Prompt Engineering for Data Analysis

Prompt Engineering for Data Analysis

Data Analysis & Data Visualisation with ChatGPT, Python, Pandas & Prompt Engineering. Formulate effective prompts that guide ChatGPT to generate the desired code

You will learn coding through prompt engineering, eliminating the need to write a single line of code. This approach is tailored to make coding accessible and enjoyable, even for absolute beginners.

For those already experienced in data analysis using Python, this course offers a game-changing opportunity to dramatically enhance your coding speed and efficiency. By leveraging GPT's capabilities, you'll learn how to use prompt engineering techniques to guide ChatGPT in generating accurate, high-quality code tailored to your specific requirements. This transformative skillset will enable you to focus on solving complex data analysis problems while ChatGPT takes care of writing the code, ultimately saving you time and effort.

Throughout the course, we'll dive deep into the world of prompt engineering, exploring how to:

•    Formulate effective prompts that guide ChatGPT to generate the desired code
•    Refine and optimize your prompts for better results and increased accuracy
•    Integrate ChatGPT-generated code seamlessly into your Python and Pandas workflows
•    Troubleshoot and iterate on ChatGPT-generated code to ensure it meets your requirements
•    Leverage ChatGPT's potential to automate repetitive tasks, freeing up your time for more critical analysis

What will this course Cover: You'll embark on a captivating journey that begins with an introduction to ChatGPT and the art of prompt engineering. As you delve deeper, you'll discover the ease of installing Anaconda and working with both Jupyter Notebook and Google Colab, two powerful tools that will become your trusted allies throughout the learning process.

Continuing on this exciting path, we'll provide you with a crash course in Python basics, ensuring a solid foundation to build upon as you progress. Next, you'll explore the essentials of Pandas, mastering the art of working with series, data frames, and multiple data frames to easily manipulate and analyze data with ease.

The course then takes you on a fascinating exploration of data visualization using the versatile Matplotlib library, empowering you to create stunning and informative visualizations to support your data analysis. Finally, you'll learn the ins and outs of importing and exporting various types of data files in Python, rounding out your skillset and making you a formidable data analyst.

Throughout this immersive experience, we'll weave the knowledge and skills together in a seamless narrative, ensuring you develop a deep understanding of the concepts and their practical applications. Enroll now and transform your data analysis journey into an engaging and rewarding adventure!

What you'll learn

•    Confidently approach data analysis tasks with Python and Pandas, even without prior coding experience.
•    Leverage the power of ChatGPT and prompt engineering techniques to efficiently generate accurate, high-quality code for data analysis and visualisation.
•    Seamlessly integrate ChatGPT-generated code into their Python and Pandas workflows, saving time and effort on manual coding.
•    Effectively communicate with ChatGPT by crafting optimised prompts that guide the AI to produce the desired results.
•    Master the use of Jupyter Notebook and Google Colab, enabling a smooth and productive learning experience.
•    Gain proficiency in importing and exporting various types of data files in Python, enhancing their versatility as data analysts.
•    Create visually appealing and informative data visualisations using the Matplotlib library to support their data-driven decision-making processes.
•    Develop a strong foundation in Python, Pandas, and data analysis, paving the way for future learning and professional growth in the field.

#python #chatgpt #dataanalysis #datavisualisation

Scikit-learn: Machine Learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

It is currently maintained by a team of volunteers.

Installation

Dependencies

scikit-learn requires:

• Python (>= 3.8)
• NumPy (>= 1.17.3)
• SciPy (>= 1.5.0)
• joblib (>= 1.1.1)
• threadpoolctl (>= 2.0.0)

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with "Display") require Matplotlib (>= 3.1.3). For running the examples Matplotlib >= 3.1.3 is required. A few examples require scikit-image >= 0.16.2, a few examples require pandas >= 1.0.5, some examples require seaborn >= 0.9.0 and plotly >= 5.14.0.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip:

pip install -U scikit-learn

or conda:

conda install -c conda-forge scikit-learn

The documentation includes more detailed installation instructions.

Changelog

See the changelog for a history of notable changes to scikit-learn.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.

