Generative AI LabExploring generative AI and large language models (LLMs)

Author(s): Juan Hernanz

The Strava race analysis tool helps athletes track their performance and compare it to others. It offers visualizations of data like speed, distance, and elevation. Users can see where they ranked and identify areas for improvement. It’s a useful tool for training and motivation.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

1. Introduction to Strava Race Analysis

Strava is a popular fitness tracking app used by millions of athletes worldwide. One of its most useful features is the ability to analyze race data, providing valuable insights into an athlete’s performance. This feature is especially beneficial for runners and cyclists, allowing them to visualize their race data and identify areas for improvement. In this blog post, we will explore the benefits of visualizing Strava race analysis and how it can help athletes reach their fitness goals.

2. Understanding Your Performance

Strava race analysis provides a detailed breakdown of an athlete’s performance, including pace, heart rate, and elevation. By visualizing this data, athletes can gain a better understanding of their overall performance and identify patterns or trends. For example, an athlete may notice that their pace significantly drops towards the end of a race, indicating that they need to work on their endurance. This information can be used to adjust training plans and improve performance in future races.

3. Identifying Weaknesses

Visualizing Strava race analysis an also help athletes identify weaknesses in their performance. The data may reveal that an athlete struggles with hills or maintaining a consistent pace. By recognizing these weaknesses, athletes can focus on specific areas during training to improve their overall performance. With the help of Strava’s visualizations, athletes can track their progress over time and see how their weaknesses improve with targeted training.

4. Comparing Performance

Another useful feature of Strava race analysis is the ability to compare performance with previous races or with other athletes. This can be a great motivator for athletes looking to improve their performance and compete with others. By visualizing their data alongside others, athletes can see where they rank and set goals to beat their personal best or outperform their competitors. This feature can also be helpful for coaches or training partners to analyze and compare performance data.

5. Tracking Progress

Strava race analysis also allows athletes to track their progress over time. By visualizing their data from previous races, athletes can see how they have improved in terms of pace, heart rate, and other metrics. This can be a great source of motivation and encouragement, especially for long-term training goals. Athletes can also use this feature to set realistic goals and track their progress towards achieving them.

6. Conclusion

In conclusion, using Strava Race Analysis allows athletes to gain valuable insight into their race performance in a user-friendly and visually appealing manner. This tool provides a comprehensive overview of key metrics, enabling athletes to identify areas for improvement and track their progress over time. With its simple and intuitive design, Strava Race Analysis is a valuable resource for any athlete looking to enhance their training and achieve their goals.

Crafted using generative AI from insights found on Towards Data Science.

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Author(s): Yury Kalbaska

TL;DR: Learn how to use the Elastic (ELK) Stack to log Databricks workflows in just a few simple steps. This powerful combination makes it easy to monitor and troubleshoot your workflows, saving you time and effort. Start tracking your Databricks data today with ELK.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

Logging is an essential aspect of any data-driven workflow. It allows us to track and monitor the execution of our workflows, identify errors, and troubleshoot issues. Databricks, a popular data analytics platform, offers a robust logging feature that allows users to track their workflows’ execution. However, to make the most out of Databricks’ logging capabilities, it is essential to integrate it with the Elastic (ELK) Stack. In this blog post, we will discuss how to log Databricks workflows with the ELK Stack and why it is beneficial.

What is the Elastic (ELK) Stack?

The ELK Stack is a popular open-source platform used for log management and analytics. It consists of three main components: Elasticsearch, Logstash, and Kibana. Elasticsearch is a distributed search engine that stores and indexes data, Logstash is a data processing pipeline that collects and processes data, and Kibana is a visualization tool that allows users to analyze and visualize data stored in Elasticsearch. Together, these components form a powerful platform for managing and analyzing logs.

Why Log Databricks Workflows with the ELK Stack?

Integrating Databricks with the ELK Stack offers several benefits. Firstly, it allows users to centralize all their logs in one place, making it easier to search, analyze, and monitor them. Secondly, the ELK Stack offers advanced querying and filtering capabilities, allowing users to quickly identify and troubleshoot issues in their workflows. Additionally, the ELK Stack provides real-time monitoring, alerting, and visualization features, enabling users to track their workflows’ performance and identify any bottlenecks. Finally, by integrating Databricks with the ELK Stack, users can leverage the power of Elasticsearch’s distributed architecture, making it easier to handle large volumes of log data.

