Data Science Study Roadmap — 2023 Update
Introduction
Learning data science can be both exciting and confusing. When I started, I struggled to find a clear, step-by-step guide. I wished for a roadmap that would tell me what I need to know, why it matters, and where to study it — all without complex terms.
Motivation
I wrote this article because, like many, I faced confusion at the start of my data science journey. The demand for data skills is growing, but the path to becoming a data scientist can be overwhelming. This guide aims to be a light in that darkness, providing a straightforward roadmap that explains not just what to learn, but why.
Key Tools for Data Science:
- Basic Foundations:
- Math: Linear Algebra, Calculus, Probability, Statistics
- Programming: Python, R, SQL (optional)
- Data Manipulation: Numpy (Python), Pandas (Python), Dplyr (R)
- Data Visualisation: Matplotlib (Python), Seaborn (Python), ggplot2(R), echarts4r (R)
2. Data Exploration and Preprocessing:
- Exploration Data Analysis (EDA)
- Feature Engineering
- Data Cleaning
- Handling Missing Data
- Data Scaling and Normalisation
3. Machine Learning:
- Supervised Learning: Regression (Linear, Polynomial), Classification (Logistic, k-Nearest Neighbours, SVM, Decision Trees, Random Forest)
- Unsupervised Learning: Clustering, Dimensionality Reduction (PCA, t-SNE, LDA)
- Reinforcement Learning
- Model Evaluation and Validation: Cross-validation, Hyper parameter tuning, model selection
- ML libraries and Frameworks: Scikit-learn (Python), Tensorflow (Python), Keras (Python), PyTorch (Python), Caret (R)
4. Deep Learning:
- Neural Networks: Perceptron, Multi-Layer Perceptron
- Convolutional Neural Networks (CNNs): Image Classification, Object Detection, Image Segmentation
- Recurrent Neural Network (RNNs): Sequence-to-Sequence Models, Text Classification, Sentiment Analysis
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU): Time Series Forecasting, Language Models
- Generative Adversarial Networks (GANs): Image Synthesis, Style Transfer, Data Augmentation
5. Data Visualisation and Reporting:
- Dashboard Tools: Tableau, Power BI, Dash (Python), Shiny (R)
- Storytelling with Data
- Effective Communication
6. Domain Knowledge and Soft Skills:
- Industry Specific Knowledge
- Problem Solving
- Communication Skills
- Time Management
- Teamwork
Here is just an overview of the main disciplines that are required to become a data scientist. In future articles, I will delve into each section of this data science roadmap, offering in-depth insights into theory, practical applications, and detailed code implementations.
Stay tuned for a comprehensive guide to navigate the intricacies of data science. Your applause fuels my motivation — let’s continue this journey together. Follow for more, as we explore the data landscape step by step together 😊