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Data Science
Certificate Course in Data Science with Python

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What you'll learn


A Data Science with Python course is designed to equip students with the skills and knowledge necessary to analyze data using Python, one of the most popular programming languages in the data science field. The course typically covers a wide range of topics, from data manipulation and visualization to machine learning and advanced data analytics. Here is an overview of what a Data Science with Python course may include:


Course Objectives:

  • To introduce the fundamental concepts of data science.
  • To teach the use of Python libraries and tools for data analysis and visualization.
  • To develop skills in data manipulation, cleaning, and exploration.
  • To provide knowledge and hands-on experience with machine learning algorithms.
  • To enable students to work on real-world data science projects.
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Course Outline

  • Introduction to Data Science
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

  • identifiers and variables
  • Keywords in Python
  • Constants in Python
  • Built in Data types
  • Numbers in Python
  • Accepting input from Console
  • Printing Statements
  • Simple Python programs
  • What is Python?
  • Installing Python
  • Python IDEs

  • Introduction to Pandas
  • DataFrames and Series
  • Data Importing and Exporting (CSV, Excel, SQL)
  • Data Cleaning (Handling Missing Values, Duplicates)
  • Data Transformation (Filtering, Sorting, Grouping, Merging)

  • Exploratory Data Analysis (EDA)
  • Descriptive Statistics
  • Data Visualization with Matplotlib
  • Advanced Plotting with Seaborn
  • Interactive Visualizations with Plotly

  • Understanding the Dataset
  • Loading datasets in Python using Pandas, NumPy, and other libraries.
  • Data Summarization
  • Addressing duplicate or inconsistent data.
  • Scaling and normalization.
  • Grouping and aggregation

  • Central Tendency
  • Probability Basics
  • Standard Deviation
  • Bias variance Tradeoff
  • Distance metrics
  • Outlier analysis
  • Missing Value treatment
  • Correlation



Learning Outcomes:

By the end of the course, students should be able to:


  • Use Python libraries and tools for data analysis and visualization.
  • Manipulate, clean, and explore data effectively.
  • Understand and apply statistical concepts in data analysis.
  • Implement and evaluate machine learning models for data science tasks.
  • Work on real-world data science projects and draw meaningful insights.

A Data Science with Python course prepares students for roles in data science, data analytics, and related fields, and provides a strong foundation for further study or career advancement in data science.

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