Ali Haider Shalwani

In March 2023, we had the pleasure of hosting the first edition of the Future of Data and AI conference – an incredible tech extravaganza that drew over 10,000 attendees, featured 30+ industry experts as speakers, and offered 20 engaging panels and tutorials led by the talented team at Data Science Dojo. 

Our virtual conference spanned two days and provided an extensive range of high-level learning and training opportunities. Attendees had access to a diverse selection of activities such as panel discussions, AMA (Ask Me Anything) sessions, workshops, and tutorials. 

Future of Data and AI conference featured several of the most current and pertinent topics within the realm of AI & data science, such as generative AI, vector similarity, and semantic search, federated machine learning, storytelling with data, reproducible data science workflows, natural language processing, machine learning ops, as well as tutorials on Python, SQL, and Docker.

In case you were unable to attend the Future of Data and AI conference, we’ve compiled a list of all the tutorials and panel discussions for you to peruse and discover the innovative advancements presented at the Future of Data & AI conference. 

Panel Discussions

On Day 1 of the Future of Data and AI conference, the agenda centered around engaging in panel discussions. Experts from the field gathered to discuss and deliberate on various topics related to data and AI, sharing their insights with the attendees.

1. Data Storytelling in Action:

This panel will discuss the importance of data visualization in storytelling in different industries, different visualization tools, tips on improving one’s visualization skills, personal experiences, breakthroughs, pressures, and frustrations as well as successes and failures.

Explore, analyze, and visualize data with our Introduction to Power BI training & make data-driven decisions.  

2. Pediatric Moonshot:

This panel discussion will give an overview of the BevelCloud’s decentralized, in-the-building, edge cloud service, and its application to pediatric medicine.

3. Navigating the MLOps Landscape:

This panel is a must-watch for anyone looking to advance their understanding of MLOps and gain practical ideas for their projects. In this panel, we will discuss how MLOps can help overcome challenges in operationalizing machine learning models, such as version control, deployment, and monitoring. Additionally, how ML Ops is particularly helpful for large-scale systems like ad auctions, where high data volume and velocity can pose unique challenges.

4. AMA – Begin a Career in Data Science:

In this AMA session, we will cover the essentials of starting a career in data science. We will discuss the key skills, resources, and strategies needed to break into data science and give advice on how to stand out from the competition. We will also cover the most common mistakes made when starting out in data science and how to avoid them. Finally, we will discuss potential job opportunities, the best ways to apply for them, and what to expect during the interview process.

 Want to get started with your career in data science? Check out our award-winning Data Science Bootcamp that can navigate your way.

5. Vector Similarity Search:

With this panel discussion learn how you can incorporate vector search into your own applications to harness deep learning insights at scale. 

 6. Generative AI:

This discussion is an in-depth exploration of the topic of Generative AI, delving into the latest advancements and trends in the industry. The panelists explore the ways in which generative AI is being used to drive innovation and efficiency in these areas and discuss the potential implications of these technologies on the workforce and the economy.

Tutorials 

Day 2 of the Future of Data and AI conference focused on providing tutorials on several trending technology topics, along with our distinguished speakers sharing their valuable insights.

1. Building Enterprise-Grade Q&A Chatbots with Azure OpenAI:

In this tutorial, we explore the features of Azure OpenAI and demonstrate how to further improve the platform by fine-tuning some of its models. Take advantage of this opportunity to learn how to harness the power of deep learning for improved customer support at scale.

2. Introduction to Python for Data Science:

This lecture introduces the tools and libraries used in Python for data science and engineering. It covers basic concepts such as data processing, feature engineering, data visualization, modeling, and model evaluation. With this lecture, participants will better understand end-to-end data science and engineering with a real-world case study.

Want to dive deep into Python? Check out our Introduction to Python for Data Science training – a perfect way to get started.  

3. Reproducible Data Science Workflows Using Docker:

Watch this session to learn how Docker can help you achieve that and more! Learn the basics of Docker, including creating and running containers, working with images, automating image building using Dockerfile, and managing containers on your local machine and in production.

4. Distributed System Design for Data Engineering:

This talk will provide an overview of distributed system design principles and their applications in data engineering. We will discuss the challenges and considerations that come with building and maintaining large-scale data systems and how to overcome these challenges by using distributed system design.

5. Delighting South Asian Fashion Customers:

In this talk, our presenter will discuss how his company is utilizing AI to enhance the fashion consumer experience for millions of users and businesses. He will demonstrate how LAAM is using AI to improve product understanding and tagging for the catalog, creating personalized feeds, optimizing search results, utilizing generative AI to develop new designs, and predicting production and inventory needs.

