Complete Python & Data Analytics • Beginner to Advanced (2023-10)

Complete Python & Data Analytics • Beginner to Advanced
52 sections • 311 lectures • 42h 53m total length
2023-10 | e-Learning | English | MP4, ZIP | 36.03 GB

Learn Python Programming, Data Analysis & Wrangling with Pandas, Data Visualization and Machine Learning.


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What you'll learn
- Python coding - from Zero to Hero.
- Realistic Data analysis and wrangling with Pandas.
- Reshaping, merging, joining, and all sorts of complex data manipulations.
- Handling Missing Values.
- Groupby Operations (with Finance applications).
- Time Series Resampling.
- Complex Rolling Windows Operations.
- OLS Linear Regressions.
- Logistic Regressions.
- Linear Discriminant Analysis.
- Neural Networks.
- Principal Component Analysis (PCA).
- Support Vector Machines.
- K-Nearest Neighbors Algorithm.
- K-Means Clustering.
- Professional data visualization with Seaborn.
- Real-World Applications in Finance, Image/Facial Recognition, etc.

Requirements
- No programming experience is required. You will learn everything you need to know about Python, data analytics and machine learning in this course.
- Intermediate-level coders can directly proceed to Part 2 (Data Analytics with pandas).

Description

This course is a complete guide on Python & data analytics. You will learn everything they need to know to conduct data analysis and carry out data science using Python.

In particular, this course consists of 4 major parts ("mini-courses"):

1. Python Crash Course for Beginners
- All essential data types and common operations
- Comprehensive string manipulations
- Control flows
- Lists, Tuples and Sets
- Dictionaries
- Object-Oriented Programming
- Inheritance
- Datetime
- Modules and Packages
- Exceptions Handling, etc.

2. A Comprehensive Course on Data Analysis and Manipulation using Pandas
- Series and Data Frames
- Indexing, filtering, sorting, counting, etc.
- Aggregation vs Transformation
- Groupby
- Reshape the data, pivot/melt
- merge/join
- missing values
- apply, map
- Time series computations & resampling
- Rolling windows
- Vectorized string and date/time manipulations, and many more

3. A Complete Course on Data Visualization
- pandas & seaborn
- 15+ Types of Plots (relational, statistical, categorical)
- Multi-plots with "facets", etc.

4. An Applied Machine Learning Course using SciKit-Learn
- Linear Regressions
- Logistic Regressions
- Linear Discriminant Analysis
- Principal Component Analysis
- K-Means
- K-Nearest Neighbors
- Support Vector Machines
- Neural Networks
- Hyper-parameters Tuning
- Facial Recognition
- Hand-written Digits Recognition, etc.

The course is one of the most comprehensive and detailed course ever on the Pandas package. It highlights the complexity of data wrangling which occupies about 80% of data scientists' time, and gives you a solid foundation to meet the challenging requirements of handling messy real-world data.

This course also introduces 8 most common ML algorithms and their applications in practical tasks such as image recognition and classifications. The focus is on applications and intuitive understanding rather than the underlying theories and mathematics.

By the end of this course, you will not only become a competent Python programmer, but also a skilled data analyst ready to take on real-world projects.

Who this course is for:
- Absolute Beginners.
- Intermediate-level Python coders who wants to level up their data analytics skills using Pandas and Machine Learning tools.
- All aspiring data scientists, data analysts and data engineers.
- Finance and business professionals/students (marketing, management, etc.).
- Anyone who is curious about Python coding and/or data analytics.




MORE COURSES:
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https://rg.to/folder/6531533/WEBSITE.html
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https://rg.to/folder/6432280/PROGRAMMING.html
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https://rg.to/folder/5704524/ENGLISH_COURSE.html
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