and more in this free E-Book. 💯🚀 Checkout free Roadmap Of Data Analytics E-Book, Like this reel and comment “Link” I’ll share all the info ❤️ - We discuss a detailed exercise sheet that provides a free roadmap to becoming a high-paying data analyst. The sheet covers all the necessary topics and resources to master data analysis, as well as interview preparation tips and resume optimization advice, all at no cost. #datascience #careergrowth #dataanalytics #careerdevelopment #interview #roadmap #freeresources #resources #DataAnalysis #FreeResources #CareerAdvice #InterviewPreparation #ResumeOptimization
#Virallink Transcriptomic Data Analysis Reel by @jessramosdata (verified account) - comment "AI" for my full synthetic data tutorial Youtube video! save for later & follow for more! You can customize any dataset for any industry, bus
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@jessramosdata
comment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more! You can customize any dataset for any industry, business problem, or project and get way more interesting data than Kaggle. Plus, you can ask for imperfect data with inconsistent values, duplicates, or nulls to make it feel more realistic to the real world. You just have to know how to specify your requirements and constraints when prompt engineering. Here’s what you should specify: ✨ size of dataset(s) (rows / columns) ✨ column names and data types ✨ primary keys and foreign keys ✨ distribution and allowed values ✨ variation of datapoints ✨ downloadable as CSVs ✨ anything else that may impact your project! Full example below: You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file. Have you tried this hack and said goodbye to Kaggle yet?

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