#Data Management

Another powerful session with the cohort. Data Management is not theory. It is not “them say.” It is structured, strategic, and executed based on real organisational realities. In this clip, I was walking my mentees through what the first 30 days of a Data Governance implementation should look like for a banking client project they’re working on. Not guesswork. Not recycled slides. But a roadmap built from verifiable, hands-on experience spanning over two decades delivering governance frameworks in regulated environments. Because knowing definitions is easy. Designing and executing in a live financial services environment is different. This is how we build practical capability — not course completion certificates. #DataGovernance #DataManagement #BankingTransformation #DataLeadership #InformationGovernance

This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year. 1️⃣ Learn SQL SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database. It’s also a very easy coding language to learn, so I would start there. Use Interview Master to learn and practice SQL (link in bio): → Learn SQL: www.interviewmaster.ai/content/sql → Practice SQL: www.interviewmaster.ai/home 2️⃣ Start building Product Sense & Business Sense Product sense & business sense basically means you know how to use Data to solve real problems. I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world. I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job. 3️⃣ Learn Statistics How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well. → Descriptive statistics → Common distributions → Probability and Bayes’ Theorem → Basic Machine Learning models → Experimentation concepts → A/B experiment design Check out Stanford’s Introduction to Statistics, which is free on Coursera. 4️⃣ Learn Python Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3. I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio). 5️⃣ Use AI-assisted coding tools Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity. And also many companies are evaluating you on your use of AI during interviews. #datascience #datascientist

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!

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!

A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

📍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

How I’d become a Data Analyst in 2026 ⬇️ 1️⃣ Get in the door (any role) Data Analyst titles are hard to land, degree or not. So get into any role at a tech forward company with an analytics team/department . Sales. Ops. Data entry. Work up! Prove your value. That’s exactly what I did. 2️⃣ Improve what’s in front of you Look for small things you can control: • Excel • MS Access • Power Query Invoices research (ms access), trends, reports doesn’t matter, anything YOU can do. 3️⃣ Learn only what you need Target the tools you’re already working with/access too. (DataCamp and Codecademy worked for me) 4️⃣ Build something real Not tutorials. Build a tool people (and you) actually use even if it’s simple. Examples could be: Using forms and VBA/SQL in ms access to build a form for people to researching invoices! 5️⃣ Show your work Demo it. Explain the impact. Who uses it. Why it matters. And how it helps! 6️⃣ Say yes to opportunities Take on EVERYTHING, prove you can do the work, even if it adds more stress. That’s how you stack proof for the next role. No degree required. 👉 Follow if you’re breaking into data. #dataanalyst #howto #breakintotech #nodegree #2026goals

1. QUALIFY + ROW_NUMBER() Lets you rank rows and filter results in the same query — perfect for grabbing the most recent or top record without subqueries. 2. LAG / LEAD Used to look at the previous or next row — great for comparing changes over time (day-over-day, month-over-month). 3. CTE (WITH clause) Creates a temporary, named query so you can break complex SQL into clean, readable steps. #data #analyst #dayinthelife #dadlife #sql

DAY IN THE LIFE OF A DATA ANALYST 💻💡 Today I decided to show you the behind the scenes. Follow along as I break down a day in the life of a data analyst, sharing insights and shedding light on this fascinating field. 🤩 #DataAnalyticsUnveiled #BehindTheData #dataanalytics #datascience #techcareers

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

What data do you actually need to make the best decision possible?