6. Unlock the Power of Embeddings with Vector Search:

This talk will include a high-level overview of embeddings and discuss best practices around embedding generation and usage, build two systems; semantic text search and reverse image search, and see how we can put our application into production using Milvus – the world’s most popular open-source vector database.

7. Deep Learning with KNIME:

This tutorial will provide theoretical and practical introductions to three deep learning topics using the KNIME Analytics Platform’s Keras Integration; first, how to configure and train an LSTM network for language generation; we’ll have some fun with this and generate fresh rap songs! Second, how to use GANs to generate artificial images, and third, how to use Neural Styling to upgrade your headshot or profile picture!

8. Large Language Models for Real-world Applications:

This talk provides a gentle and highly visual overview of some of the main intuitions and real-world applications of large language models. It assumes no prior knowledge of language processing and aims to bring viewers up to date with the fundamental intuitions and applications of large language models.  

9. Building a Semantic Search Engine on Hugging Face:

Perfect for data scientists, engineers, and developers, this tutorial will cover natural language processing techniques and how to implement a search algorithm that understands user intent. 

10. Getting Started with SQL Programming:

Are you starting your journey in data science? Then you’re probably already familiar with SQL, Python, and R for data analysis and machine learning. However, in real-world data science jobs, data is typically stored in a database and accessed through either a business intelligence tool or SQL. If you’re new to SQL, this beginner-friendly tutorial is for you! 

In retrospect

As we wrap up our coverage of the Future of Data and AI conference, we’re delighted to share the resounding praise it has received. Esteemed speakers and attendees alike have expressed their enthusiasm for the valuable insights and remarkable networking opportunities provided by the conference.

Stay tuned for updates and announcements about the Future of Data and AI Conference!

We would also love to hear your thoughts and ideas for the next edition. Please don’t hesitate to leave your suggestions in the comments section below. 

This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio.  

What is GitHub?

GitHub is a powerful platform for data scientists, data analysts, data engineers, Python and R developers, and more. It is an excellent resource for beginners who are just starting with data science, analytics, and engineering. There are thousands of open-source repositories available on GitHub that provide code examples, datasets, and tutorials to help you get started with your projects.   

This blog lists some useful GitHub repositories that will not only help you learn new concepts but also save you time by providing pre-built code and tools that you can customize to fit your needs. 

Want to get started with data science? Do check out our Data Science Bootcamp as it can navigate your way!  

Best GitHub repositories to stay ahead of the tech Curve

With GitHub, you can easily collaborate with others, share your code, and build a portfolio of projects that showcase your skills.  

1. Scikit-learn: A Python library for machine learning built on top of NumPy, SciPy, and matplotlib. It provides a range of algorithms for classification, regression, clustering, and more.  

Link to the repository: https://github.com/scikit-learn/scikit-learn  

2. TensorFlow: An open-source machine learning library developed by Google Brain Team. TensorFlow is used for numerical computation using data flow graphs.  

Link to the repository: https://github.com/tensorflow/tensorflow  

3. Keras: A deep learning library for Python that provides a user-friendly interface for building neural networks. It can run on top of TensorFlow, Theano, or CNTK.  

Link to the repository: https://github.com/keras-team/keras  

4. Pandas: A Python library for data manipulation and analysis. It provides a range of data structures for efficient data handling and analysis.  

Link to the repository: https://github.com/pandas-dev/pandas  

Add value to your skillset with our instructor-led live Python for Data Science training.  

5. PyTorch: An open-source machine learning library developed by Facebook’s AI research group. PyTorch provides tensor computation and deep neural networks on a GPU.  

Link to the repository: https://github.com/pytorch/pytorch  

6. Apache Spark: An open-source distributed computing system used for big data processing. It can be used with a range of programming languages such as Python, R, and Java.  

Link to the repository: https://github.com/apache/spark  

7. FastAPI: A modern web framework for building APIs with Python. It is designed for high performance, asynchronous programming, and easy integration with other libraries.  

Link to the repository: https://github.com/tiangolo/fastapi  

8. Dask: A flexible parallel computing library for analytic computing in Python. It provides dynamic task scheduling and efficient memory management.  

Link to the repository: https://github.com/dask/dask  

9. Matplotlib: A Python plotting library that provides a range of 2D plotting features. It can be used for creating interactive visualizations, animations, and more.  

Link to the repository: https://github.com/matplotlib/matplotlib 

Looking to begin exploring, analyzing, and visualizing data with Power BI Desktop? Our Introduction to Power BI training course is designed to assist you in getting started! 

10. Seaborn: A Python data visualization library based on matplotlib. It provides a range of statistical graphics and visualization tools.  