How to Log Databricks Workflows with the ELK Stack?

The process of logging Databricks workflows with the ELK Stack involves three main steps: setting up Elasticsearch, configuring Logstash, and creating visualizations in Kibana. Firstly, users need to set up an Elasticsearch cluster and configure it to receive logs from Databricks. Next, they need to configure Logstash to collect and process the logs from Databricks and send them to Elasticsearch. Finally, users can create visualizations in Kibana to monitor and analyze their Databricks logs. Databricks provides detailed documentation and tutorials on how to set up and configure the ELK Stack with their platform, making it easy for users to

In conclusion, logging Databricks workflows with the Elastic (ELK) Stack is a simple and effective way to monitor and track data processes. By utilizing the ELK Stack, users can easily gather and analyze important data points, allowing for better decision making and troubleshooting. Implementing this method can greatly improve the efficiency and accuracy of data workflows, making it a valuable tool for any organization. With straightforward steps and clear benefits, it is worth considering for any data-driven team.

Crafted using generative AI from insights found on Towards Data Science.

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TL;DR: LLM inference costs have significantly reduced with the introduction of context caching, allowing for 10x cheaper access to reused input tokens. Deepmind and Gemini have made progress in this area, with Deepmind also releasing a new small 2B Gemma model benefiting from model distillation. China-based DeepSeek has announced automatic context caching, reducing API costs by 90%. In parallel, research on inference-time scaling laws suggests that increasing the number of inference steps can significantly improve LLM performance. These advancements are synergistic and could make agentic LLM systems more feasible. The evolution of LLM compression methods, from QuIP to A

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

This week, our eyes were again on the rapid progress in LLM inference, in particular, the possibility of significantly reducing the cost for reused input tokens with context caching. We might labor this point a bit much, but the progress in inference compute prices for LLMs is truly unprecedented. In this blog post, we will discuss the latest developments in LLM inference, including the innovations in context caching and the potential impact it could have on the field of AI.

Deepseek’s 10x Cheaper Reused LLM Input Tokens

At the peak of Moore’s law, the cost per transistor reduced around ~4,000x in the first 14 years up to 1982. But transistors were not getting fundamentally more capable at the same time! At this stage, it is hard to imagine progress at this pace not soon having a global impact. The innovations in context caching this week tie into a great new paper investigating how LLM performance can benefit from repeated inference steps, or “inference-time scaling laws”. Together, we think these provide a very powerful new avenue for unlocking economically useful LLM capabilities.

Deepmind’s Flurry of Activity

Deepmind followed META’s week in the spotlight with a flurry of activity. Gemini released a new Pro 1.5 experimental model, which, for the first time, put Deepmind at the top of the LMSYS arena, suggesting they have finally caught up in the LLM capability race on some measures (but still behind on Livebench and Zeroeval benchmarks). They also announced the Flash model that will reduce 5x in price next week (taking it to half of GPT-4o-mini cost), a move we think is partly a reflection of progress in distillation but also likely competitive pressure from Llama 3.1. They also released an impressive (for its size) new small 2B Gemma model benefiting from model distillation (which we expect to join the LLM builder toolkit post Llama 3.1, as we discussed last week).

DeepSeek’s Context Caching on Disk

Less than 24 after the Gemini Flash price announcement, inference compute pricing was taken a whole level lower with China-based DeepSeek announcing Context Caching on Disk via their API. This automatically reduces the cost of handling reused input tokens by 90%, down to $0.014 / million tokens, making it 10x cheaper than GPT-4o-mini. The caching mechanism works by storing input content it expects to be reused in a

This week, there have been significant advancements in the field of large language models, specifically in the area of inference compute prices. It is now possible to access reused input token inference with Deepseek v2 for 4,300x cheaper than GPT-3 (da-vinci 002) cost just 24 months ago. This is a truly unprecedented progress and is expected to have a global impact. Along with this, new research on inference-time scaling laws suggests that we can significantly improve LLM performance by increasing the number of inference steps. This approach, known as repeated sampling, allows weaker models to outperform stronger ones in certain tasks. These advancements are synergistic and are expected to make some agentic LLM systems far more feasible, both in terms of cost and latency. The reduced cost and latency also open up new avenues for using LLMs in scenarios where repeated querying of the same input tokens is essential, such as multi-step data analysis, codebase questioning, and multi-turn conversations. As these advancements continue, we can expect even more significant cost reductions and increased capabilities for LLMs, which will have a profound impact on various industries and applications.

