Data Analytics using Python
Data Analytics using Python, available at $19.99, has an average rating of 4.65, with 38 lectures, based on 11 reviews, and has 32 subscribers.
You will learn about Learn Python Coding for Exploratory Data Analysis from zero base Extensive Examples of Pandas Library for Data Analysis Extensive Examples of Numpy Library to handle Multidimensional Arrays Complete Data Analysis Project Walkthrough This course is ideal for individuals who are Beginner Python Developers curious about Data Analysis, Data Science, Machine Learning It is particularly useful for Beginner Python Developers curious about Data Analysis, Data Science, Machine Learning.
Enroll now: Data Analytics using Python
Summary
Title: Data Analytics using Python
Price: $19.99
Average Rating: 4.65
Number of Lectures: 38
Number of Published Lectures: 38
Number of Curriculum Items: 38
Number of Published Curriculum Objects: 38
Original Price: ₹799
Quality Status: approved
Status: Live
What You Will Learn
- Learn Python Coding for Exploratory Data Analysis from zero base
- Extensive Examples of Pandas Library for Data Analysis
- Extensive Examples of Numpy Library to handle Multidimensional Arrays
- Complete Data Analysis Project Walkthrough
Who Should Attend
- Beginner Python Developers curious about Data Analysis, Data Science, Machine Learning
Target Audiences
- Beginner Python Developers curious about Data Analysis, Data Science, Machine Learning
Are you aspiring to learn Data Analysis using Python? if yes, then this course on Python will give you the right base, and that too in less than 10 hours.
In this course, you will learn about the basics of the Python Language, Language Elements, Multidimensional Array Handling using the Numpy Library, handling business data using Pandas Library, etc.
You will also learn the tools and techniques of Data Analysis followed by a Data Analysis Project.
Course Sections:
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Python Language in Detail
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Python internal Data Structures
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Python Language Elements
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Pandas Data Structure – Series and DataFrames
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Python Visualizations
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Data Analysis (EDA) Techniques covered exhaustively through Project work
Some of the areas you will master using the powerful Numpy and Pandas Libraries in this course:
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Data Structures: Numpy provides arrays that are optimized for numerical operations, while Pandas provides two main data structures – Series and DataFrame. You will learn how to create, manipulate, and use these structures for data analysis.
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Data Cleaning: Pandas provides a range of functions to clean and preprocess data. You can learn how to handle missing data, remove duplicates, and deal with data outliers.
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Data Aggregation: Pandas provides functions to group data by one or more variables and perform various aggregation operations on the data such as sum, count, mean, and standard deviation. Numpy provides functions to perform mathematical operations on arrays such as sum, mean, max, min, etc.
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Data Transformation: Pandas provides functions for transforming data, including reshaping, merging, and pivoting data. Numpy provides functions for slicing and indexing arrays, and for reshaping and manipulating arrays.
Happy Learning!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course Introduction
Lecture 2: Introduction to Python
Lecture 3: Starting with Python with Jupyter Notebook
Lecture 4: Python Variables and Conditions
Lecture 5: Python Iterations 1
Lecture 6: Python Iterations 2
Lecture 7: Python Lists
Lecture 8: Python Tuples
Lecture 9: Python Dictionaries 1
Lecture 10: Python Dictionaries 2
Lecture 11: Python Sets 1
Lecture 12: Python Sets 2
Lecture 13: Numpy Arrays 1
Lecture 14: Numpy Arrays 2
Lecture 15: Numpy Arrays 3
Lecture 16: Pandas Series 1
Lecture 17: Pandas Series 2
Lecture 18: Pandas Series 3
Lecture 19: Pandas Series 4
Lecture 20: Pandas DataFrame 1
Lecture 21: Pandas DataFrame 2
Lecture 22: Pandas DataFrame 3
Lecture 23: Pandas DataFrame 4
Lecture 24: Pandas DataFrame 5
Lecture 25: Pandas DataFrame 6
Lecture 26: Python User Defined Functions
Lecture 27: Python Lambda Functions
Lecture 28: Python Lambda Functions and Date-Time Operations
Lecture 29: Python String Operations
Chapter 2: Exploratory Data Analysis with Project
Lecture 1: Exploratory Data Analysis – Introduction
Lecture 2: Tools and Processes of EDA
Lecture 3: EDA-Project-1
Lecture 4: EDA-Project-2
Lecture 5: EDA-Project-3
Lecture 6: EDA-Project-4
Lecture 7: EDA-Project-5
Lecture 8: EDA-Project-6
Lecture 9: EDA-Project-7
Instructors
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Manas Dasgupta
Startup Founder, Data Science Expert
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 2 votes
- 4 stars: 0 votes
- 5 stars: 9 votes
Frequently Asked Questions
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