Data Analysis With Python For Beginners- Learn By Practice

Data Analysis With Python For Beginners: Learn By Practice, available at $59.99, has an average rating of 4.65, with 100 lectures, based on 105 reviews, and has 3160 subscribers.

You will learn about How to use Numpy & Pandas to perform data analysis. How data is manipulated in the form of series and Data Frames. How to extract, clean and load data for analysis. Different kinds of methods used in the process of analyzing raw data. This course is ideal for individuals who are Students who want to start their career in the data science domain . or Anyone who wants to use Python for data analysis. It is particularly useful for Students who want to start their career in the data science domain . or Anyone who wants to use Python for data analysis.

Enroll now: Data Analysis With Python For Beginners: Learn By Practice

Summary

Title: Data Analysis With Python For Beginners: Learn By Practice

Price: $59.99

Average Rating: 4.65

Number of Lectures: 100

Number of Published Lectures: 100

Number of Curriculum Items: 100

Number of Published Curriculum Objects: 100

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

How to use Numpy & Pandas to perform data analysis.

How data is manipulated in the form of series and Data Frames.

How to extract, clean and load data for analysis.

Different kinds of methods used in the process of analyzing raw data.

Who Should Attend

Students who want to start their career in the data science domain .

Anyone who wants to use Python for data analysis.

Target Audiences

Students who want to start their career in the data science domain .

Anyone who wants to use Python for data analysis.

Data Science With Python

Learn how to perform data analysis in Python using the powerful Pandas library.

Here Is What You Get By Enrolling In This Course:

Word-By-Word Explanation: In the entire course, I explain each line of code, without skipping a single line of code.

Awesome Quality Content: Over 5+ hours of HD(1080p) Videos.

Well Structured & Easy To Learn: This course teaches you the exact process of performing analysis on a set of data and drawing meaningful conclusions from it.

24 X 7 Support: I will always be there to guide you in your journey to become Python project expert.

Note: Student queries and problems will be answered immediately.

Here Is Everything You Will Learn In This Complete Course:

The Complete Course is divided into 10 Major sections:

Section 1: Introduction to Numpy.

Section 2: Series & Data Frames.

Section 3: Arithmetic between series and data frame.

Section 4: Functions, sorting & ranking.

Section 5: Handling data & Indexing.

Section 6: Data loading.

Section 7: Merging & reshaping.

Section 8: Data visualization.

Section 9: Data transformation.

Section 10: Time series.

So let’s begin the journey of becoming an expert in Data Analysis.

In addition to the Udemy 30-day money back guarantee, you have my personal guarantee that you will love what you learn in this course. If you ever have any questions please feel free to message me directly and I will do my best to get back to you as soon as possible!

Make sure to enrol in the course before the price changes.

Take yourself one step closer towards becoming a professional Data Analyst by clicking the “take this course button” now!

Join the journey.

Sincerely,

Ashutosh Pawar

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to data analysis

Lecture 2: Installing required tools

Chapter 2: Introduction to Numpy

Lecture 1: Creating a Numpy Array

Lecture 2: Arithmetic operations betweeen Numpy array

Lecture 3: Indexing & Slicing a Numpy array

Lecture 4: Practice Example 1

Lecture 5: Practice example 1 solution

Lecture 6: Practice Example 2

Lecture 7: Practice example 2 solution

Lecture 8: Practice Example 3

Lecture 9: Practice example 3 solution

Lecture 10: Practice Example 4

Lecture 11: Practice example 4 solution

Chapter 3: Series & Data Frames

Lecture 1: Creating a Series

Lecture 2: Creating a Data Frame

Lecture 3: Reindexing a DataFrame and Series

Lecture 4: Practice Example 5

Lecture 5: Practice example 5 solution

Lecture 6: Practice Example 6

Lecture 7: Practice example 6 solution

Lecture 8: Practice example 7

Lecture 9: Practice example 7 solution

Lecture 10: Practice example 8

Lecture 11: Practice example 8 solution

Chapter 4: Arithmetic between Data Frame & Series

Lecture 1: Arithmetic methods with fill values

Lecture 2: Arithmetic between Data Frame and Series.

Lecture 3: Practice example 9

Lecture 4: Practice example 9 solution

Lecture 5: Practice example 10

Lecture 6: Practice example 10 solution

Chapter 5: Functions, Sorting & Ranking

Lecture 1: Function application and mapping

Lecture 2: Sorting and ranking

Lecture 3: Duplicate values

Lecture 4: Practice example 11

Lecture 5: Practice example 11 solution

Lecture 6: Practice example 12

Lecture 7: Practice example 12 solution

Chapter 6: Handling Data & Indexing

Lecture 1: Handling missing data

Lecture 2: Hierarchical indexing

Lecture 3: Practice example 13

Lecture 4: Practice example 13 solution

Lecture 5: Practice example 14

Lecture 6: Practice example 14 solution

Lecture 7: Practice example 15

Lecture 8: Practice example 15 solution

Chapter 7: Data Loading

Lecture 1: Introduction to data loading

Lecture 2: Reading data from multiple files

Lecture 3: Reading partial data from files

Lecture 4: Practice example 16

Lecture 5: Practice example 16 solution

Chapter 8: Merging & Reshaping

Lecture 1: Perform merge on datasets

Lecture 2: Merge with multiple keys

Lecture 3: Concat

Lecture 4: Combining values with overlap

Lecture 5: Reshaping

Lecture 6: Practice example 17

Lecture 7: Practice example 17 solution

Lecture 8: Practice example 18

Lecture 9: Practice example 18 solution

Lecture 10: Practice example 19

Lecture 11: Practice example 19 solution

Lecture 12: Practice example 20

Lecture 13: Practice example 20 solution

Lecture 14: Practice example 21

Lecture 15: Practice example 21 solution

Lecture 16: Practice Example 22

Lecture 17: Practice example 22 solution

Lecture 18: Practice Example 23

Lecture 19: Practice example 23 solution

Lecture 20: Practice Example 24

Lecture 21: Practice example 24 solution

Lecture 22: Practice Example 25

Lecture 23: Practice example 25 solution

Chapter 9: Data Visualization

Lecture 1: Introduction to matplotlib

Lecture 2: Plotting subplots

Lecture 3: Plotting multiple figures

Lecture 4: Adding text

Lecture 5: Plotting DataFrame & Series

Lecture 6: Practice Example 26

Lecture 7: Practice example 26 solution

Lecture 8: Practice Example 27

Lecture 9: Practice example 27 solution

Lecture 10: Practice Example 28

Lecture 11: Practice example 28 solution

Lecture 12: Practice example 29

Lecture 13: Practice example 30

Lecture 14: Practice example 30 solution

Chapter 10: Data Transformation

Lecture 1: Data Transformation part 1

Lecture 2: Data Transformation part 2

Lecture 3: Filtering outliers

Instructors

Ashutosh Pawar
Python & Django Geek, Software Engineer, Entrepreneur.

Rating Distribution

1 stars: 3 votes

2 stars: 5 votes

3 stars: 11 votes

4 stars: 36 votes

5 stars: 50 votes

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