4 answers
Tasawar
Student
Lahore, Punjab, Pakistan
3
Questions
Asked
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How to Start a Career in Data Science as a Beginner After Quitting Game Development?
Hi, I am an undergraduate Software Engineering student in my 4th semester. Recently, I began pursuing game development as a career. However, I quickly became overwhelmed by the complexities of design, art, and development. Without much guidance, I decided to quit game development and shift my focus towards a career in data science or mobile app development. I find data science and AI more interesting and would prefer to pursue them.
Given that I am a complete beginner in data science, can anyone guide me on how to start this journey? What skills should I learn first, what resources or courses are best, and how should I progress step by step?
Thanks in advance!
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4 answers
Joshua Allard, Ph.D.
Data Science & AI, Quantum AI designer developer
25
Answers
Port St. Lucie, Florida
Updated
Joshua’s Answer
It's fantastic that you've found a new passion in data science and AI after exploring game development. Although shifting focus can seem daunting, the world of data science is full of opportunities, and starting from scratch is an exciting challenge. To begin, it's essential to build a solid foundation in programming. Since you already have a background in software engineering, you're familiar with coding. Still, you'll need to focus on languages like Python, which is widely used in data science for data manipulation, analysis, and visualization tasks. Libraries like Pandas, NumPy, and Matplotlib are great for these purposes. Additionally, learning SQL is crucial for managing databases and retrieving data.
Next, strengthen your understanding of mathematics, particularly statistics, probability, and linear algebra. These subjects are the backbone of data science, helping you interpret data, understand algorithms, and apply machine learning models. Brushing up on calculus will also help with optimization techniques in AI. As you dive deeper into data science, start with the core concepts of data wrangling and exploratory data analysis (EDA), which will allow you to clean and understand data. Once you're comfortable, move on to machine learning, where you’ll encounter algorithms like regression, decision trees, and clustering, which are vital to extracting insights from data.
For structured learning, online platforms like Coursera, edX, and Udemy offer excellent beginner courses. Andrew Ng’s Machine Learning course on Coursera is highly recommended to get a solid introduction to the field. DataCamp is also a wonderful resource for hands-on, interactive learning. Hands-on experience is vital as you progress, and platforms like Kaggle are great for applying your skills to real-world datasets and challenges. By participating in projects and competitions, you'll build your portfolio and deepen your understanding of data science concepts.
As you gain more experience, you can explore advanced topics like deep learning, neural networks, and natural language processing (NLP), which use frameworks such as TensorFlow or PyTorch. These areas are significant if you're interested in the AI side of data science. Suppose you’re still considering mobile app development. In that case, you might also want to learn Swift (for iOS) or Kotlin (for Android), combining these skills with data science to create mobile applications that use AI and data-driven features.
Ultimately, take things step by step, and don’t rush the process. Both data science and mobile app development offer exciting possibilities, and with your software engineering background, you're already in a great position to excel. Keep exploring, building projects, and learning, and you'll be well on your way to a rewarding career in data science and AI.
Next, strengthen your understanding of mathematics, particularly statistics, probability, and linear algebra. These subjects are the backbone of data science, helping you interpret data, understand algorithms, and apply machine learning models. Brushing up on calculus will also help with optimization techniques in AI. As you dive deeper into data science, start with the core concepts of data wrangling and exploratory data analysis (EDA), which will allow you to clean and understand data. Once you're comfortable, move on to machine learning, where you’ll encounter algorithms like regression, decision trees, and clustering, which are vital to extracting insights from data.
For structured learning, online platforms like Coursera, edX, and Udemy offer excellent beginner courses. Andrew Ng’s Machine Learning course on Coursera is highly recommended to get a solid introduction to the field. DataCamp is also a wonderful resource for hands-on, interactive learning. Hands-on experience is vital as you progress, and platforms like Kaggle are great for applying your skills to real-world datasets and challenges. By participating in projects and competitions, you'll build your portfolio and deepen your understanding of data science concepts.
As you gain more experience, you can explore advanced topics like deep learning, neural networks, and natural language processing (NLP), which use frameworks such as TensorFlow or PyTorch. These areas are significant if you're interested in the AI side of data science. Suppose you’re still considering mobile app development. In that case, you might also want to learn Swift (for iOS) or Kotlin (for Android), combining these skills with data science to create mobile applications that use AI and data-driven features.
