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You want to become a data scientist in application development. What are the first steps you should take?

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Data science is a booming field that combines programming, statistics, and problem-solving skills to extract insights from data and create solutions for various domains. Application development is one of the areas where data science can have a huge impact, as it involves designing, building, and testing software applications that meet the needs and preferences of users. If you want to become a data scientist in application development, you need to take some steps to prepare yourself for this exciting and challenging career path. Here are some of the first steps you should take.

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Master the basics:

Start with a strong foundation in key areas—learn programming languages like Python, understand data structures, algorithms, and familiarize yourself with essential tools.By honing core skills such as coding and understanding algorithms, you're setting up for success. These foundational skills are your toolkit for tackling complex data science challenges in application development.
Embrace foundational models:

Utilize pre-trained models to save time and effort when working on data science projects that involve text mining or other predictive tasks.Incorporating existing models into your work streamlines the process. It's a smart shortcut that allows you to focus on customization rather than building from scratch, boosting efficiency and productivity.

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Paolo Tamagnini

Senior Data Scientist
Sayali Balgude

Gen AI Consultant @ Axa

1 Learn the basics

Before you dive into data science, you need to have a solid foundation in the core concepts and skills that are essential for any application developer. This includes learning how to code in one or more programming languages, such as Python, Java, or C#, that are widely used for data science and application development. You also need to understand the basics of data structures, algorithms, databases, and software engineering principles, such as version control, testing, debugging, and documentation. These skills will help you write clean, efficient, and maintainable code that can handle data processing and analysis tasks.

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Sayali Balgude

Gen AI Consultant @ Axa
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To begin your journey as a data scientist in application development, establish a strong foundation first. Master skills like mathematics, statistics, and a programming language like Python or R. Understand core data science concepts, including data structures, algorithms, and essential libraries like NumPy, Pandas, Matplotlib, and scikit-learn. Familiarize yourself with different databases like SQL, NoSQL, MongoDB, etc for efficient data management. With these fundamentals in place, you'll be prepared to delve into advanced topics and succeed in data science.

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Vignesh Subramanian

AI Trainer & Prompt Engineer | Security Researcher @HackerOne | Specializing in Large Language Models (LLMs) for Coding & Algorithmic Challenges
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To learn the basics for data science and application development, here's a suggested roadmap: Programming Languages: Start with Python, as it's widely used in data science and has a simple syntax. You can then explore Java or C# for application development. Data Structures and Algorithms: Understand the fundamentals of data structures (arrays, linked lists, stacks, queues, trees, graphs) and algorithms (sorting, searching, dynamic programming, etc.). Databases: Learn about relational databases (SQL) and non-relational databases (NoSQL) to manage and query data efficiently.

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Rémy Wehrung
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Pour réussir en science des données, ma?trisez les bases du codage, des structures de données, des algorithmes, des bases de données et de l'ingénierie logicielle.

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Scott K.
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Learning theory, practical application, and process development strategies are great, but mastering the basics in whatever data science language fits the desired outcome and applying those basic skills to real-world business needs is a critical skill I would recommend to anyone as a starting point. Be humble, you will never "master" it all, but applying yourself, challenge your ingenuity, and never giving up even if the deadline is approaching are sought after concepts anyone should admire in business application development. What sets professional athletes apart from the rest of us? Because they mastered the basics, and apply those skills each and every time.

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Ashish Deshmukh

Data Analyst @ JYOTIRLING | Data Science, Machine Learning
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If you want to build career in data science then you first be proficient in One Programming Language of your choice but I will recommend Python Focus On Basics of python data structures. Then You should have knowledge of SQL, You should have familiarity of different file formats like(.jpg,.npg,.xlsx,.csv,.json). You should have good knowledge of statistics. You should analyze the data before going ahead and eliminate outliers so your model will be good. Try to make structured data so your model will perform very well keep your self updated and give dedicated practice everyday for other things you can directly download roadmap from Google.

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2 Explore data science tools

Data science requires a variety of tools and frameworks that can help you collect, store, manipulate, visualize, and model data. Some of the most common tools include libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn for Python, or packages such as Tidyverse, GGplot2, and Caret for R. These tools provide functions and methods that can simplify and speed up your data science workflow. You also need to familiarize yourself with data science platforms, such as Jupyter Notebook, Google Colab, or Kaggle, that allow you to create and share interactive notebooks that contain code, text, images, and charts. These platforms are useful for experimenting with data, presenting your findings, and collaborating with other data scientists.

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Paolo Tamagnini

Senior Data Scientist
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Low code data science (+/- AutoML) tools are also of great help, at the very least we should consider them for prototyping solutions..

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Rémy Wehrung
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Pour réussir en science des données, ma?trisez les outils et frameworks tels que NumPy, Pandas, Matplotlib, PyTorch, TensorFlow pour Python, ou Tidyverse, GGplot2, Caret pour R, ainsi que les plateformes Jupyter, Colab, Kaggle.