Important links

• Official source code repo: https://github.com/scikit-learn/scikit-learn
• Download releases: https://pypi.org/project/scikit-learn/
• Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Contributing

To learn more about making a contribution to scikit-learn, please see our Contributing guide.

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 7.1.2 installed):

pytest sklearn

See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: https://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

• HTML documentation (stable release): https://scikit-learn.org
• HTML documentation (development version): https://scikit-learn.org/dev/
• FAQ: https://scikit-learn.org/stable/faq.html

Communication

• Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
• Gitter: https://gitter.im/scikit-learn/scikit-learn
• Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
• Blog: https://blog.scikit-learn.org
• Calendar: https://blog.scikit-learn.org/calendar/
• Twitter: https://twitter.com/scikit_learn
• Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
• Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
• Website: https://scikit-learn.org
• LinkedIn: https://www.linkedin.com/company/scikit-learn
• YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
• Facebook: https://www.facebook.com/scikitlearnofficial/
• Instagram: https://www.instagram.com/scikitlearnofficial/
• TikTok: https://www.tiktok.com/@scikit.learn

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn

Website: https://scikit-learn.org

Download Details:

Author: Scikit-learn
Source Code: https://github.com/scikit-learn/scikit-learn 
License: BSD-3-Clause license

#machinelearning #python #datascience #dataanalysis 

Olympics Data Analysis Using Python 2021

The Olympics Data Analysis Using Python 2021 video covers a brief history of the Olympic games and will make you understand how to analyze and visualize past Olympics data for drawing crucial insights. This Olympics Exploratory Data Analysis Tutorial video will help you learn how to use Python libraries and their associated functions for creating interesting visualizations and analyzing vast data. The datasets used in this video can be download from Kaggle: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results  

What is Data Analysis?
Data Analysis is the process of exploring and analyzing large datasets to find hidden patterns, unseen trends, discover correlations and valuable insights. Data Analytics and visualization can be used for Improved Decision Making, Better Customer Service, Efficient Operations as well as Effective Marketing. There are various steps involved in the data analytics process. Below are steps:
1. Understand the problem
2. Data Collection
3. Data Cleaning
4. Data Exploration and Analysis
5. Interpret the results

A Brief About the Olympic Games:
Olympics is one of the biggest sporting events featuring summer and winter sports competitions where thousands of athletes from around the world participate in a variety of competitions. More than 200 nations participate.The Olympic Games are usually held every four years, alternating between the Summer and Winter Olympics every two years in the four-year period. The International Olympic Committee (IOC) as formed in 1894 and the first modern Olympics was held in 1896.

#python #dataanalysis

Guide To Mito - A Low Code Tool for Exploratory Data Analysis(EDA)

In data science, exploratory data analysis(EDA) is one of the most important processes that need to be done before modeling or any other processes regarding data usage. There are various fields instead of data science like business analytics and graph representations where the EDA is required. This enables us to find insights and patterns present inside the data which we can’t understand by seeing the data in raw formats like CSV, excel and pandas data frames. 

Read more: https://analyticsindiamag.com/guide-to-mito-a-low-code-tool-for-exploratory-data-analysiseda/

#dataanalysis 

Data Analyst Full Course 2021 | Data Analyst Skills Required | Data Analytics Lecture

In this Data Analyst Full Course video, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. You will then understand a case study and perform analysis of data using Python and R. In addition to that; we will see the top 10 data analysis tools and understand the difference between a data scientist and a data analyst. Finally, we’ll see the top interview questions that will help you crack a data analyst interview.

This Data Analyst Master’s Program in collaboration with IBM will make you an expert in data analytics. In this Data Analytics course, you'll learn analytics tools and techniques, how to work with SQL databases, the languages of R and Python, how to create data visualizations, and how to apply statistics and predictive analytics in a business environment.

Why become Data Analyst?
By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in today’s business environment. In fact, 2.5 quintillion bytes of data are created each day. With an ever-increasing skill gap in data analytics, the value of data analysts is continuing to grow, creating a new job and career advancement opportunities.

