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Mega Python - Pandas, Numpy, ML, APIs, GraphQL, AWS, PySpark

Adnan Waheed

4.6

Based on 48 ratings

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Adnan Waheed

Welcome to Mega Python. This course will guide you through everything you need to know to use Python for practical use and more. I’ve worked for Bloomberg for 17+ years and will present the knowledge to help you in this course. This course is a 'Mega Course', packed with so many practical topics to help you success practically. We'll cover the following topics:

Python Fundamentals
NumPy for High Speed Numerical Processing
Pandas for Efficient Data Analysis
Matplotlib for Data Visualization
Pandas Time Series Analysis Techniques
Statsmodels
Importing financial markets data
Create interactive financial charts with plotly
Time series analysis with indexing, filling and resampling
Create interactive data apps with streamlit
Data visualization with Dash

Why you should listen to me...

In my career, I have built an extensive level of expertise and experience in both areas: Finance and Coding

Finance:

17 years experience in Bloomberg for the Finance and Investment Industry...
Build various financial markets analytics companies like
- KlickAnalytics,
- Cryptoquote
- ClickAPIs and more

Python & Pandas:

My existing companies extensively used python based models and algorithms
Code, models, and workflows are Real World Project-proven

Best Seller author on Udemy

e.g. PostgreSQL Bootcamp: Go from Beginner to Advanced, 60+ Hours course
Master Redis - From Beginner to Advanced, 20+ hours
Python for Finance

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What's inside

Syllabus

Setting up the Environment

Install python

Python3 and python

The python interpreter

Writing our first python code

Python IDLE program

Installing Anaconda

Create your first python notebook

Setting up IDE - Visual Studio Code

String functions

Intro to Numbers

Built-in functions for numbers

The double equality sign

Getting User Input

Python Operators

Logical Operators

Comparison Operators

Boolean Operators

Python List

Adding and removing elements in a list

Popping items from a list

Removing an item by value

Permanent and temporarily sort

Reverse a list

Avoiding Index errors

Numerical List

min, max and sum functions

Negative Indexing

Multi-diementional list

Range function

Looping multi-dimentional list

Slicing of a list

Slicing a List Part 2

Iterate over multiple list

Check if an item exist or not

Count total occurrence of an item

Membership operators

Nested List

List Comprehensions

List Comprehensions with if clause

Nested List Comprehensions

Flatten a list of lists

Remove duplicates from the list

tuple constructor

Access tuple items

Nested Tuples

Slicing a tuple

Change Tuple item

Change Tuple item?

Concatenation and Repetition

Iterate through a tuple

Tuple Sorting

Tuple Packing & Unpacking

Tuple count() method

Tuple index() method

all function with tuple

any() function with tuples

sum() function with tuples

enumerate() function with tuples

Create, Set Constructor, Add and remove methods

Find Length, clear all elements, and iterate all elements

pop() method

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Pandas time series analysis techniques, which are essential for analyzing financial data and identifying trends over time

Begins with Python fundamentals, which provides a solid foundation for those new to the language before diving into more advanced topics

Includes Matplotlib for data visualization, which allows learners to create informative charts and graphs to communicate insights effectively

Teaches Streamlit, which enables the creation of interactive data apps, allowing users to explore and analyze data in a dynamic way

Explores data visualization with Dash, which is useful for building interactive web applications with complex dashboards and visualizations

Requires learners to install Anaconda, which may be difficult for some learners who are not familiar with package and environment management

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Reviews summary

Broad python ecosystem overview

According to learners, this "Mega Python" course lives up to its name, providing broad coverage of many Python areas like data analysis, ML, APIs, and cloud. Students praise the instructor's tremendous knowledge and experience, especially his finance background, and find the explanations clear. The core sections like Pandas and Numpy are particularly strong. However, multiple reviews note that while broad, the course offers more of a high-level introduction to advanced topics such as ML, AWS, and PySpark, suggesting these areas may require further study elsewhere. Overall, it's considered a solid foundation and great value, especially for beginners or those seeking a wide overview.

Serves well as a broad starting point.

"Overall, a solid foundation."

"It's a great resource if you want a broad understanding..."