Crafted using generative AI from insights found on Towards AI.

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Tl;DR: AI is evolving and humans need to define their relationship with it. It was born from years of research, experimentation, and the combination of statistics, math, and computer power. Linear regression was the initial state of AI, using data and computation to understand patterns. Now, AI is more productive than the human brain, but it needs constant input of data to make decisions. ChatGPT was a major breakthrough, democratizing AI.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

Artificial Intelligence (AI) has rapidly evolved over the years, fundamentally transforming industries, lifestyles, and the human experience. Much like biological evolution, AI’s development can be seen as a form of natural selection, where algorithms and models are continually refined and improved to better fit their intended functions. In this blog, we’ll delve into how AI has evolved, the pivotal moments that have shaped its trajectory, and what this means for humanity as we define our relationship with this powerful technology.

The Birth of AI

AI’s journey began decades ago, rooted in the convergence of statistics, mathematics, and computational power. The early days of AI were marked by the use of simple models like linear regression, which utilized data and computation to discern patterns. This was the foundational stage of AI, where machines started to mimic basic human tasks by recognizing and predicting trends from given data sets.

Evolution and Breakthroughs

AI’s evolution can be likened to Darwinian natural selection, where the fittest algorithms survive and adapt. As computational power increased and more sophisticated statistical methods were developed, AI began to exhibit capabilities that surpassed basic pattern recognition. Machine learning models, particularly neural networks, began to demonstrate unprecedented accuracy in tasks like image recognition and language processing.

A significant breakthrough came with the development of deep learning, which allowed AI to process vast amounts of data and uncover complex patterns with minimal human intervention. This leap transformed AI from a tool of prediction to a system capable of autonomous decision-making in certain contexts, such as self-driving cars and real-time language translation.

ChatGPT: A New Era

The launch of ChatGPT marked a significant milestone in AI’s journey. By democratizing AI, ChatGPT made advanced language processing capabilities accessible to the masses, enabling more interactive and intuitive human-computer interactions. This model, and others like it, have underscored AI’s ability to perform tasks that require understanding and generating human language, which was once thought to be a uniquely human capability.

ChatGPT’s success is a testament to AI’s natural selection process, where countless iterations and improvements have led to models that not only meet but often exceed human expectations in specific tasks. However, these advancements come with the caveat of needing vast amounts of data to make informed decisions and predictions.

Defining Our Relationship with AI

As AI continues to evolve, it becomes increasingly crucial for humanity to define its relationship with this technology. The rapid advancements have sparked debates about the role of AI in society, ethical considerations, and the potential for AI to outpace human intelligence. While AI has the power to augment human capabilities and solve complex problems, it also poses challenges that require careful navigation, such as data privacy, bias, and the need for accountability in AI-driven decisions.

The Need for Constant Input

Despite AI’s impressive capabilities, it still requires constant input of data to learn, adapt, and make decisions. This reliance on data is a double-edged sword, providing AI with the ability to continually improve while also necessitating a steady flow of information to maintain its effectiveness. As a result, the relationship between humans and AI is symbiotic; humans supply the data, and AI processes it to yield valuable insights and solutions.

The Future of AI Evolution

Looking ahead, the evolution of AI is poised to continue at an accelerated pace. With ongoing research and development, AI is likely to become even more integrated into everyday life, offering solutions that enhance efficiency and productivity. However, this evolution must be guided by thoughtful consideration of ethical implications and a clear understanding of the societal impacts of AI.

