Data Scientist≈Data Analysis Skills+Machine Learning & AI Knowledge😊 Yes, It's True!
- Collect, clean, analyze, and interpret large datasets to provide actionable insights.
- Build predictive models and machine learning algorithms.
- Communicate results to stakeholders using visualizations and storytelling.
- Collaborate with cross-functional teams to solve business problems using data.
- Data collection, cleaning, and preparation.
- Statistical analysis and predictive modeling.
- Machine learning model development and evaluation.
- Communicating insights through dashboards and visualizations.
- Builds problem-solving and analytical thinking skills.
- Forms the foundation for ML algorithms, models, and data analysis.
- Essential for understanding functions, optimization, and quantitative reasoning in data science and machine learning.
- Understand data, patterns, and trends.
- Essential for hypothesis testing, distributions, and inference.
- Descriptive Statistics: Mean, median, mode, variance, standard deviation, percentiles.
- Inferential Statistics: Hypothesis testing, confidence intervals, t-tests, z-tests, ANOVA.
- Probability: Basics, conditional probability, Bayes' theorem.
- Distributions: Normal, binomial, Poisson, uniform, exponential.
- Correlation vs. Causation
- Regression: Linear, multiple, logistic.
- Calculus: Partial Derivatives, integrals, applications in optimization and area.
- Matrix Algebra: Operations, inverses, eigenvalues.
- Combinatorics: Permutations, combinations.
- Sampling: Random, stratified, sample size, bias.
- Central Limit Theorem
- Law of Large Numbers
- Random Variables: Discrete, continuous, expected value.
- Markov Chains Basics.
- A-Z Linear Algebra & Calculus for AI & Data Science
- Complete Applied Statistics for Data Scientists with R
- Practice using Python (
NumPy,SciPy, andstatsmodels).
- Python is the go-to programming language for data science due to its simplicity and robust libraries.
- Used for data cleaning, manipulation, and building machine learning models.
- Supports deep learning frameworks and statistical analysis.
- Python Basics
- Variables, data types, loops, conditionals, functions, and OOPs.
- Libraries
- NumPy: For numerical computations.
- Pandas & Polars: For data manipulation (DataFrames, cleaning data, handling datasets).
- Matplotlib/Seaborn: For creating visualizations.
- Official Python Docs
- Python Playlist
- For Basic to Advanced Python
- Notes/Books
- Pandas Tutorials
- Practice with Python datasets on Kaggle or public repositories.
- Excellent for statistical computing, data visualization, and academic research.
- Ideal for creating interactive dashboards with R Shiny.
- Basics: Variables, data types, loops, functions.
- Data Manipulation:
dplyr,tidyr. - Visualization:
ggplot2,plotly. - Statistical Modeling: Regression, hypothesis testing.
- Dashboards: Build interactive apps with R Shiny.
- Key for building predictive models and solving real-world problems.
- Forms the foundation of advanced AI and data science applications.
- Supervised Learning:
- Linear Regression, Logistic Regression, Polynomial Regression, Lasso, Ridge.
- KNN, Decision Trees, Naive Bayes, Support Vector Machines (SVM), Random Forest, LDA, Extra Trees Classifier, LightGBM.
- Norm, Loss & Cost function, R Squared, Confusion Matrix.
- Unsupervised Learning:
- Clustering (K-means, DBSCAN).
- Dimensionality Reduction (PCA, t-SNE).
- Time Series Analysis
- Forecasting trends and seasonality.
- ARIMA models and other time series techniques.
- Advanced Concepts:
- Ensemble methods (Boosting, Bagging).
- Regularizations
- Data Sampling Methods
- Gradient Descents
- Hyperparameter tuning
- Cross-validation.
- Scikit-learn (
sklearn): For statistical machine learning models. - Statsmodels: For statistical analysis.
- SciPy: For statistical analysis.
- Machine Learning Playlist
- Machine Learning Module
- Practice using Python's
sklearnand Kaggle competitions.
- For advanced AI applications such as image recognition, NLP, and generative models.
- Used in tasks that require neural networks for high-dimensional data.
- Basics: Neural Networks (Perceptron, Feedforward, Backpropagation).
- Architectures: ANN (for normal tasks), CNNs (for images), RNNs (for sequences), Transformers (for NLP tasks).
- Transfer Learning, Fine-tuning
- GD Optimizers, Regularization and Generalization
- Frameworks: TensorFlow, Keras, PyTorch.
- TensorFlow Library: For deep learning & AI.
- PyTorch Library: For deep learning & AI.
- Deep Learning Playlist
- Another DL Playlist
- Another DL Playlist
- Gradient Descent
- Basic to Advanced Deep Learning
- For communicating insights effectively to stakeholders.
- A critical skill for storytelling with data.
- Tools: Power BI, Tableau, Matplotlib, Seaborn.
- Skills:
- Creating dashboards.
- Customizing reports and interactive visuals.
- GitHub is a crucial platform for version control and collaboration.
- Enables you to showcase your projects and build a portfolio.
- Facilitates teamwork on data science projects.
- Git Basics:
- Version control concepts, repositories, branches, commits, pull requests.
- GitHub Skills:
- Hosting projects, collaboration workflows, managing issues.
- Best Practices:
- Writing READMEs, structuring repositories, using
.gitignorefiles.
- Writing READMEs, structuring repositories, using
- Complete GitHub for Data Scientists
- Use GitHub to practice hosting Python, SQL, and machine learning projects.
- Essential for querying, extracting, and joining data from relational databases.
- Used to preprocess and prepare data before modeling.
- Basics: SELECT, INSERT, UPDATE, DELETE.
- Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries.
- Advanced: Window functions, CTEs (Common Table Expressions), and query optimization.
- SQL Learning Playlist
- Programming with Mosh - SQL Playlist
- SQL Module of Data Analysis Specialization
- Tools like MySQL Workbench, SQLite, or PostgreSQL.
- Hands-on experience to apply all the skills learned.
- Builds a strong portfolio showcasing
data scienceexpertise.
- Python & Machine Learning:
- Build a predictive model for house prices.
- Build a predictive model for stock prices
- Create a recommendation system.
- Deep Learning:
- Train a CNN for image classification.
- Build an NLP model for sentiment analysis.
- Design a Chatbot using Generative AI
- SQL:
- Analyze and clean a retail dataset.
- Visualization:
- Design an interactive dashboard using
PowerBifor e-commerce KPIs.
- Design an interactive dashboard using
- Extract data using SQL.
- Preprocess and clean data using Python and Pandas.
- Analyze the data using Statistics and Machine Learning.
- Visualize insights with Power BI, Tableau, or Matplotlib.
- Deploy
ML modelsusing Flask / FastAPI orPowerBidashboards for business use.
Following this roadmap step-by-step will give you the skills needed to succeed as a Data Scientist. Let me know if you’d like additional resources or specific examples! Just write an email to me.
This repository is continually updated based on the top job postings on LinkedIn and Indeed. Please pray for me and my family—your prayers are all I ask as a token of appreciation.
- Basic to Advanced Python
- A-Z Linear Algebra & Calculus for AI & Data Science
- Machine Learning & Deep Learning Core Concepts
- Advanced Deep Learning & Generative AI
- SQL & Data Analysis Tools
Note: We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that T-shaped skills are better than i-shaped skill. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of free tutorials that are also great for learning. Best of luck!
Hazrat Ali
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- 🎓 Programmer || Software Engineering