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Kuria Felix
Kuria Felix

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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.
These instills a great foundation in data analytical methodologies and statistics.
o Linear Algebra
o Calculus
o Probability and Statistics
These programming languages are essential in data manipulation and analysis.
o Python
o R

Phase 2: Data Manipulation and visualization

  • 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.

Top comments (1)

randellbrianknight profile image
Randell Brian Knight

Excellent article, Thanks for sharing.