Who should take up this course?
Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analyst Master’s Program, including:
1. IT professionals
2. Banking and finance professionals
3. Marketing managers
4. Sales professionals
5. Supply chain network managers
6. Beginners in the data analytics domain
7. Students in UG/ PG programs

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#dataanalysis #dataanalyst #developer 

Streamlit vs PlotlyDash: Comparison With Python Examples

Web development is the term that stands for creating, deploying and operating web applications and conceptualizing application programming interfaces for the World Wide Web. The Web has grown tremendously during the past decade, raising the count in the number of sites, users, interface and implementation capabilities since the first website went live in 1989. Web development is the concept that comprises all the activities involved around websites and web applications.
Read more: https://analyticsindiamag.com/streamlit-vs-plotlydash-comparison-with-python-examples/

#dataanalysis #python #data-science

Making Your Data Analysis Compelling

Imagine you have spent hours on data analysis at your boss’s behest. You put in a lot of effort to make your final product accurate, insightful and well packaged. But in the end, your boss decides to not use your presentation, and all your efforts go down the drain. There is nothing more frustrating than this for a data scientist, but apparently it happens far too often in the analytics industry.

Read more: https://analyticsindiamag.com/making-your-data-analysis-compelling/

#dataanalysis #data-science #analytics #visualizations

A Quantile based historical Stock Analysis

How data analytics can prove to be extremely powerful in making historical stock price analysis.

Read more: https://analyticsindiamag.com/a-quantile-based-historical-stock-analysis/

#analytics #data-science #investment #stockanalysis #dataanalysis #fintech

Top 8 Data Transformation Methods

Data transformation is a technique of conversion as well as mapping of data from one format to another. Here are the best eight methods for data transformation one must know.
Read more: https://analyticsindiamag.com/top-8-data-transformation-methods/

#data #analytics #dataanalysis #big-data

New Development On AI Predicting The Future

Researchers from Columbia University have developed an AI framework with the capability to tell what is predictable in the future. Interestingly, the AI model is built totally using unlabelled video data.
Read more: https://analyticsindiamag.com/predictability-as-a-hierarchy-new-development-on-ai-predicting-the-future/

#ai #data #prediction #future #research #dataanalysis

Top XGBoost Interview Questions For Data Scientists

Introduced a few years ago by Tianqi Chen and his team of researchers at the University of Washington, eXtreme Gradient Boosting or XGBoost is a popular and efficient gradient boosting method.
Read more: https://analyticsindiamag.com/top-xgboost-interview-questions-for-data-scientists/

#data-science #datascientist #xgboost #interviewquestion #dataanalysis

Cognizant Acquires Analytics Company Servian To Boost Digital Capabilities

Cognizant has entered into an agreement to acquire Sydney-based analytics consulting firm @Servian, to enhance its digital portfolio.

Read more: https://analyticsindiamag.com/cognizant-acquires-sydney-based-analytics-company-servian-to-boost-digital-capabilities/?fbclid=IwAR3B5HJOARSEqB3bMH5DkSecq6N59nvNuqM_FtAgSU1QOCiw_yyIcKLYwU0

#analytics #startup #data-science #big-data #dataanalysis

Top 10 Visual Analytics Provider For 2021

Visual analytics software can help provide real-time, accurate, and actionable insights using the data generated by organisations.
Analytics India Magazine enlists some of the top Visual Analytics providers whose products and applications have relevance in 2021.
Read more: https://analyticsindiamag.com/top-10-visual-analytics-provider-for-2021/

#bigdata #analytics #data-science #data #datavisualization #dataanalysis

Which Countries Pay The Most To Data Scientists

We have included 50 major countries to compare the data scientists salaries today.
Denmark, Switzerland, & Australia are the highest paying countries for datascientists.
Read more: https://analyticsindiamag.com/which-countries-pay-the-most-to-data-scientists/

#data-science #datascientists #coding #big-data #dataanalysis

Will SAS Continue To Hold Ground In Data Science?

Experts believe, to retain the ‘market leader’ tag and to maintain its hegemony, SAS would need to revamp internally.
Read more: https://analyticsindiamag.com/will-sas-continue-to-hold-ground-in-data-science/?fbclid=IwAR1MuT0cxJtVS0fvhlH2RhaozEwIJn9ShX5wLgUYuKfbo7S_IqeZCksXRj4

#sas #data-science #datascientist #analytics #big-data #dataanalysis