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Data Science with Python

Learn how to use NumPy, Pandas, Matplotlib, Plotly, Machine Learning and more !

Advanced (861 Ratings) 1529 Students Enrolled
Created By Harish Gowda Last Updated 03-Nov-2019 English
What Will I Learn?
  • Use Python for Data Science and Machine Learning
  • Implement Machine Learning Algorithms
  • Learn to use Pandas for Data Analysis
  • Learn to use NumPy for Numerical Data
  • Learn to use Matplotlib for Python Plotting
  • Use Plotly for interactive dynamic visualizations

Requirements
  • Python programming experience
  • Passion to become Data scientist
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Description

Are you ready to start your path to becoming a Data Scientist! 

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

There are about 1.5 Lakh jobs are unoccupied in Data Science, Artificial Intelligence and Big Data positions, and it is the suggestive job of the 21st century.

The career positions that you can choose are Data Analyst, Research Analyst, Data Scientist, Data Analyst, Advanced Data Analytics Professional, Business Analyst Consultant / Manager.

Curriculum For This Course
60 Lessons 7 Hours
  • Download and Install Anaconda Python 00:06:01 Preview
  • Overview of Jupyter Notebook 00:05:25
  • Introduction to Python 00:01:40 Preview
  • Python - Number, String, Variable 00:11:41
  • Python - List, Tuples, Dictionary, Set 00:12:46
  • Python 'IF ELSE' looping 00:10:47
  • Python - Function, Lambda, Map 00:13:03
  • Introduction to Numerical Python 00:03:15
  • Numpy Array 00:14:06
  • Numpy Array Operations 00:06:14
  • Indexing, Slicing in Numpy Array 00:10:09
  • Introduction to Pandas 00:02:32 Preview
  • Pandas - Introduction to Series 00:07:01
  • Pandas - Introduction to Dataframe 00:11:29
  • Dataframe - Index, Multi-index 00:08:38
  • Handling Missing data 00:07:52
  • Grouping data 00:10:37
  • Read Write - .csv, .html, .excel 00:05:20
  • Visualization of data with Pandas 00:07:27
  • Introduction to MatPlotLib 00:02:53 Preview
  • MatPlotLib - Basic plotting, Plotting terminology 00:09:40
  • MatPlotLib subplots 00:04:12
  • MatPlotLib Special plot 00:03:32
  • Introduction to Web Scraping 00:02:03
  • What is Web Scraping? 00:06:59
  • Web Scraping Process 00:06:15
  • Search element by TagName and TagByClass 00:08:50
  • What is Machine Learning? 00:03:24
  • How Machine Learning system works? 00:04:41
  • Different types of Machine Learning system - Supervised vs Unsupervised learning 00:05:01
  • Machine Learning system steps Scikit Learn Library 00:07:43
  • Parametric vs Non-parametric Machine Learning system 00:06:37
  • Introduction to Plotly 00:03:01
  • Basic plotting Plotly 00:08:00
  • Plotly - Bar chart 00:04:09
  • Plotly - Bubble chart 00:03:28
  • Plotly - Histogram and Distribution plot 00:11:48
  • Plotly - scatter and line chart 00:10:27
  • Reading Plain text file 00:04:39
  • Reading csv file 00:07:24
  • Reading Excel and Matlab file 00:04:05
  • Read Sqlite Database 00:03:52
  • Fetch data from Remote file 00:06:50
  • Fetch data from Facebook API 00:09:09
  • Introduction to Data Preprocessing 00:02:06
  • Reading data 00:05:17
  • Handling Missing data 00:08:44
  • Categorical Data 00:10:59
  • Splitting Data in Training and Testing Set 00:03:49
  • Normalize Data 00:07:38
  • EDA of pima Indian diabetes Dataset 00:11:44
  • Visualize pima Indian diabetes Dataset 00:09:34
  • Introduction 00:03:09
  • Scaling data & Standardize Data 00:07:40
  • Normalize Data & Binarize Data 00:06:02
  • Introduction 00:04:14
  • Univariate Feature Selection method 00:06:47
  • Recursive Feature Elimination 00:05:28
  • Principal component analysis 00:06:33
  • Remove feature with low variance 00:05:10
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  • 60 Lessons
  • Full Lifetime Access
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