Conclusion

The evolution of AI, much like natural selection, has been marked by incremental advancements and groundbreaking breakthroughs. As we continue to explore the potential of AI, it’s essential to maintain a balance between leveraging its capabilities and addressing the ethical and societal challenges it presents. By defining our relationship with AI thoughtfully and responsibly, we can harness its power to create a future that benefits all of humanity.

Discover the full story originally published on Towards AI.

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TL;DR: The requirements.txt file used for managing Python project dependencies is now obsolete. Poetry, a new tool, simplifies the process by handling dependencies and metadata in a more efficient way. It also supports virtual environments and allows for easy installation and updates of packages.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Requirements.txt Is Obsolete: The Need for a Better Solution

For years, requirements.txt has been the go-to solution for managing Python project dependencies. This simple text file lists all the packages and their versions needed for a project to run. However, as projects become more complex and the Python ecosystem evolves, it has become clear that requirements.txt is no longer the best option for managing dependencies. In this blog post, we will discuss the limitations of requirements.txt and the need for a better solution.

The Limitations of Requirements.txt

One of the main limitations of requirements.txt is its lack of metadata. This means that it only lists the package names and versions, without any information about the project or its dependencies. This makes it difficult to track down the source of a dependency or determine which packages are no longer needed. Additionally, requirements.txt does not support specifying dependencies based on specific operating systems or Python versions, making it challenging to ensure compatibility across different environments.

Introducing Poetry: A Modern Solution for Managing Dependencies

Enter Poetry, a modern dependency management tool for Python projects. Poetry is a command-line tool that not only manages dependencies but also handles project metadata. This means that it not only lists the packages and their versions but also provides information about the project, such as its name, author, and description. This makes it easier to track down dependencies and understand the project’s structure.

Managing Dependencies with Poetry

Using Poetry is simple and intuitive. First, you need to create a new project and specify the dependencies in the pyproject.toml file. This file is similar to requirements.txt, but it also includes project metadata. Next, you can use the poetry install command to install all the dependencies listed in the pyproject.toml file. Poetry also supports virtual environments, making it easy to manage dependencies for different projects without conflicts.

The Benefits of Using Poetry

The use of Poetry for managing Python project dependencies has several benefits. Firstly, it provides a more organized and structured way to manage dependencies, making it easier to track down issues and ensure compatibility across environments. Additionally, Poetry supports both PyPI and private repositories, giving developers more flexibility in managing their dependencies. Lastly, Poetry has a built-in lock file that ensures reproducibility, meaning that the same dependencies will be installed every time the project is deployed.

In conclusion, requirements.txt is no longer the best solution for managing Python project dependencies. Its lack of metadata and limited functionality have made it obsolete in today’s complex Python ecosystem. Poetry provides a modern and efficient alternative, with its support for project metadata.

Discover the full story originally published on Towards Data Science.

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TL;DR: Omitted variable bias refers to the potential bias in statistical analysis when a key variable is left out of the model, leading to inaccurate results. To avoid this, researchers should carefully consider all relevant variables and include them in their analysis.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Understanding Omitted Variable Bias: What It Is and Why It Matters

Omitted Variable Bias is a commonly encountered issue in statistical analysis. It occurs when a relevant variable is left out of a statistical model, leading to biased and inaccurate results. In this blog post, we will explore the concept of Omitted Variable Bias, its impacts, and how to avoid it in your own data analysis.

What is Omitted Variable Bias?

Omitted Variable Bias occurs when a relevant variable is not included in a statistical model, leading to biased estimates of the relationship between the included variables. This can happen for various reasons, such as limited data availability, oversight, or simply not knowing which variables to include. The presence of Omitted Variable Bias can significantly affect the accuracy and reliability of statistical results, making it a crucial concept to understand for anyone working with data.

Impacts of Omitted Variable Bias

The presence of Omitted Variable Bias can have significant impacts on the results of a statistical analysis. It can lead to both overestimation and underestimation of the relationship between the included variables, making it difficult to draw accurate conclusions. In some cases, Omitted Variable Bias can even reverse the direction of the relationship, leading to misleading and incorrect interpretations. This can have serious consequences, especially in fields such as economics, where policy decisions are often based on statistical analysis.

