#Data Analysis Software

Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!

Comment „Sheets“ to get it, your data analyst is just a WhatsApp message away. Dealing with data in a spreadsheet can be a hassle, especially when you’re on the go and need an instant answer. This automation changes all of that by turning your Google Sheet into an on-demand analysis tool that lives right in your pocket. This is a personal data analyst you can talk to. Here’s how it works. You send a quick, natural language question to a WhatsApp number—for example, „What were our sales for June?“ An AI agent, powered by n8n’s no-code workflow, connects directly to your Google Sheet. It analyzes the data, finds the exact insight you asked for, and sends you a clear, instant response. No more opening spreadsheets, searching for the right column, or building complex formulas. Just effortless, on-demand insights at your fingertips. Imagine you’re in a client meeting and need a specific metric, or you’re a team lead wanting a quick summary of a project’s status. With this agent, the answer is just a text message away. What kind of insights would you want to get from your data? #n8n #aiautomation

Here’s thing i wish i knew before becoming a data analyst 📊 1. SQL is your best friend — it gets you through 80% of the work. 2. Excel isn’t basic — pivot tables & formulas are used daily. 3. Visualization tools (Tableau/Power BI) make you stand out. 4. Communication > technical sometimes — if you can’t explain insights, they don’t matter. 5. You don’t need 100 certifications — projects & practice speak louder. 6. Most of your time is data cleaning — not fancy dashboards. 7. Business understanding is key — knowing why the data matters is more valuable than just coding. 8. Networking gets you jobs faster than applications — LinkedIn visibility + projects > sending 500 resumes [data analytics,data analyst, corporate, data]

3 AI tools you need if you hate doing data analysis work! Of course, this is AI so please exercise critical thinking with AI generated reports or analysis #dataanalysis #aitools

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you’ll use to get data from databases, and then use to analyse business performance 2. Learn Excel or something similar: it’s great for ad hoc analysis and building engaging charts and diagrams 3. Get familiar with a reporting tool, you don’t need to be great at this just an understanding is fine 4. The core skills are communicating your insights clearly and understanding business metrics Save this and come back to it when you’re planning what to learn, I have links on my profile for courses/guides for each of these aspects!

🧭 Data Analysis Roadmap (Short Version) 1. SQL → W3Schools Learn basics (SELECT, JOIN, GROUP BY), then practice on sample datasets. 2. Maths → Khan Academy Focus on statistics, probability, and basic algebra relevant to data analysis. 3. Excel → Chandoo (YT) Master formulas, pivot tables, charts, and simple dashboards. 4. Power BI → Avi Singh (YT) Learn Power Query, DAX, and build interactive dashboards. 5. Tableau → Tableau Tim (YT) Create visualizations, use filters, and build dashboards. 6. Python → CS50 (YT) Learn basics + data analysis with Pandas, NumPy, Matplotlib/Seaborn. 7. Data Analysis → Alex The Analyst (YT) Follow end-to-end tutorials, build projects, and prep for interviews. Special Benefits for Our Instagram Subscribers 🔻 ➡️ Free Resume Reviews & ATS-Compatible Resume Template ➡️ Quick Responses and Support ➡️ Exclusive Q&A Sessions ➡️ Data Science Job Postings ➡️ Access to MIT + Stanford Notes ➡️ Full Data Science Masterclass PDFs ⭐️ All this for just Rs.45/month! . . . . . . . #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp

day 1 of learning power bi in 30 days Full data analysis course

Let’s build a Data Analyst AI agent together (WITHOUT writing code) I’m using n8n for this. This is one of my favorite no-code automation tools. If you’d like to see a version in Python, let me know! #datascience #aiagent #n8n

5 Data Analyst Projects That Can Get You Hired (With Tutorials) Most portfolios are filled with the same boring projects everyone else does. These five stand out because they solve real business problems and show recruiters you can think, not just code. Here are the 5 projects: 1. Sales Data Dashboard Build an interactive dashboard analyzing sales trends, revenue by region, and product performance using Excel, Power BI, or Tableau 📎 Tutorial: https://www.youtube.com/watch?v=fZn83JRt4Nk 2. Customer Segmentation Analysis Use Python and K-means clustering to segment customers based on behavior and create targeted marketing strategies 📎 Tutorial: https://365datascience.com/tutorials/python-tutorials/build-customer-segmentation-models/ 3. SQL Database Analysis Query and analyze customer purchase patterns, retention rates, and lifetime value using SQL 📎 Tutorial: https://www.geeksforgeeks.org/sql/customer-behavior-analysis-in-sql/ 4. Time Series Forecasting Predict future sales or trends using Python with ARIMA or Prophet models to demonstrate forecasting skills 📎 Tutorial: https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-prophet-in-python-3 5. A/B Testing Framework Design and analyze an A/B test to optimize website conversions or marketing campaigns using statistical testing 📎 Tutorial: https://www.kdnuggets.com/a-complete-guide-to-a-b-testing-in-python These aren't just tutorials you follow. They're projects that demonstrate real business impact, clean code, and the ability to communicate insights. Recruiters check GitHub. Make sure yours has well-documented projects that show practical impact, not just technical skills. Save this and start building. [dataanalyst, data, analyst, analytics, portfolio, projects, SQL, python, powerbi, tableau, excel, dashboard, visualization, forecasting, machinelearning, career, job, hired, beginner, tutorial, github, skills, business, insights, statistics, segmentation, testing, resume] #dataanalyst #dataanalysis #portfolio #projects

Comment “Link” to get link of these courses Unlock Your Data Analytics Potential with Free Courses! 🌟 Are you passionate about data analytics and eager to advance your career without spending a dime? We’ve curated the top 3 free data analyst courses that not only provide valuable knowledge but also come with certifications to boost your resume: ✅1. Power PA Data Analyst Professional Certificate by Microsoft: This course offers comprehensive training from industry experts at Microsoft. Learn how to analyze data, create reports, and make data-driven decisions with advanced tools and techniques. ✅2. Data Analyst Professional Certificate by Meta: Dive into data analytics with Meta’s specialised course. Gain insights into the latest data analysis methodologies and tools used by professionals at Meta, and develop skills that are in high demand in the tech industry. ✅3. Data Analyst Capstone Project by IBM: This project-based course from IBM allows you to work on real-world data analysis projects. Get hands-on experience with IBM’s advanced analytics tools and showcase your skills with a recognized certification. To get these free courses link, 1.Share this video 2.Follow @qriocity.in 3.Comment “Link” in comment box Turn on 🔔 notification on this profile & so that you don’t miss our posts. #learnforfree #dataanalytics #careerboost

📍How to prepare for Data Scientist role in 2026 🚀 CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE FEATURE ENGINEERING & DATA UNDERSTANDING: ● This is where strong candidates stand out. ● Handling missing data ● Encoding categorical variables ● Feature scaling ● Outlier treatment CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE +++ for more look at the comment #datascientist #aiengineer #softwareengineer #datascience #dataengineer