"Good introduction to a wide array of Python uses."

"Excellent starting point."

"This course provided a good overview of many Python topics."

Pandas and Numpy sections are well done.

"The sections on Pandas and Numpy were particularly strong."

"The early parts (Python basics, Pandas, Numpy) are well-explained."

"The data analysis parts were solid."

"The core Python, Pandas, Numpy sections were strong."

"The instructor's clarity in explaining core concepts, especially in Pandas and Numpy, made complex topics accessible."

Instructor has deep expertise and experience.

"The instructor has tremendous knowledge and experience."

"It was very helpful to learn from his long experience in finance and coding."

"The instructor is knowledgeable."

"The instructor's practical experience shines through."

"The instructor's clarity in explaining core concepts... made complex topics accessible."

Covers many Python domains and libraries.

"This course covered wide area about Python, Pandas, Numpy, APIs, Graphql, AWS, Pyspark, etc. Highly recommended."

"What a mega course indeed! It lives up to its name, covering so much ground."

"The breadth of topics covered is amazing."

"Good introduction to a wide array of Python uses."

"It's a great resource if you want a broad understanding of how Python is used across different domains..."

Advanced modules feel rushed or superficial.

"...some advanced topics like PySpark and AWS felt a bit rushed compared to others."

"...sometimes the explanations could be a bit more in-depth, especially for the later modules like ML or AWS, which are complex subjects."

"...The later sections, particularly ML and PySpark, felt superficial."

"I needed to supplement with other resources for ML and AWS."

"I found the later sections (ML, APIs, AWS) to be more of a high-level introduction, requiring further study elsewhere."

"It's more of an introduction to these topics rather than a deep dive."

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Career center

Learners who complete Mega Python - Pandas, Numpy, ML, APIs, GraphQL, AWS, PySpark will develop knowledge and skills that may be useful to these careers:

Data Analyst

A data analyst interprets data to identify trends and patterns. This role helps companies make better business decisions. A course in Mega Python, with its focus on NumPy and Pandas, two crucial Python libraries for data manipulation and analysis, helps build a foundation for this career. The course also delves into time series analysis techniques, which may be useful for analyzing trends over time. Furthermore, it covers data visualization with Matplotlib and Dash, tools utilized to communicate findings effectively through charts and graphs. Anyone aspiring to become a data analyst may find this course helpful.

See salaries and explore the career path for Data Analyst

Financial Analyst

A financial analyst evaluates financial data and provides recommendations to businesses and individuals regarding investment strategies. This career often involves building financial models and analyzing market trends. A course in Mega Python, which covers importing financial markets data and creating interactive financial charts with Plotly, is relevant to this career path. The course’s coverage of Pandas for efficient data analysis also builds a strong basis for financial modeling and analysis of financial statements. The course may be useful for anyone looking to apply Python skills to the finance industry.

See salaries and explore the career path for Financial Analyst

Machine Learning Engineer

A machine learning engineer develops and implements machine learning algorithms. This involves working with large datasets and applying statistical techniques to extract insights and build predictive models. A course in Mega Python may be an excellent way to enter this field. The course mentions machine learning, and it covers NumPy and Pandas. These tools are essential for pre-processing data and implementing machine learning models. The course may be especially helpful to those who intend to use Python for machine learning projects.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A data scientist uses programming, statistics, and machine learning to extract meaningful insights from data. This role involves designing experiments, building predictive models, and communicating findings to stakeholders. A course in Mega Python, which provides a broad overview of various Python tools and their applications, is useful. The course covers fundamental Python concepts, data manipulation with Pandas, numerical processing with NumPy, and data visualization with Matplotlib. All of these may be useful to a data scientist. This may also be helpful because it discusses machine learning.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

A quantitative analyst, often called a quant, develops and implements mathematical models for pricing derivatives, managing risk, or identifying trading opportunities. A course in Mega Python can be a stepping stone toward this career. The course, with its coverage of NumPy for numerical processing, Pandas for data analysis, and importing financial markets data would be helpful for building quantitative models. The material on time series analysis and statistics may also be beneficial, as these are commonly used in quantitative finance.

