Python Data Analytics

About Course

Embark on a comprehensive educational journey with our course, “Python Data Analytics.” Whether you are new to data analysis or seeking to enhance your proficiency in Python, this course is designed to take you from a beginner level to an advanced understanding of data analytics using Python. Uncover the intricacies of data manipulation, visualization, and analysis through a carefully curated curriculum that covers fundamental concepts and progresses to advanced techniques. Acquire practical skills and knowledge that are essential for effective data analysis and interpretation.

Course Highlights:

Foundations of Python:
Introduction to Python: Get acquainted with Python, understanding its syntax, data structures, and basic programming principles.
Setting Up Your Environment: Learn to set up your Python development environment with tools such as Jupyter Notebook and Anaconda.

Data Manipulation with Pandas:
Getting Started with Pandas: Discover the powerful Pandas library for data manipulation and analysis.
DataFrames and Series: Learn to create, modify, and manage DataFrames and Series for efficient data handling.
Data Cleaning: Master techniques for cleaning and preprocessing data to ensure accuracy and consistency.

Data Visualization:
Matplotlib and Seaborn: Explore the essential libraries for data visualization in Python.
Creating Plots and Charts: Learn to create various types of plots and charts to visually represent data insights.
Advanced Visualization Techniques: Delve into more complex visualizations to uncover deeper insights from your data.

Statistical Analysis:
Descriptive Statistics: Understand the basics of descriptive statistics to summarize and describe data.
Inferential Statistics: Learn techniques for making inferences about populations based on sample data.
Hypothesis Testing: Master the fundamentals of hypothesis testing and its applications in data analysis.

Machine Learning with Scikit-Learn:
Introduction to Machine Learning: Gain an overview of machine learning concepts and their significance in data analytics.
Supervised Learning: Learn techniques for supervised learning, including regression and classification models.
Unsupervised Learning: Explore unsupervised learning methods such as clustering and dimensionality reduction.

Advanced Data Analysis:
Time Series Analysis: Understand techniques for analyzing time series data and forecasting trends.
Text Mining and NLP: Learn the basics of natural language processing to analyze textual data.
Big Data Handling: Explore strategies for working with large datasets using tools like Dask and PySpark.

Practical Projects:
Real-World Applications: Apply your newfound knowledge in real-world scenarios through hands-on projects.
Case Studies: Analyze case studies to understand practical applications of data analytics techniques.

Career Development:
Industry Insights: Receive insights into industry trends and job market demands related to Python data analytics skills.
Enhancing Employability: Enhance your employability by gaining practical skills that are directly applicable to various professional roles requiring data analytics proficiency.

Embark on this transformative learning experience with our “Python Data Analytics” course and unlock the full potential of Python for data manipulation, analysis, and visualization. Whether you’re aiming to pursue a career in data science, data analysis, or business intelligence, this course equips you with the essential skills needed for success in the dynamic field of data analytics.

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Benefits of the course

### Understand the Core Concepts and Principles of Python:
- Gain a thorough understanding of Python's syntax, data types, and core principles.
- Explore Python's versatility and how it can be used for various applications such as web development, data analysis, artificial intelligence, and automation.
### Master Programming with Python:
- Learn the fundamentals of programming, including variables, control structures, functions, and error handling.
- Understand the object-oriented programming paradigm and how to apply it in Python.
### Implement and Manage Python Projects:
- Create, modify, and manage Python projects using virtual environments and package managers.
- Develop modular and maintainable code by organizing your projects with packages and modules.
### Develop Advanced Python Skills:
- Enhance your proficiency in Python by learning advanced topics such as decorators, context managers, and metaprogramming.
- Explore Python's powerful standard libraries and how to utilize them effectively.
### Optimize Performance and Efficiency:
- Learn techniques to optimize Python code for performance, such as using built-in functions, efficient data structures, and profiling tools.
- Understand the importance of algorithmic efficiency and how to implement efficient algorithms in Python.
### Handle Data Processing and Analysis:
- Master the processes for data manipulation and analysis using libraries such as Pandas, NumPy, and Matplotlib.
- Learn to read, write, and process data from various sources including CSV, Excel, SQL databases, and APIs.
### Utilize Python's Advanced Features:
- Learn about Python's unique features such as list comprehensions, generator functions, and the use of lambda functions.
- Explore Python's capabilities for asynchronous programming and concurrency.
### Implement Data Security and Compliance:
- Understand best practices for securing Python applications, including secure coding practices, data encryption, and authentication mechanisms.
- Ensure compliance with data protection regulations when handling sensitive data in Python projects.
### Leverage Python for Web Development:
- Learn how to build web applications using frameworks such as Django and Flask.
- Understand how to integrate Python with front-end technologies and create full-stack web applications.
### Apply Knowledge in Real-World Scenarios:
- Work on practical projects and hands-on exercises that simulate common challenges faced in software development and data analysis using Python.
- Develop problem-solving skills and apply Python knowledge to create robust and scalable solutions.

