Data Science Course

Data Science Course

This course provides a headstart to the fundamentals of data science, covering key concepts, techniques, and methodologies used to analyze and extract insights from data. Students will learn how to collect, clean, analyze, and visualize data using popular tools and programming languages. The course will include hands-on exercises and projects to reinforce learning and develop practical data science skills.

Learning Outcome

This course outline provides a structured approach to learning data science fundamentals, covering key topics and skills necessary for understanding and applying data science techniques in various contexts. You will be job ready after taking this course

  • Gain proficiency in Data Transformation
  • Big Data Technologies
  • Develop skills in data visualization
  • Build job-ready portfolios

Course Outline

Here's a simple breakdown of what you'll learn if you enroll for the Data Science Course Today:.

  • Overview of data science and its applications
  • Data Cleaning - Introduction to jupyter notebook
  • Importing data into the jupyter notebook
  • Checking for missing values, duplicates and mislabels

  • What are outliers and How to identify them
  • Standard deviation methods
  • Interquartile Range

  • Identifying Missing Values
  • Removing Missing Values

  • Mean, Median, Mode

  • What is feature selection
  • Statistics for feature selection
  • Feature selection with any data type
  • Categorical feature selection
  • Modeling with selected features

  • Numerical Feature Selection
  • Modeling with selected features

  • Regression Dataset
  • Numerical Feature Selection
  • Modeling with selected features

  • Recursive feature elimination
  • RFE with Scikit-learn

  • Numerical data scaling methods
  • Minimax scales transform
  • Standard scales transform
  • IQR scales transform

  • Nominal & Ordinal Variables
  • Encoding Categorical Data
  • Onehot encoder transform

  • Change Data Distribution
  • Discretization Transforms
  • Uniform Discretization Transform
  • Polynomial Features
  • Polynomial Features Transform

  • Introduction to big data technologies (Hadoop, Spark)
  • Processing large datasets using distributed computing frameworks
  • Hands-on exercises with big data tools

  • Principles of effective data visualization
  • Tools for data visualization (matplotlib, seaborn, Tableau)
  • Designing charts, graphs, and dashboards
  • Interactive visualization techniques

  • Applying data analysis techniques to a real-world dataset
  • Project planning, data exploration, analysis, and visualization
  • Presentation of findings and insights