Data Science for Beginners; 2023-2024 Complete Road Map.

Excited to kick-start your Data Science career? Let’s get into it. Starting out this exciting journey into data science can be challenging and overwhelming given the vast array of skills and competencies one is required to have in order to excel in this field. Let’s dive into the world of data science with this complete road map. First, we will start by defining and understanding what data science is.

What is Data Science?

Data science is a multidisciplinary academic field of study that extracts meaningful knowledge and insights from data (this may be structured or unstructured data) through the use of statistics, scientific methodologies, algorithms and systems.

2023-2024 Complete Data Science Road Map

Phase 1: Foundational Knowledge
The journey begins by building a strong foundation in mathematics and programming. First focus on the following areas.
Mathematics
These instills a great foundation in data analytical methodologies and statistics.
o Linear Algebra
o Calculus
o Probability and Statistics
Programming
These programming languages are essential in data manipulation and analysis.
o Python
o R

Phase 2: Data Manipulation and visualization
Manipulation

NumPy (Python)
Pandas (Python)
Dplyr (R)
Visualization
Matplotlib (Python)
Seaborn (Python)
Ggplot2 (R)

Phase 3: Data exploration Analysis and Preprocessing

Exploratory Data Analysis (EDA)
Feature Engineering
Data Cleaning
Handling Missing Data
Data Scaling and Normalization

Phase 4: Machine Learning
Familiarizing yourself with popular machine learning algorithms like linear regression, decision trees, and neural networks.
Supervised Learning

Regression
Classification
Unsupervised Learning
Clustering
Dimensionality Reduction
Reinforcement Learning
Model Evaluation and Validation
Cross-validation
Hyperparameter Tuning
Model Selection
ML Libraries and Frameworks
Scikit-learn (Python)
TensorFlow (Python)
Keras (Python)
PyTorch (Python)

Phase 5: Deep Learning

Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
Generative Adversarial Networks (GANs)

Phase 6: Big Data Technologies
With an increasing volume of data, knowledge in big data tools and cloud platforms eg; AWS and Azure is very valuable.

Hadoop
Spark
NoSQL Databases

Phase 7: Data Visualization and reporting

Dash-boarding Tools
Storytelling with Data
Effective Communication

Phase 8: Real World Projects
Applying the knowledge gained in developing real world projects is a very crucial step as it familiarizes you in solving real world problems ad also helps you in gaining practical experiences. This will in turn help you in building your resume or CV.