How to Avoid Omitted Variable Bias

The most effective way to avoid Omitted Variable Bias is to be aware of its potential presence and take steps to address it in your analysis. This can include conducting a thorough review of the literature to identify all relevant variables, using statistical techniques such as stepwise regression to determine which variables to include, and performing sensitivity analyses to assess the impact of potential omitted variables. It is also important to consider the underlying theory and logic behind the variables included in the model to ensure they are relevant and appropriate.

Conclusion

In conclusion, Omitted Variable Bias is a common issue in statistical analysis that can have significant impacts on the accuracy and reliability of results. It occurs when a relevant variable is left out of a statistical model, leading to biased estimates of the relationship between the included variables. To avoid Omitted Variable Bias, it is important to be aware of its potential presence and take steps to address it in your analysis. By understanding and addressing this issue, we can ensure more accurate and reliable results in our data analysis.

Crafted using generative AI from insights found on Towards Data Science.

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“Learn how to boost your productivity by using AWS Gen AI to quickly summarize meeting notes. Follow a simple guide on leveraging AWS Lambda, Bedrock, and S3 to create a streamlined workflow for efficient meeting summaries.”

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

In today’s fast-paced business world, time is of the essence. Meetings are an essential part of any organization, but they can also be a major drain on productivity. Taking meeting notes can be a tedious and time-consuming task, especially when there are multiple meetings in a day. However, with the help of AWS Gen AI, you can now summarize your meeting notes in seconds, freeing up valuable time for other important tasks.

What is AWS Gen AI?

AWS Gen AI is a service provided by Amazon Web Services (AWS) that uses artificial intelligence (AI) and machine learning (ML) to analyze and summarize text. It can quickly identify key points and important information from a large amount of text, making it an ideal tool for summarizing meeting notes. This service is available as part of the AWS AI/ML suite and can be easily integrated into your existing workflow.

Creating a Workflow with AWS Lambda, Bedrock, and S3

To leverage the power of AWS Gen AI for summarizing meeting notes, you will need to create a workflow that utilizes AWS Lambda, Bedrock, and S3. Here is a comprehensive walkthrough on how you can do that:

Step 1: Set up a Lambda function

The first step is to set up a Lambda function that will act as the trigger for your workflow. This function will receive the meeting notes as input and send them to AWS Gen AI for summarization.

Step 2: Configure Bedrock

Next, you will need to configure Bedrock, a serverless framework for AWS, to handle the workflow. Bedrock will help you manage the different components of your workflow and ensure that they work together seamlessly.

Step 3: Create an S3 bucket

Now, you will need to create an S3 bucket to store the summarized meeting notes. This will also act as the output destination for your workflow.

Step 4: Connect the components

The final step is to connect all the components together. The Lambda function will trigger the workflow, which will then use Bedrock to send the meeting notes to AWS Gen AI for summarization. The summarized notes will then be stored in the S3 bucket.

Conclusion

In conclusion, utilizing AWS Gen AI, Lambda, Bedrock, and S3 can greatly increase productivity by automating the task of summarizing meeting notes. This comprehensive walkthrough provides a step-by-step guide on how to set up this workflow, making it easier and faster to summarize meeting notes in just seconds. With this powerful combination of tools, individuals and teams can save time and effort while still ensuring that important information from meetings is captured and easily accessible.

Crafted using generative AI from insights found on Towards Data Science.

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TL;DR: Plotly is a great tool for creating professional visualizations. Some key features to know are its ease of use, interactive capabilities, and compatibility with leading newspapers. You can create high-quality charts and graphs that will impress your audience. No need for complex or fancy language, just give it a try!”

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

In today’s data-driven world, creating professional visualizations is a crucial skill for anyone working with data. Whether you are a data analyst, a journalist, or a business professional, being able to effectively communicate your insights through visualizations is essential. Plotly is a popular data visualization tool that allows users to create interactive and professional-looking visualizations. In this blog post, we will discuss seven key features you should know for creating professional visualizations with Plotly and how to create visualizations at the level of leading newspapers.