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3 Learn data science techniques

Data science involves applying various techniques and methods to analyze data and generate insights that can inform decision-making and improve application performance. Some of the most important techniques include data cleaning, data exploration, data visualization, feature engineering, machine learning, and deep learning. Data cleaning is the process of removing or correcting errors, outliers, duplicates, and missing values from data. Data exploration is the process of examining the characteristics, patterns, and relationships of data using descriptive statistics and visualizations. Data visualization is the process of creating graphs, charts, maps, and dashboards that can communicate data effectively. Feature engineering is the process of creating or transforming variables that can enhance the predictive power of machine learning models. Machine learning is the process of creating algorithms that can learn from data and make predictions or classifications. Deep learning is a subset of machine learning that uses artificial neural networks to perform complex tasks, such as image recognition, natural language processing, or speech synthesis.

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Paolo Tamagnini

Senior Data Scientist
(已编辑)
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Foundations models are an important trend... entails using a big multi-purpose model instead of training your own. LLMs are valid examples for text mining. This can save lots of time compared to training a custom model from scratch. XAI is useful to get an approximate explanation to any classification or regression prediction. On top of that there are also metrics to score the fairness of the model of those predictions can be linked to individuals where the model might have some gender, race ... bias What about productionization techniques (containerization, continuous deployment, MLOps, CICD, monitoring data drift ..)? Your implementation can be clean but it's made to run on a local system, not the company infrastructure!

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Rémy Wehrung
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La science des données comprend diverses techniques telles que le nettoyage, l'exploration, la visualisation des données, l'ingénierie des caractéristiques, l'apprentissage automatique et profond, pour améliorer les performances des applications.

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4 Build data science projects

One of the best ways to learn and demonstrate your data science skills is to build projects that solve real-world problems or showcase your creativity. Data science projects can range from simple to complex, depending on your goals, interests, and experience level. For example, you can start with a project that involves analyzing a dataset, such as the Titanic survival data, the Iris flower data, or the Boston housing data, and applying basic data science techniques, such as data cleaning, data exploration, data visualization, and machine learning. You can then move on to more advanced projects that involve collecting your own data, using web scraping or APIs, or applying deep learning techniques, such as building a face detection app, a sentiment analysis app, or a chatbot app. Data science projects can help you practice your skills, improve your portfolio, and impress potential employers.

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Sayali Balgude

Gen AI Consultant @ Axa
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Building a Data Science Portfolio: The next step is to solidify your learning by tackling real-world projects you can do this doing 2 things : First step Personal Projects: Utilize platforms like Kaggle, offering diverse datasets. Choose topics of interest, experiment, analyze, and refine your approach. This hands-on experience strengthens technical skills and showcases problem-solving abilities, building a compelling portfolio. Second Step Contribute to Open-Source Projects: On GitHub, explore data science projects aligned with your interests. Contribute meaningfully, collaborate with experienced practitioners, and learn from their expertise. This demonstrates commitment to the data science community while enhancing your skillset.

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5 Network and learn from others

Data science is a dynamic and evolving field that requires constant learning and updating of your skills and knowledge. One of the best ways to keep up with the latest trends and developments in data science and application development is to network and learn from others who share your passion and curiosity. You can join online communities, such as Reddit, Stack Overflow, or Medium, where you can ask questions, share your insights, and learn from experts and peers. You can also participate in online courses, webinars, podcasts, or blogs that offer valuable resources and tips on data science and application development. You can also attend offline events, such as meetups, workshops, hackathons, or conferences, where you can meet and interact with other data scientists and application developers, exchange ideas, and get feedback.

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Sayali Balgude

Gen AI Consultant @ Axa
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Continuous learning fuels data science success. Participate in online communities such as Kaggle, Data Science Central, and relevant subreddits to connect with peers, seek advice, and discuss best practices. Engaging in these platforms enables knowledge exchange and keeps you updated on industry trends. Additionally, attend local meetups and online conferences dedicated to data science application development. These events offer opportunities to network with professionals, gain valuable insights, and stay informed about the latest advancements in the field. By actively engaging in both online communities and offline events, you can enhance your skills and remain competitive in the dynamic field of data science.

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6 Keep practicing and improving

Data science is a field that requires continuous practice and improvement of your skills and knowledge. You should always seek new challenges and opportunities to apply your data science skills to different domains and problems. You should also always seek feedback and criticism from others who can help you identify your strengths and weaknesses, and provide suggestions for improvement. You should also always reflect on your learning process and outcomes, and identify what worked well and what didn't, and what you can do better next time. Data science is a rewarding and fulfilling career path that can offer you endless possibilities and opportunities to create value and impact with data. By taking these first steps, you can prepare yourself for becoming a data scientist in application development.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You want to become a data scientist in application development. What are the first steps you should take?

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1 Learn the basics

2 Explore data science tools

3 Learn data science techniques

4 Build data science projects

5 Network and learn from others

6 Keep practicing and improving

7 Here’s what else to consider

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