4 Career Paths In Data Analytics

1) Data Analyst:

Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.

They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.

Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.

Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.


2)Data Scientist:

Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.

They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.

Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.


3)Business Intelligence (BI) Analyst:

Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.

They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.

Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.

Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.

4)Data Engineer:

Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.

Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.

Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.

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Have you ever thought about this?... 🤔

When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights.

Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, it’s not the sexiest topic to talk about.

The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace.

So, here is why we need to clean our data 👇🏻

1️⃣ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process.
2️⃣ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility.
3️⃣ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.

Product team cases where a #productteams improved content discovery

Case: Netflix and Personalized Content Recommendations

Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.

Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.

Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.

Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).





Case: Spotify and Music Discovery

Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.

Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.

Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).

🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

➖➖➖➖➖

🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

➖➖➖➖➖

🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

✅ Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

➖➖➖➖➖

🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

➖➖➖➖➖

🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

➖➖➖➖➖

🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

➖➖➖➖➖

🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

➖➖➖➖➖

🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data

If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.

Yes, you might hear a lot about them or some other trending technology of the year...but guess what!

Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.

Instead, here are basic skills that will get you further than mastering any framework:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.

You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.

You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.

You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai

I love frameworks and libraries, and they can make anyone's job easier.

But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.

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Machine Learning Roadmap

Roadmap to become Data Scientist

Guys, Big Announcement! 🚀

We've officially hit 3 Lakh subscribers on WhatsApp— and it's time to kick off the next big learning journey together! 🤩

Artificial Intelligence Complete Series — a comprehensive, step-by-step journey from scratch to real-world applications. Whether you're a complete beginner or looking to take your AI skills to the next level, this series has got you covered!

This series is packed with real-world examples, hands-on projects, and tips to understand how AI impacts our world.

Here’s what we’ll cover:

*Week 1: Introduction to AI*

- What is AI? Understanding the basics without the jargon

- Types of AI: Narrow vs. General AI

- Key AI concepts (Machine Learning, Deep Learning, and Neural Networks)

- Real-world applications: From Chatbots to Self-Driving Cars 🚗

- Tools & frameworks for AI (TensorFlow, Keras, PyTorch)


*Week 2: Core AI Techniques*

- Supervised vs. Unsupervised Learning

- Understanding Data: The backbone of AI

- Linear Regression: Your first AI algorithm!

- Decision Trees, K-Nearest Neighbors, and Support Vector Machines

- Hands-on project: Building a basic classifier with Python 🐍


*Week 3: Deep Dive into Machine Learning*

- What makes ML different from AI?

- Gradient Descent & Model Optimization

- Evaluating Models: Accuracy, Precision, Recall, and F1-Score

- Hyperparameter Tuning

- Hands-on project: Building a predictive model with real data 📊


*Week 4: Introduction to Neural Networks*

- The fundamentals of neural networks & deep learning

- Understanding how a neural network mimics the human brain 🧠

- Training your first Neural Network with TensorFlow

- Introduction to Backpropagation and Activation Functions

- Hands-on project: Build a simple neural network to recognize images 📸


*Week 5: Advanced AI Concepts*

- Natural Language Processing (NLP): Teach machines to understand text and speech 🗣️

- Computer Vision: Teaching machines to "see" with Convolutional Neural Networks (CNNs)

- Reinforcement Learning: AI that learns through trial and error (think AlphaGo)

- Real-world AI Use Cases: Healthcare, Finance, Gaming, and more

- Hands-on project: Implementing NLP for text classification 📚


*Week 6: Building Real-World AI Applications*

- AI in the real world: Chatbots, Recommendation Systems, and Fraud Detection

- Integrating AI with APIs and Web Services

- Cloud AI: Using AWS, Google Cloud, and Azure for scaling AI projects

- Hands-on project: Build a recommendation system like Netflix 🎬


*Week 7: Preparing for AI Careers*

- Common interview questions for AI & ML roles 📝

- Building an AI Portfolio: Showcase your projects

- Understanding AI in Industry: How it’s transforming businesses

- Networking and building your career in AI 🌐


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7 Essential Data Analysis Techniques You Need to Know in 2025

✅ Exploratory Data Analysis (EDA) – Uncover patterns, spot anomalies, and visualize distributions before diving deeper
✅ Time Series Analysis – Analyze trends over time, forecast future values (using ARIMA or Prophet)
✅ Hypothesis Testing – Use statistical tests (T-tests, Chi-square) to validate assumptions and claims
✅ Regression Analysis – Predict continuous variables using linear or non-linear models
✅ Cluster Analysis – Group similar data points using K-means or hierarchical clustering
✅ Dimensionality Reduction – Simplify complex datasets using PCA (Principal Component Analysis)
✅ Classification Algorithms – Predict categorical outcomes with decision trees, random forests, and SVMs

Mastering these will give you the edge in any data analysis role.

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Free Programming and Data Analytics Resources 👇👇

✅ Data science and Data Analytics Free Courses by Google

https://developers.google.com/edu/python/introduction

https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field

https://cloud.google.com/data-science?hl=en

https://developers.google.com/machine-learning/crash-course

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🔍 Free Data Analytics Courses by Microsoft

1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/

3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

🤖 Free AI Courses by Microsoft

1. Fundamentals of AI by Microsoft

https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

2. Introduction to AI with python by Harvard.

https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

📚 Useful Resources for the Programmers

Data Analyst Roadmap
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Free C course from Microsoft
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Python for Data Science and ML
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Ethical Hacking Bootcamp
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Unity Documentation
https://docs.unity3d.com/Manual/index.html

Advanced Javascript concepts
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Oops in Java
https://nptel.ac.in/courses/106105224

Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction

Python Data Structure and Algorithms
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Data Structures Interview Preparation
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🍻 Free Programming Courses by Microsoft

❯ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/

❯ TypeScript
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❯ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

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ENJOY LEARNING 👍👍

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

Some common supervised learning algorithms include:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

Some popular unsupervised learning algorithms include:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

Common semi-supervised learning algorithms include:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

Popular reinforcement learning algorithms include:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

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