Complete Python & Data Analytics: Beginner to Advanced

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

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