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[BESTSELLER]™ Python Data Analysis & Visualization Bootcamp


[BESTSELLER]™ Python Data Analysis & Visualization Bootcamp

Financial Data Analysis and Visualization with Python: The HighQuality Study kit ranked as bestseller

What you'll learn

  • Perform Data Analytics seamlessly and smartly


  • Experience in using a Computer - Windows/Linux/Mac
  • Basic Mathematical Knowledge


Welcome to Data Analysis Analytics Bootcamp content powered by TakenMind.

Are you interested to learn how zetabytes of data are processed by top tech companies to analyse data inorder to boost their business growth? Well, for a beginner you are at the right place and this is the most probably the right time for you to learn this.

The average data scientist today earns $123,000 a year, according to Indeed research. But the operating term here is “today,” since data science has paid increasing dividends since it really burst into business consciousness in recent years.

This course has its base on financial Analysis and the following concepts are covered:

  • Python Fundamentals
  • Pandas for Efficient Data Analysis
  • NumPy for High Speed Numerical Processing
  • Matplotlib for Data Visualization
  • Pandas for Data Manipulation and Analysis
  • Seaborn Data Visualization
  • Worked-up examples.

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

You will learn how to:

  • Import data sets
  • Clean and prepare data for analysis
  • Manipulate pandas DataFrame
  • Summarize data
  • Build machine learning models using scikit-learn
  • Build data pipelines

Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:

  • Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
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