Data Science and Artificial Intelligence Interview Prep

Practicing Data Scientist Program

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Certificate of completion
  • Introduction to Data Science
  • Python Programming
  • Data Cleaning and Preprocessing
  • Data Exploration and Visualization
  • Statistical Concepts
  • Machine Learning Fundamentals
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Evaluation Metrics
  • Model Selection and Hyperparameter Tuning
  • Feature Engineering
  • Time Series Analysis
  • Deep Learning
  • Natural Language Processing (NLP) and Computer Vision
  • Project Work
  • Ethics and Bias in Data Science

Course Curriculum & Resources

  • Overview of Data Science and its applications.

  • Basic Python syntax and data structures.
  • Libraries like NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization.

  • Handling missing data.
  • Data normalization and standardization.
  • Dealing with outliers.

  • Descriptive statistics and summary metrics.
  • Data visualization techniques using libraries like Matplotlib and Seaborn.

  • Probability distributions.
  • Hypothesis testing.
  • Correlation and regression analysis.

  • Supervised vs. Unsupervised learning.
  • Model training, testing, and evaluation.
  • Bias-variance tradeoff.

  • Linear Regression.
  • Logistic Regression.
  • Decision Trees and Random Forests.
  • Support Vector Machines.
  • K-Nearest Neighbors.
  • Naive Bayes.

  • Clustering (K-Means, Hierarchical).
  • Principal Component Analysis (PCA).
  • Association Rule Mining.

  • Accuracy, precision, recall, F1-score, ROC-AUC.
  • Confusion matrices.

  • Cross-validation.
  • Grid search.

  • Feature selection.
  • Feature selection.
  • Creating new features.

  • Handling time series data.
  • Forecasting techniques.

  • Introduction to neural networks.
  • Basics of TensorFlow or PyTorch.

  • Basics of NLP using libraries like NLTK or SpaCy.
  • Introduction to image processing and computer vision using libraries like OpenCV.

  • Applying learned concepts to real-world datasets.
  • Building end-to-end data science and machine learning projects.

  • Considerations for fairness and transparency in machine learning models.

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    Practicing Data Scientist Program

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