Data Science, Python, Exam Prep: Validate skills in Pandas, NumPy, Scikit-learn, ML Modeling, and Statistical Analysis.
Sub Category
- IT Certifications
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Objectives
- Assess readiness for professional Data Science certifications (e.g., Microsoft, IBM).
- Validate fundamental understanding of NumPy and Pandas for high-performance data manipulation.
- Demonstrate proficiency in exploratory data analysis (EDA) techniques using Python tools.
- Test knowledge of essential statistical concepts relevant to machine learning and hypothesis testing.
- Evaluate skill in data preprocessing, cleaning, and advanced feature engineering methods.
- Apply and distinguish between various supervised learning algorithms (Classification and Regression models).
- Understand and interpret model evaluation metrics necessary for reporting and deployment success.
- Practice scenario-based problem solving typical of technical data science interviews.
- Master the interpretation of Scikit-learn outputs, parameters, and model tuning strategies.
- Identify specific areas requiring further study and knowledge reinforcement based on test results.
- Solidify conceptual knowledge of unsupervised learning algorithms like Clustering and Dimensionality Reduction.
Pre Requisites
- Strong foundational knowledge of Python programming (loops, functions, and data structures).
- Experience using core Python libraries: Pandas, NumPy, and Matplotlib.
- Basic understanding of statistical methods (mean, median, standard deviation, probability distributions).
- Prior exposure to Machine Learning concepts (training, testing, overfitting, bias-variance trade-off).
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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