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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