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What skills do you need to transition from mobile app developer to data scientist?

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If you are a mobile app developer who wants to switch to data science, you might wonder what skills you need to make the transition. Data science is a broad and interdisciplinary field that involves collecting, analyzing, and presenting data to solve problems and generate insights. Mobile app development is a specific and technical field that involves creating, testing, and maintaining applications for mobile devices. While both fields require coding, logic, and creativity, they also have different goals, tools, and methods. In this article, we will explore what skills you need to transition from mobile app developer to data scientist, and how you can acquire them.

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Duncan K.

Global Director of Solutions Engineering | Empowering Organizations with Innovative Technology Solutions

1 Coding languages

One of the most obvious skills you need to transition from mobile app developer to data scientist is coding languages. Depending on what platform you develop for, you might be familiar with languages such as Java, Kotlin, Swift, Objective-C, or C#. However, these languages are not very common in data science, where Python, R, SQL, and Scala are more widely used. Python and R are especially popular for data analysis, visualization, and machine learning, while SQL and Scala are useful for working with large and complex data sets. To transition from mobile app developer to data scientist, you need to learn these languages or at least be able to adapt to them quickly.

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2 Data analysis

Another skill you need to transition from mobile app developer to data scientist is data analysis. Data analysis is the process of exploring, manipulating, and interpreting data to answer questions and find patterns. As a mobile app developer, you might have some experience with data analysis, such as using analytics tools to track user behavior, feedback, and performance. However, as a data scientist, you need to go deeper and use more advanced techniques, such as statistics, hypothesis testing, regression, clustering, classification, and natural language processing. To transition from mobile app developer to data scientist, you need to master these techniques and be able to apply them to various data sources and scenarios.

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Duncan K.

Global Director of Solutions Engineering | Empowering Organizations with Innovative Technology Solutions
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understanding concepts like linear algebra, calculus, probability, and statistical inference. Python is the most popular language for data science, but learning R and SQL will also be beneficial. Familiarity with scripting languages like Bash can also be helpful. Mastering libraries like Pandas, NumPy, and Matplotlib in Python will be crucial for wrangling and visualizing data. Understand and implement various machine learning algorithms, including supervised and unsupervised learning techniques. Libraries like scikit-learn in Python can be your learning companions. While not always essential, familiarity with deep learning frameworks like TensorFlow or PyTorch can open doors to more advanced data science problems.

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Rupert Young
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If you don't love stats and finding patterns in data, you should probably not consider making the transition. Not a data science and analysis jobs are made the same so to break in you might focus on roles considering data analysis presentation rather than a team who has large data sets and needs strong skills in building pipelines.

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Damian Kleiman

Senior Python Developer | JavaScript | GIS | Data Science | Team Manager | Consulting | Business | Entrepreneur
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Sin dudas para el análisis de datos de deben entender los conceptos claves. Además, es importante conocer un lenguaje que permita procesar los datos (como Python con NumPy, Tensorflow y similar). También es indispensable conocer de bases de datos, para eso SQL es de gran ayuda.

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3 Data visualization

A third skill you need to transition from mobile app developer to data scientist is data visualization. Data visualization is the art of presenting data in a clear, engaging, and meaningful way, using charts, graphs, maps, dashboards, and other visual elements. As a mobile app developer, you might have some experience with data visualization, such as using charts and graphs to display data in your app or report. However, as a data scientist, you need to be more proficient and creative with data visualization, and use tools such as Matplotlib, Seaborn, ggplot2, Tableau, or Power BI. To transition from mobile app developer to data scientist, you need to learn these tools and be able to create effective and appealing data visualizations.

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4 Machine learning

A fourth skill you need to transition from mobile app developer to data scientist is machine learning. Machine learning is a branch of artificial intelligence that involves training computers to learn from data and make predictions or decisions. As a mobile app developer, you might have some exposure to machine learning, such as using frameworks such as TensorFlow, PyTorch, or Core ML to implement features such as image recognition, speech recognition, or recommendation systems. However, as a data scientist, you need to have a deeper understanding and application of machine learning, and use algorithms such as linear regression, logistic regression, k-means, decision trees, random forests, neural networks, or deep learning. To transition from mobile app developer to data scientist, you need to study these algorithms and be able to use them for various data science projects.

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5 Communication skills

A fifth skill you need to transition from mobile app developer to data scientist is communication skills. Communication skills are the ability to convey your ideas, findings, and recommendations to different audiences, such as clients, managers, colleagues, or stakeholders. As a mobile app developer, you might have some communication skills, such as writing documentation, giving feedback, or presenting your app. However, as a data scientist, you need to have more refined and persuasive communication skills, and use tools such as Jupyter Notebook, Markdown, or PowerPoint to create reports, slides, or stories. To transition from mobile app developer to data scientist, you need to improve your communication skills and be able to explain your data science work in a clear and compelling way.

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6 Business acumen

A sixth skill you need to transition from mobile app developer to data scientist is business acumen. Business acumen is the knowledge and understanding of the business context, goals, and challenges of your data science projects. As a mobile app developer, you might have some business acumen, such as knowing your target market, competitors, or monetization strategies. However, as a data scientist, you need to have more comprehensive and strategic business acumen, and use tools such as SWOT analysis, value proposition, or ROI calculation to align your data science solutions with the business objectives and values. To transition from mobile app developer to data scientist, you need to develop your business acumen and be able to provide data-driven insights and recommendations that add value to the business.

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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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What skills do you need to transition from mobile app developer to data scientist?

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1 Coding languages

2 Data analysis

3 Data visualization

4 Machine learning

5 Communication skills

6 Business acumen

7 Here’s what else to consider

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