1. Interactive and Dynamic Visualizations

One of the key features of Plotly is its ability to create interactive and dynamic visualizations. This means that users can hover over data points, zoom in and out, and toggle between different views of the data. This not only makes the visualizations more engaging but also allows viewers to explore the data in more detail. With Plotly, you can create interactive visualizations that are not only visually appealing but also informative.

2. Customizable Layouts and Themes

Another important feature of Plotly is its customizable layouts and themes. With Plotly, you can customize the layout of your visualization by adjusting the size, color, and font of different elements. You can also choose from a variety of pre-designed themes to give your visualization a professional and polished look. This allows you to create visualizations that are consistent with your brand or publication’s style.

3. Multiple Chart Types

Plotly offers a wide range of chart types to choose from, including bar charts, line charts, scatter plots, and more. This allows you to select the most suitable chart type for your data and the story you want to tell. You can also combine different chart types in one visualization to create more complex and informative visualizations.

4. Collaboration and Sharing

With Plotly, you can collaborate with your team members and share your visualizations with others. This is particularly useful for teams working on data projects together. You can also embed your visualizations on websites or share them on social media platforms. This makes it easy to share your insights with a wider audience.

5. Integration with Other Tools

Plotly integrates with other popular data analysis and visualization tools, such as Excel, Tableau, and R. This allows you to import data from these tools and create visualizations using Plotly. This integration makes it easier to incorporate Plotly into your existing data analysis workflow.

6. Customizable Interactivity

In addition to the default interactive features, Plotly also allows users to customize interactivity.

In summary, by understanding the key features for creating professional visualizations with Plotly, you can elevate your visualization skills to the level of leading newspapers. These features will help you create visually stunning and informative graphics that effectively convey your data and insights. With Plotly, creating professional and impactful visualizations is within your reach.

Crafted using generative AI from insights found on Towards Data Science.

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The author shares their journey of becoming a data scientist at Meta, without a stats degree or attending a bootcamp, by working 6 different jobs and making 2 career pivots. They offer advice on how to break into the field and the skills they found most valuable in their career path.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

In today’s data-driven world, the demand for data scientists is at an all-time high. However, many people assume that a career in data science requires a background in statistics or a degree from a top bootcamp. As someone who didn’t have either, I never thought I could become a data scientist. But here I am, working as a data scientist at Meta, and I want to share my journey with you.

My Background and Early Career

I graduated from college with a degree in economics and started my career as a financial analyst at a large corporation. I enjoyed working with numbers, but I didn’t feel fulfilled in my role. I wanted to explore other career options, and that’s when I stumbled upon data science. It seemed like the perfect blend of my analytical skills and my interest in technology. However, I had no formal training in statistics, and I couldn’t afford to attend a bootcamp. Despite this, I was determined to break into the field.

My First Job in Data Science

After months of self-study and networking, I landed my first job as a data analyst at a small startup. It was a steep learning curve, but I was eager to prove myself. I spent long hours learning new tools and techniques, and I quickly became the go-to person for data-related tasks. My hard work paid off, and I was promoted to a data scientist within a year.

Pivoting to a New Industry

As a data scientist, I was constantly learning and growing, but I wanted to explore new industries. I took a risk and joined a healthcare company as a data scientist. The transition was challenging, but I was able to apply my skills to solve real-world problems in a completely different field. After a few years, I felt confident in my abilities as a data scientist and wanted to take on a new challenge.

Joining Meta as a Data Scientist

When I saw an opening for a data scientist at Meta, I knew I had to apply. The company’s mission to use data for social good aligned with my personal values, and I was excited about the opportunity to work with a diverse team of talented individuals. Despite not having a stats degree or a bootcamp certification, I was confident in my skills and experiences. After a rigorous interview process, I was offered the job and accepted it without hesitation.

Conclusion

In conclusion, this article has shared the inspiring journey of becoming a data scientist at Meta without a statistics degree or attending a bootcamp. The author’s experience of having 6 different jobs and making 2 career pivots highlights the importance of determination, hard work, and continuous learning in achieving success in the field of data science. This serves as a reminder that anyone can pursue their passion and achieve their goals, regardless of their background or previous experiences.

Crafted using generative AI from insights found on Towards Data Science.

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