See salaries and explore the career path for Quantitative Analyst

Business Intelligence Analyst

A business intelligence analyst analyzes data to identify trends and insights that can help improve business performance. This involves gathering data from various sources, creating reports and dashboards, and communicating findings to stakeholders. A course in Mega Python, with its focus on Pandas for efficient data analysis and Matplotlib and Dash for data visualization, may be a good starting point for this career path. The course may be useful for those who want to develop practical Python skills for business intelligence applications.

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Research Scientist

A research scientist designs and conducts experiments, analyzes data, and publishes findings in academic journals or industry reports. This role may require a strong background in a specific scientific field, along with excellent analytical and problem-solving skills and often requires a PhD. A course in Mega Python, with its coverage of NumPy for numerical processing, Pandas for data analysis, and Statsmodels, may be a useful tool. The course’s emphasis on Python fundamentals also provides a solid foundation for scientific computing and data analysis.

See salaries and explore the career path for Research Scientist

Market Research Analyst

A market research analyst studies market conditions to examine potential sales of a product or service. This role focuses on analyzing consumer behavior and preferences. A course in Mega Python may be relevant to this career. The course’s coverage of Pandas for data analysis and Matplotlib for data visualization may be useful for analyzing market data and presenting findings effectively. The course also covers Python fundamentals, which may be helpful for building custom tools and scripts for market analysis.

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Data Engineer

A data engineer designs, builds, and maintains data pipelines and infrastructure. This role involves working with large datasets, ensuring data quality, and making data accessible to data scientists and analysts. A course in Mega Python, which covers Python fundamentals, NumPy, and Pandas, may be helpful for learning the basic tools and techniques used in data engineering. The course’s emphasis on practical applications and real-world projects may be helpful.

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Statistician

A statistician collects, analyzes, and interprets numerical data to identify significant trends and relationships, often requiring a master's degree or PhD. This role involves applying statistical methods to solve problems in various fields, such as healthcare, finance, and marketing. A course in Mega Python, with its coverage of NumPy, Pandas, and Statsmodels, may be an asset for a statistician. The course’s coverage of Python fundamentals builds a foundation for statistical computing and data analysis. It may be useful for those who want to use Python for their statistical work.

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Database Administrator

A database administrator manages and maintains databases, ensuring data security, integrity, and availability. This role involves designing database systems, monitoring performance, and troubleshooting issues. A course in Mega Python, may be helpful for automating tasks and integrating with various database systems. The course’s emphasis on Python fundamentals may provide an understanding of how to interact with databases programmatically. It may be a good starting point for those looking to combine Python skills with database administration.

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Economist

An economist studies the production, distribution, and consumption of goods and services. This role involves analyzing economic data, developing models, and forecasting trends. A course in Mega Python may be beneficial for an economist. The course’s coverage of Pandas for data analysis, NumPy for numerical processing, and Statsmodels, may be useful for economic modeling and forecasting. The course may be a way to apply Python skills to economic analysis.

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Risk Manager

A risk manager identifies, assesses, and mitigates risks that could impact an organization. This role involves analyzing financial data, developing risk models, and implementing risk management strategies. A course in Mega Python, with its coverage of NumPy for numerical processing and Pandas for data analysis, may be helpful for building risk models and analyzing financial data. The course also covers financial data, which may be relevant to risk management. This complements knowledge in risk management.

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Actuary

An actuary analyzes statistical data to estimate the probability of risk and calculate insurance rates and premiums. This role requires a strong background in mathematics and statistics, as well as knowledge of insurance and finance. A course in Mega Python may be useful for automating tasks and building predictive models. The course’s coverage of NumPy for numerical processing and Pandas for data analysis skills may be useful for actuarial modeling. It would complement expertise in actuarial science.

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Systems Analyst

A systems analyst studies an organization's existing computer systems and procedures and designs solutions to improve efficiency and productivity. This role involves analyzing business requirements, designing system specifications, and coordinating with programmers to implement new systems. A course in Mega Python may be helpful for a systems analyst seeking to modernize legacy systems or integrate new technologies. The course’s coverage of Python fundamentals provides a basis for understanding how to automate tasks and interact with different systems programmatically.

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