Complete Data science boot camp using Python

End to end Implementation of Data science and Machine Learning model.

From Data analysis and gathering to creating your own modelling will be covered as part of this course.

Pandas:

• Creation of Data representation

• Data filtering

• Data framework

• Selection and viewing

• Data Manipulation

  
  

Numpy:

• Datatypes in Numpy

• Creating arrays and Matrix.

• Manipulation of data.

• Standard deviation and variance.

• Reshaping of Matrix.

• Dot function

• Mini-project using Numpy and Pandas package

Matplotlib:

• Creation Plots - Line, Scatter, bar and Histogram.

• Creating plots from Pandas and Numpy data

• Creation of subplots

• Customization and saving plots

Scikit Learn: Scikit-learn is a free, open-source Python library for machine learning. It offers simple, efficient tools for data analysis and modeling, including classification, regression, clustering, preprocessing, and model selection. Built on NumPy and SciPy, it features a consistent API and supports various popular algorithms

Supervised Learning: A machine learning method where models are trained using labeled data, meaning each input is paired with the correct output or label. The algorithm learns the relationship between inputs and outputs, enabling it to predict or classify new, unseen data accurately

Skills & Applications

• Import, preprocess, and visualize real-world datasets

• Perform statistical analyses efficiently

• Create reproducible analyses and effective visual storytelling

This course is ideal for beginners and intermediate learners aiming to build analytical and visualization skills necessary for data-driven decision making in science, business, and engineering.