Course Content

Why Python
Where Python can be used
How Python works internally
Python and IDE Installation Guide

Naming conventions (e.g., snake_case, camelCase)
Keywords (reserved words)
Naming restrictions
Python Keywords

Declaring and assigning values
Different data types (covered later)
Scope (local vs. global)

Using input() function for user input
Printing output with print() function
Formatting output with f-strings or other methods

Arithmetic operators (+, -, *, /, %, //, **)
Comparison operators (==, !=, , =)
Logical operators (and, or, not)
Assignment operators (=, +=, -=, *=, /=, etc.)

Numbers (int, float)
Strings
Booleans
Collections (lists, tuples, dictionaries, sets)

Defining functions with def keyword
Arguments and parameters
Return values

Importing modules using import statement
Using functions and variables from modules
Creating your own modules and packages

What is NumPy and why use it?
Advantages of NumPy over Python lists for numerical data
Installation and basic setup

Importing the NumPy library (import numpy as np)
Converting Python lists to NumPy arrays (np.array())
Generating arrays using NumPy functions:

Indexing – Accessing specific elements (array[index])
Slicing – Extracting sub-arrays (array[start:end:step])
Arithmetic operations:(+, -, *, /)
Mathematical functions: (np.sin(array))
Comparison operators:(>, <, ==)

Reshaping arrays using np.reshape() and np.ravel()
Combining arrays using concatenation (np.concatenate()) and stacking (np.stack())
Splitting arrays using np.split()
Copying and modifying arrays (np.copy())

Creating matrices and vectors from arrays
Dot product (np.dot()) for matrix multiplication and vector dot products
Matrix operations: transpose (np.transpose()), inverse (np.linalg.inv())

Pandas library overview and its purpose
Setting up a development environment to use Pandas
Core data structures in Pandas: Series, DataFrame, and Panel (with explanations of dimensions and data types)

Series: creation, data access (using position and labels), and manipulation
DataFrame: creation (from various data sources like lists, dictionaries),selecting/adding/deleting columns and rows
Panel: creation and data selection

Essential DataFrame operations (accessing data, subsetting, sorting
Descriptive statistics functions for summarizing data
Applying functions to DataFrames (table-wise, row/column-wise, element-wise

Reindexing and filling missing data while reindexing
Renaming elements
Iterating through DataFrames (using different methods)
Sorting DataFrames (by labels and using sorting algorithms)
Working with text data (cleaning and manipulation)
Options and customization for Pandas behavior

Indexing and selecting data using .loc, .iloc, and .ix methods with explanations of notations
Statistical functions for data analysis (percent change, covariance, correlation, ranking)
Performing aggregations on DataFrames (applying functions to groups)
Handling missing data (cleaning, filling, and dropping)
Working with grouped data (splitting, viewing, iterating, selecting groups, applying aggregations, transformations, and filtering)
Merging and joining DataFrames
Concatenating objects (combining DataFrames)

Reading data from CSV files using Pandas
Reading data from CSV files using Pandas
Potential pitfalls and best practices when using Pandas
Comparison of Pandas with SQL for data analysis tasks

Creating different plot types (line plots, scatter plots, bar charts, pie charts, histograms, etc.)
Customizing plots with labels, titles, legends, and annotations
Working with data from various sources (lists, NumPy arrays, CSV files

Subplots and combining multiple plots
Customization: colors, markers, line styles, fonts
Axes control: setting limits, scales, and ticks
Working with dates and time series data

3D plotting with Matplotlib
Using object-oriented approach for more control
Interactive plotting with libraries like IPython
Creating custom Matplotlib elements and visualizations

Saving plots as images in different formats
Animations with Matplotlib
Integration with other data analysis libraries (pandas, Seaborn)

Instructor

Krishna Mishra

Python Developer

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Free

30-Day Money-Back Guarantee

Update: July 18, 2024
Lectures100
Skill LevelIntermediate
LanguageEnglish
Course Duration: 10h

Your Instructors

Krishna Mishra

We are delighted to introduce Krishna, our accomplished Python instructor, who brings extensive experience and enthusiasm to our educational community. With substantial hands-on experience in the tech industry, Krishna has collaborated with numerous top-tier companies and currently excels as a Senior Software Engineer.

Krishna's role involves leading groundbreaking projects and mentoring a team of adept developers, showcasing exceptional leadership and technical skills.

Krishna excels at deconstructing complex programming concepts into manageable, engaging lessons, ensuring that students not only understand the material but can also apply it effectively in practical scenarios. His teaching philosophy is centered on making learning interactive and accessible for everyone, regardless of their starting level.

We are confident that with Krishna as your mentor, you'll acquire the skills and knowledge necessary to thrive in the dynamic world of technology and programming.

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Requirements

The course materials for "Python Data Analytics" include comprehensive lecture notes, hands-on practice exercises, access to a database for practical application, and supplementary resources such as video tutorials and reference materials.

Target Audience

Beginners: Individuals new to Python programming and data analysis who want to build a strong foundation in these areas.
Advanced Users: Data professionals with some experience in Python who wish to deepen their knowledge and tackle more complex data challenges.

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