Ultimately, take things step by step, and don’t rush the process. Both data science and mobile app development offer exciting possibilities, and with your software engineering background, you're already in a great position to excel. Keep exploring, building projects, and learning, and you'll be well on your way to a rewarding career in data science and AI.
Biju Baburajan
Product Leader, Startups, Software development & AI
7
Answers
Updated
Biju’s Answer
If you are interested to become a data scientist, start with the basics like variables, data types and basic level statistical analysis. once you are comfortable, move to complex analysis, probability, distributions, and hypothesis testing. They're the building blocks of data analysis.
Once you've got those down, try to explore data cleaning, learn how to handle missing values, outliers, and inconsistencies. Be familiar with Data Visualization tools.
To get in to the next level, start exploring machine learning techniques such as supervised learning ( Train models on labeled data to make predictions) and Unsupervised Learning: (Identify patterns in unlabeled data) . If you are not familiar with programming, I would recommend to learn Python which is critical in DS and AI.
I'm not recommending any specific courses or books, as there is a vast amount of resources available online on the mentioned topic. However, feel free to reach out if you need any specific recommendations.
Once you've got those down, try to explore data cleaning, learn how to handle missing values, outliers, and inconsistencies. Be familiar with Data Visualization tools.
To get in to the next level, start exploring machine learning techniques such as supervised learning ( Train models on labeled data to make predictions) and Unsupervised Learning: (Identify patterns in unlabeled data) . If you are not familiar with programming, I would recommend to learn Python which is critical in DS and AI.
I'm not recommending any specific courses or books, as there is a vast amount of resources available online on the mentioned topic. However, feel free to reach out if you need any specific recommendations.
Fazal Elahi
online assignment writer
39
Answers
Chitrāl, N.W.F.P
Updated
Fazal’s Answer
Hello Randy
Beginning a profession in information science in the wake of stopping game improvement can be a smooth change, particularly in the event that you have insight with coding and critical thinking. Here is a bit by bit guide:
### 1. **Leverage Your Programming Skills**
- **Programming Languages**: Your game improvement foundation probably gave you experience in programming dialects. Center around learning information science-explicit dialects like **Python** and **R**, which are broadly utilized in the field.
- **Libraries**: Dive more deeply into information science libraries, for example, **Pandas**, **NumPy**, **Scikit-learn** (for AI), and **Matplotlib** or **Seaborn** (for information representation).
### 2. **Learn Key Information Science Concepts**
- **Insights and Probability**: Reinforce your insight into measurements, as it's significant for dissecting information, making expectations, and testing speculations.
- **Information Manipulation**: Figure out how to clean, process, and control information, as a lot of information science includes changing crude information into usable experiences.
- **Information Visualization**: Figure out how to introduce information utilizing instruments like **Tableau**, **Power BI**, or Python's representation libraries.
### 3. **Take Online Courses**
- Stages like **Coursera**, **edX**, and **Udacity** offer novice agreeable courses in information science. A few suggested choices are:
- **"IBM Information Science Proficient Certificate"** on Coursera.
- **"Prologue to Information Science"** on edX.
- **"Information Science Nanodegree"** on Udacity.
### 4. **Work on Projects**
- **Begin Small**: Start by examining freely accessible datasets (e.g., from **Kaggle** or **UCI AI Repository**). Work on basic undertakings like foreseeing house costs, grouping pictures, or investigating overview information.
- **Fabricate a Portfolio**: Record your tasks and code on stages like **GitHub**. Your portfolio will be pivotal for exhibiting your abilities to possible businesses.
### 5. **Learn SQL**
- **SQL** is fundamental for questioning information bases. Work on composing questions, making data sets, and performing information examination utilizing SQL.
### 6. **Understand Machine Learning**
- Get to know **machine learning** calculations like direct relapse, choice trees, and grouping. These strategies are broadly utilized in information science.
- Work on applying these calculations to datasets utilizing libraries like **Scikit-learn** or **TensorFlow**.
### 7. **Network with Information Science Communities**
- Join information science networks and gatherings, for example, **Kaggle**, **Reddit's Information Science subreddit**, and **LinkedIn groups**. Take part in conversations, challenges, and hackathons to acquire insight and perceivability.
### 8. **Earn Certifications**
- Acquire affirmations in information science from all around perceived foundations. Having qualifications can help your resume and sign your obligation to the field.
### 9. **Apply for Temporary jobs or Section Level Positions**
- Indeed, even as a fledgling, you can apply for **data analyst** jobs, which frequently require less insight yet give you openness to the information science work process.
