Search
START TYPING AND PRESS ENTER TO SEARCH

    Have a Question About This Course?





    Image

    Using Data Science Tools in Python®

    Python Institute Certification Course
    An increasing number of enterprises are embracing data science as a pivotal driver for informed business strategies. Irrespective of the industry, the capacity to glean insights from data holds paramount importance in maintaining competitiveness in today's market. Python®, emerging as a frontrunner in data science, stands out with its extensive libraries, empowering data scientists to effortlessly load, analyze, manipulate, cleanse, and visualize data in user-friendly yet robust ways. This course is tailored to equip you with the essential skills to proficiently harness these pivotal libraries, enabling you to derive actionable insights from data, thereby delivering substantial value to the business.

    Using Data Science Tools in Python® Training Objectives

    • Set up a Python data science environment.
    • Manage and analyze data with NumPy arrays.
    • Manipulate and modify data with NumPy arrays.
    • Manage and analyze data with pandas DataFrames.
    • Manipulate
    • modify
    • and visualize data with pandas DataFrames.
    • Visualize data with Matplotlib and Seaborn.

    Need Assistance Finding the Right Training Solution

    Our Consultants are here to assist you
    Arrange a consultation

    Key Point of Training Programs

    We have different work process to go step by step for complete our working process in effective way.
    • Using Data Science Tools in Python® Training Prerequisites

      Target Student:

      This course is designed for students who wish to expand their ability to extract knowledge from business data. The target student for this course understands the principles and benefits of data science and has used basic data-driven tools like Microsoft® Excel® and Structured Query Language (SQL) queries, but wants to take the next steps into more advanced applications of data science.

      So, the target student may be a programmer or data analyst looking to solve business problems using powerful programming libraries that go beyond the limitations of prepackaged GUI tools or database queries; libraries that give the data scientist more fine-tuned control over the analysis, manipulation, and presentation of data.

      A typical student in this course should have several years of experience with computing technology, along with a proficiency in programming.

      Prerequisites:

      To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including but not limited to: data engineering, data analysis, data storage, data visualization, and statistics.

      You should also be proficient in programming with Python. You can obtain this level of skills and knowledge by taking the following Ratio courses:

      Python® Programming: Introduction
      Python® Programming: Advanced

    • Using Data Science Tools in Python® Training Format

      In-Person

      Online

    • Using Data Science Tools in Python® Outline

      Lesson 1: Setting Up a Python Data Science Environment
      Topic A: Select Python Data Science Tools

      Topic B: Install Python Using Anaconda

      Topic C: Set Up an Environment Using Jupyter Notebook

      Lesson 2: Managing and Analyzing Data with NumPy
      Topic A: Create NumPy Arrays

      Topic B: Load and Save NumPy Data

      Topic C: Analyze Data in NumPy Arrays

      Lesson 3: Transforming Data with NumPy
      Topic A: Manipulate Data in NumPy Arrays

      Topic B: Modify Data in NumPy Arrays

      Lesson 4: Managing and Analyzing Data with pandas
      Topic A: Create Series and DataFrames

      Topic B: Load and Save pandas Data

      Topic C: Analyze Data in DataFrames

      Topic D: Slice and Filter Data in DataFrames

      Lesson 5: Transforming and Visualizing Data with pandas
      Topic A: Manipulate Data in DataFrames

      Topic B: Modify Data in DataFrames

      Topic C: Plot DataFrame Data

      Lesson 6: Visualizing Data with Matplotlib and Seaborn
      Topic A: Create and Save Simple Line Plots

      Topic B: Create Subplots

      Topic C: Create Common Types of Plots

      Topic D: Format Plots

      Topic E: Streamline Plotting with Seaborn

      Appendix A: Scraping Web Data Using Beautiful Soup