Link to the repository: https://github.com/mwaskom/seaborn 

11. NumPy: A Python library for numerical computing that provides a range of array and matrix operations. It is used extensively in scientific computing and data analysis.  

Link to the repository: https://github.com/numpy/numpy  

12. Tidyverse: A collection of R packages for data manipulation, visualization, and analysis. It includes popular packages such as ggplot2, dplyr, and tidyr.  

Link to the repository: https://github.com/tidyverse/tidyverse  

In a nutshell

In conclusion, GitHub is a valuable resource for developers, data scientists, and engineers who are looking to stay ahead of the technology curve. With the vast number of repositories available, it can be overwhelming to find the ones that are most useful and relevant to your interests. The repositories we have highlighted in this blog cover a range of topics, from machine learning and deep learning to data visualization and programming languages. By exploring these repositories, you can gain new skills, learn best practices, and stay up-to-date with the latest developments in the field.

Do you happen to have any others in mind? Please feel free to share them in the comments section below!  

Data Science newsletter by Data Science Dojo is your one-stop source for all the latest news, updates, and resources in the world of data science. In this newsletter, we bring you the latest trends and insights from the industry, along with informative blogs, engaging infographics, and upcoming events to keep you informed and up-to-date.   

 Whether you’re a seasoned data scientist or just starting out, our newsletter provides you with all the tools and resources you need to stay ahead of the curve.

From crash courses to expert blogs, our content is designed to help you learn, grow, and succeed in your data science journey. So, sit back, relax, and let our newsletter be your go-to guide for all things data science!  Here’s everything that’s included in our weekly and monthly newsletters:   

Featured tutorials  

In this section of our newsletter, we feature some of the most informative and useful blogs and tutorials on data science. Our carefully curated selection of content is designed to help our readers learn and understand various concepts and techniques in this exciting field.   

Featured courses

This section of our newsletter offers a free course on one of the data science-related topics, allowing you to expand your existing knowledge in the field. Our carefully curated selection of courses is designed to help you develop new skills and gain valuable insights into the latest trends and techniques.     

Data science visuals 

Here we include some trending data science infographics from social. These informative graphics are designed to help you quickly revise and recall important concepts and techniques in a visually engaging and easy-to-understand format. Whether you’re short on time or looking for a quick refresher, our infographics offer a convenient way to keep your knowledge up-to-date.   

Editor’s pick  

Upcoming webinars  

Whether you’re looking to learn about new techniques and tools, or simply seeking to expand your professional network, our weekly webinars offer an excellent opportunity to connect with other data science professionals and learn from the best in the business.   

Data science certifications  

If you are planning to take your data science or analytics a step ahead, then make sure to check out our paid programs. In our newsletter section, we list our upcoming paid programs revolving around data science and cutting-edge technologies that might be of interest to you.  Our carefully curated selection of blogs, infographics, upcoming events, and crash courses offers a comprehensive overview of the most relevant and useful resources in the world of data science.

Subscribe to our data science newsletter

Python has become a popular programming language in the data science community due to its simplicity, flexibility, and wide range of libraries and tools. With its powerful data manipulation and analysis capabilities, Python has emerged as the language of choice for data scientists, machine learning engineers, and analysts.     

By learning Python, you can effectively clean and manipulate data, create visualizations, and build machine-learning models. It also has a strong community with a wealth of online resources and support, making it easier for beginners to learn and get started.   

This blog will navigate your path via a detailed roadmap along with a few useful resources that can help you get started with it.   

Step 1. Learn the basics of Python programming  

Before you start with data science, it’s essential to have a solid understanding of its programming concepts. Learn about basic syntax, data types, control structures, functions, and modules.  

Step 2. Familiarize yourself with essential data science libraries    

Once you have a good grasp of Python programming, start with essential data science libraries like NumPy, Pandas, and Matplotlib. These libraries will help you with data manipulation, data analysis, and visualization.   

This blog lists some of the top Python libraries for data science that can help you get started.  

Step 3. Learn statistics and mathematics  

To analyze and interpret data correctly, it’s crucial to have a fundamental understanding of statistics and mathematics.   This short video tutorial can help you to get started with probability.   

Additionally, we have listed some useful statistics and mathematics books that can guide your way, do check them out!  

Step 4. Dive into machine learning  

Start with the basics of machine learning and work your way up to advanced topics. Learn about supervised and unsupervised learning, classification, regression, clustering, and more.   

This detailed machine-learning roadmap can get you started with this step.   

Step 5. Work on projects  

Apply your knowledge by working on real-world data science projects. This will help you gain practical experience and also build your portfolio. Here are some Python project ideas you must try out!  

Step 6. Keep up with the latest trends and developments 

Data science is a rapidly evolving field, and it’s essential to stay up to date with the latest developments. Join data science communities, read blogs, attend conferences and workshops, and continue learning.  

