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- Instant access to 11,000+ online courses
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HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- Basics, OOP, Numpy, Pandas, Seaborn, Matplotlib, Jupyter Notebook, Data Cleaning, Data Wrangling, all this with Python
Course Overview
· What you will learn
· Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
· Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas & Numpy.
· Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
· Data Wrangling
In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.
· Exploratory Data Analysis
· In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.
Pre-requisites
- Basic windows 10 computer & laptop with minimum intel core 2 due processor & 4 GB ram
- Desired to be a data Analyst
- No coding Experience required to learn.
Target Audience
- The guy who want to learn the Basic windows 10 computer & laptop with minimum intel core 2 due processor & 4 GB ramdata Analysis & data wrangling & cleaning & data
- data Analysis & data wrangling & cleaning & data Visualization's & also the data Scrapping and then Analysing the data with only python.
- No coding Experience required to learn.
Curriculum 35 Lectures 03:33:38
-
Section 1 : Python All Lectures
- Lecture 2 :
- Basic variables
- Lecture 3 :
- Basic operators
- Lecture 4 :
- Basic function's like input print int float str comment etc
- Lecture 5 :
- Basics string function's
- Lecture 6 :
- Basic tuples
- Lecture 7 :
- Basic List
- Lecture 8 :
- Basic Set
- Lecture 9 :
- Basic Dictionary
- Lecture 10 :
- Basic If Else
- Lecture 11 :
- Basic While Loop
- Lecture 12 :
- Basic for Loop
- Lecture 13 :
- Basic Function's Def, lambda, map, filter
- Lecture 14 :
- Basic Reduce function
- Lecture 15 :
- Advance Class
- Lecture 16 :
- Advance Constructors in class
- Lecture 17 :
- Inheritence in the class
- Lecture 18 :
- AutoViz_Class Library Basics
- Lecture 19 :
- Numpy part 1
- Lecture 20 :
- Numpy part 2
- Lecture 21 :
- Pandas part 1 in python
- Lecture 22 :
- Pandas part 2 in python
- Lecture 23 :
- Pandas part 3 data frames in python
- Lecture 24 :
- Pandas part 4 data frames in python
- Lecture 25 :
- matplotlib part 1
- Lecture 26 :
- matplotlib part 2
- Lecture 27 :
- Seaborn part 1
- Lecture 28 :
- Seaborn part 2
- Lecture 29 :
- AutoViz_Class Library Advance
- Lecture 30 :
- Webscrapping part 1
- Lecture 31 :
- Webscrapping part 2
- Lecture 32 :
- PowerBi part 1
- Lecture 33 :
- MS Excel part 1
- Lecture 34 :
- MS Excel part 2
- Lecture 35 :
- MS Excel part 3
Our learners work at
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