Data Science Foundation

Course Start Date: 23rd July

Training Duration: 10 Days

This beginner course on data science provides an introduction to the field and its various applications. The course covers key concepts and techniques used in the data science process, including data exploration and visualization, cleaning and preprocessing, data analysis and statistical modeling, data wrangling, machine learning, deep learning, natural language processing, and data visualization. The course also includes a project where participants will have the opportunity to apply their learning to a real-world data science problem. The course is designed to give participants a strong foundation in data science and to provide them with the kno

Learning Outcomes


Provide an overview of Data Science and its various applications


Familiarize participants with the data science process and setting up a development environment


Teach techniques for data exploration, visualization, cleaning, and preprocessing

Introduce participants to data analysis and statistical modeling using tools such as pandas, matplotlib, seaborn, and scikit-learn. Teach data wrangling techniques using SQL and other tools.


Introduce participants to machine learning and deep learning, including supervised and unsupervised learning algorithms, and building models using TensorFlow.


Teach advanced visualization techniques and building interactive dashboards.

Course Outlines

  • Overview of Data Science and its applications
  • Understanding the data science process
  • Setting up a development environment
  • Importing and exploring data using pandas
  • Creating visualizations using matplotlib and seaborn
  • Understanding the importance of data cleaning
    • Techniques for handling missing and duplicate data
    • Data normalization and standardization
    • Encoding categorical variables



  • Description statistics: understanding measures of central tendency and dispersion
  • Basic probability theory
  • Inferential statistics and basic distributions
  • Techniques for handling large and complex data
  • Data reshaping and merging
  • Using SQL and other data wrangling tools
  • Advanced visualization techniques using Plotly and bokeh
  • Creating interactive dashboards and visualizing geospatial data
  • Introduction to machine learning and its applications
  • Understanding supervised and unsupervised learning
  • Linear regression and its applicationsĀ 
  • Understanding classification: k-Nearest Neighbors
  • Logistic regression and classification metrics
  • Decision Trees, Model Ensembling, and Random Forests.
  • Introduction to deep learning and its applications
  • Neural networks and backpropagation
  • Building deep learning models using TensorFlow
  • Techniques for processing and analyzing text data
  • Sentiment analysis and text classification
  • Named entity recognition and parts-of-speech tagging
    • Working on a real-world data science project
    • Understanding the ethical considerations of data science
    • Next steps and resources for continuing learning