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Data Analysis & Visualization Bootcamp - 2020 BESTSELLER

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Free Coupon Discount - Financial Data Analysis and Visualization with Python: The HighQuality Study kit ranked as bestseller

  • Created by Python Data Analytics Data Science
  •  English


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Description
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.





Who this course is for:
Beginner Python Data Analyst should take up this course.
Intermediate Python Data Analyst should take up this course.

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