- Temporary positions or independent work can give involved insight while permitting you to progress from game turn of events.
### 10. **Highlight Adaptable Skills**
- Utilize your game improvement experience for your potential benefit. Abilities like programming, critical thinking, and inventive reasoning are profoundly applicable to information science. Underline these in your resume and during interviews.
By zeroing in on these means, you can effectively turn from game improvement to information science, a field with critical learning experiences.
Beginning a profession in information science in the wake of stopping game improvement can be a smooth change, particularly in the event that you have insight with coding and critical thinking. Here is a bit by bit guide:
### 1. **Leverage Your Programming Skills**
- **Programming Languages**: Your game improvement foundation probably gave you experience in programming dialects. Center around learning information science-explicit dialects like **Python** and **R**, which are broadly utilized in the field.
- **Libraries**: Dive more deeply into information science libraries, for example, **Pandas**, **NumPy**, **Scikit-learn** (for AI), and **Matplotlib** or **Seaborn** (for information representation).
### 2. **Learn Key Information Science Concepts**
- **Insights and Probability**: Reinforce your insight into measurements, as it's significant for dissecting information, making expectations, and testing speculations.
- **Information Manipulation**: Figure out how to clean, process, and control information, as a lot of information science includes changing crude information into usable experiences.
- **Information Visualization**: Figure out how to introduce information utilizing instruments like **Tableau**, **Power BI**, or Python's representation libraries.
### 3. **Take Online Courses**
- Stages like **Coursera**, **edX**, and **Udacity** offer novice agreeable courses in information science. A few suggested choices are:
- **"IBM Information Science Proficient Certificate"** on Coursera.
- **"Prologue to Information Science"** on edX.
- **"Information Science Nanodegree"** on Udacity.
### 4. **Work on Projects**
- **Begin Small**: Start by examining freely accessible datasets (e.g., from **Kaggle** or **UCI AI Repository**). Work on basic undertakings like foreseeing house costs, grouping pictures, or investigating overview information.
- **Fabricate a Portfolio**: Record your tasks and code on stages like **GitHub**. Your portfolio will be pivotal for exhibiting your abilities to possible businesses.
### 5. **Learn SQL**
- **SQL** is fundamental for questioning information bases. Work on composing questions, making data sets, and performing information examination utilizing SQL.
### 6. **Understand Machine Learning**
- Get to know **machine learning** calculations like direct relapse, choice trees, and grouping. These strategies are broadly utilized in information science.
- Work on applying these calculations to datasets utilizing libraries like **Scikit-learn** or **TensorFlow**.
### 7. **Network with Information Science Communities**
- Join information science networks and gatherings, for example, **Kaggle**, **Reddit's Information Science subreddit**, and **LinkedIn groups**. Take part in conversations, challenges, and hackathons to acquire insight and perceivability.
### 8. **Earn Certifications**
- Acquire affirmations in information science from all around perceived foundations. Having qualifications can help your resume and sign your obligation to the field.
### 9. **Apply for Temporary jobs or Section Level Positions**
- Indeed, even as a fledgling, you can apply for **data analyst** jobs, which frequently require less insight yet give you openness to the information science work process.
- Temporary positions or independent work can give involved insight while permitting you to progress from game turn of events.
### 10. **Highlight Adaptable Skills**
- Utilize your game improvement experience for your potential benefit. Abilities like programming, critical thinking, and inventive reasoning are profoundly applicable to information science. Underline these in your resume and during interviews.
By zeroing in on these means, you can effectively turn from game improvement to information science, a field with critical learning experiences.
Randy Lane
Consultant - and technical advisor
2
Answers
Bothell, Washington
Updated
Randy’s Answer
Hi Tasawar,
In the field of Data Science, Mathematics and Statistics are the key starting points. There are numerous books available that delve into the mathematical aspects of data.
As a Data Scientist, your role involves exploring data, employing a significant amount of math and statistics. This is done to identify patterns, formulate a hypothesis, and then create queries. Furthermore, you'll use statistical analysis and analytics to validate your hypothesis. This process is fundamental to your work as a Data Scientist.
In the field of Data Science, Mathematics and Statistics are the key starting points. There are numerous books available that delve into the mathematical aspects of data.
As a Data Scientist, your role involves exploring data, employing a significant amount of math and statistics. This is done to identify patterns, formulate a hypothesis, and then create queries. Furthermore, you'll use statistical analysis and analytics to validate your hypothesis. This process is fundamental to your work as a Data Scientist.