Our weekly and monthly data science newsletters can help you stay updated with the top trends in the industry and useful data science & AI resources, you can subscribe here.   

Additional resources    

1. Learn how to read and index time series data using Pandas package and how to build, predict or forecast an ARIMA time series model using Python’s statsmodels package with this free course. 
2. Explore this list of top packages and learn how to use them with this short blog. 
3. Check out our YouTube channel for Python & data science tutorials and crash courses, it can surely navigate your way.
   

By following these steps, you’ll have a solid foundation in Python programming and data science concepts, making it easier for you to pursue a career in data science or related fields.   

For an in-depth introduction do check out our Python for Data Science training, it can help you learn the programming language for data analysis, analytics, machine learning, and data engineering. 

Wrapping up

In conclusion, Python has become the go-to programming language in the data science community due to its simplicity, flexibility, and extensive range of libraries and tools.

This blog outlines a collection of 12 must-have AI tools that can assist with day-to-day activities and make tasks more efficient and streamlined.  

The development of Artificial Intelligence has gone through several phases over the years. It all started in the 1950s and 1960s with rule-based systems and symbolic reasoning.

In the 1970s and 1980s, AI research shifted to knowledge-based systems and expert systems. In the 1990s, machine learning and neural networks emerged as popular techniques, leading to breakthroughs in areas such as speech recognition, natural language processing, and image recognition. 

In the 2000s, the focus on Artificial Intelligence shifted to data-driven AI and big data analytics.   Today, in 2023, AI is transforming industries such as healthcare, finance, transportation, and entertainment, and its impact is only expected to grow in the future.  

Adapting to Artificial Intelligence is becoming increasingly important for companies and individuals due to its numerous benefits. It can help automate mundane and repetitive tasks, freeing up time for more complex and creative work. It can also enable businesses to make more accurate and informed decisions by quickly analyzing large amounts of data.

In today’s fast-paced and competitive environment, companies and individuals who fail to adapt to Artificial Intelligence may find themselves falling behind in terms of efficiency and innovation. Therefore, it is essential for companies and individuals to embrace AI and use it to their advantage.  

Here’s a list of top 12 AI tools that can be useful for different individual and business work:  

1. ChatGPT is a chatbot created by OpenAI that uses natural language processing to generate human-like conversations.  
2. Ximilar is an image recognition and analysis tool that uses machine learning to identify objects and scenes in images and videos.  
3. Moodbit is an emotional intelligence tool that uses natural language processing to analyze and measure emotional language in text, helping businesses improve communication and employee well-being.  
4. Knoyd is a predictive analytics platform that uses machine learning to provide data-driven insights and predictions to businesses.  
5. Chorus.AI is a conversation analysis tool that uses natural language processing to analyze sales calls and provide insights on customer sentiment, product feedback, and sales performance.  
6. Receptivity is a personality analysis tool that uses natural language processing to analyze language patterns and provide insights into personality traits and emotional states.  
7. Paragone is a text analysis tool that uses natural language processing to extract insights and trends from large volumes of unstructured text data.  
8. Ayasdi is a data analysis and visualization tool that uses machine learning to uncover hidden patterns and insights in complex data sets.  
9. Arria NLG is a natural language generation tool that uses machine learning to generate human-like language from data, enabling businesses to automate report writing and other written communication.  
10. Cognitivescale is a cognitive automation platform that uses machine learning to automate complex business processes, such as customer service and supply chain management.  
11. Grammarly is a writing assistant that uses AI to detect grammar, spelling, and punctuation errors in your writing, as well as suggest a more effective vocabulary and writing style.
12. Hootsuite Insights is a social media monitoring tool that helps businesses monitor social media conversations and track brand reputation, customer sentiment, and industry trends. 

Read more about –> ChatGPT Enterprise: All you need to know about OpenAI’s enterprise-grade version of ChatGPT

Unravel the modern business challenges with must-have AI tools

The development of Artificial Intelligence has rapidly advanced over the years, leading to the creation of a wide range of powerful tools that can be used by individuals and businesses alike. These tools have proven to be incredibly useful in a variety of tasks, from data analysis to streamlining processes and boosting productivity. As we look toward the future, it is clear that the role of AI will continue to expand, leading to new and exciting opportunities for businesses of all kinds.  

If you are interested in learning more about the latest advancements in Artificial Intelligence and data, be sure to check out the upcoming future of AI and Data conference on March 1st and 2nd. With over 20 industry experts, this conference is a must-attend event for anyone looking to stay at the forefront of this rapidly evolving field. Register today and start exploring the limitless possibilities of Artificial Intelligence and data!