Data Science with Python

• Data Manipulation:

  • Libraries: pandas, NumPy.
  • Functions: Data cleaning, transformation, and aggregation.

• Data Visualization:

  • Libraries: Matplotlib, Seaborn, Plotly.
  • Functions: Plotting graphs, charts, and interactive visualizations.

• Statistical Analysis:

  • Libraries: SciPy, statsmodels.
  • Functions: Descriptive statistics, hypothesis testing, and probability distributions.

• Machine Learning:

  • Libraries: scikit-learn, TensorFlow, Keras.
  • Functions: Model building, evaluation, and tuning for supervised and unsupervised learning.

Before learning Data Science with Python, you should have:

1. Basic Python Programming:

   • Familiarity with Python syntax and data structures (lists, dictionaries, tuples).

2. Mathematics:

   • Understanding of linear algebra, calculus, and basic statistics.

3. Statistics:

   • Knowledge of probability, descriptive statistics, and inferential statistics.

4. Data Manipulation:

   • Experience with data handling and basic data cleaning techniques.

By learning Data Science with Python, you gain:

1. Advanced Python Programming:

   • Proficiency in libraries such as pandas, NumPy, and SciPy for data manipulation and analysis.

2. Data Visualization:

   • Skills in creating informative visualizations using Matplotlib, Seaborn, or Plotly.

3. Statistical Analysis:

   • Ability to perform complex statistical analyses and hypothesis testing.

4. Machine Learning:

   • Knowledge of applying machine learning algorithms using scikit-learn for predictive modeling.