Introduction to data analysis using python

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Course Overview

The Mastering Python for Analytics course is designed to equip learners with the essential Python programming skills needed to perform data analysis. This comprehensive Python analytics course begins with the basics in Module 1, where students learn to execute Python code, understand the syntax, and write simple scripts. As they progress through subsequent modules, they delve into more complex topics such as functions, math operations, string manipulation, and data structures like dictionaries and sets. Emphasizing practical applications in analytics, the course covers flow control for logical operations, and object-oriented programming to structure code effectively, and introduces essential libraries like NumPy, Pandas, Seaborn, and Matplotlib. These libraries are pivotal for data analysis, allowing students to handle large datasets, perform statistical analyses, and create compelling visualizations. By the end of this Python for Analytics course, learners will have a solid foundation in Python programming and the skills to analyze and visualize data proficiently.

Learning Objectives and Outcomes

  • Understand the fundamentals of Python scripting, including variables, functions, modules, and how to write Python code effectively.
  • Learn to manipulate data using Python’s built-in capabilities for numerical computations, string operations, and data structures such as lists, dictionaries, and sets.
  • Develop proficiency in controlling program flow with conditional statements, loops, and exception handling to execute complex tasks.
  • Gain the ability to define and use classes and objects, understanding concepts like inheritance, encapsulation, and polymorphism in Python.
  • Master the use of NumPy for efficient array manipulation, scientific computing, and performing advanced data analysis tasks.
  • Explore Pandas for data analysis, including data manipulation, cleaning, exploration, and visualization with Series and DataFrames.
  • Utilize Seaborn and Matplotlib for data visualization, learn to create a variety of plot types, and customize graphical representations for better data insights.
  • Grasp statistical data analysis and visualization techniques to interpret data and make informed decisions backed by Python’s analytical capabilities.
  • Learn to import, export, and manipulate data, and apply advanced indexing and array operations to prepare data for analysis.

Course Prerequisites

To ensure that you can successfully undertake the Mastering Python for Analytics course, the following are the minimum required prerequisites:

  • Basic understanding of programming concepts (such as variables, loops, and functions)
  • Familiarity with any programming language (prior experience with Python is helpful but not mandatory)
  • Basic knowledge of how to navigate and perform operations on a computer
  • Willingness to learn and problem-solve
  • Ability to install software and set up a development environment on your computer (guidance will be provided during the course)

Target Audiance

  • Data Analysts
  • Business Analysts
  • Data Scientists
  • Software Engineers interested in data science
  • IT Professionals looking to transition into analytics roles
  • Researchers requiring data analysis tools
  • Marketing Analysts
  • Financial Analysts

Schedule Dates

Introduction to data analysis using python
15 July 2024 - 18 July 2024
Introduction to data analysis using python
21 October 2024 - 24 October 2024
Introduction to data analysis using python
27 January 2025 - 30 January 2025
Introduction to data analysis using python
28 April 2025 - 01 May 2025

Course Content

  • Introduction to basic syntax, data types, and control structures in Python.

  • Techniques for cleaning, transforming, and manipulating data using the Pandas library.

  • Exploring various plotting techniques for visualizing data effectively.

  • Utilizing NumPy for statistical computations and analysis.

  • Introduction to EDA techniques to understand data distributions, patterns, and relationships.

  • Overview of machine learning concepts and algorithms using scikit-learn.

  • Application of learned concepts through hands-on projects and case studies relevant to data analysis in various industries

FAQs

Data analysis involves examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making processes. It’s crucial for understanding trends, making predictions, and gaining insights into various phenomena.

Python is a powerful and versatile programming language with extensive libraries like Pandas, NumPy, and Matplotlib specifically tailored for data analysis. It’s widely used in the industry, offering robust tools and a supportive community.

Basic understanding of Python programming and familiarity with concepts like variables, loops, functions, and conditional statements would be beneficial. However, absolute beginners are welcome, as this course is designed to accommodate learners of all levels.

Our course offers a blended learning approach, combining pre-recorded video lectures, interactive assignments, and live instructor-led sessions. This format provides flexibility for students to learn at their own pace while still receiving guidance and support from experienced instructors.

Yes, we recommend students to bring their laptops equipped with Python and relevant libraries installed. Detailed installation instructions will be provided prior to the course start date to ensure smooth participation.

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