7 answers
Tasawar
Student
Lahore, Punjab, Pakistan
3
Questions
Asked
876 views
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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7 answers
Joshua Allard, Ph.D.
Data Science & AI, Quantum AI designer developer
39
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.
Adit Sheth
Senior Software Engineer
117
Answers
Bellevue, Washington
Updated
Adit’s Answer
As a guide, I encourage you to concentrate on three pivotal areas to kickstart your data science career:
1. Fundamental Skills: Begin by becoming proficient in Python and SQL, the cornerstones of data manipulation and analysis. Additionally, establish a robust foundation in mathematics, particularly statistics, probability, and linear algebra. These subjects will assist you in comprehending data models and algorithms.
2. Hands-on Experience: Engage in projects that utilize real-world datasets. Websites like Kaggle present novice-friendly projects where you can put your data cleaning, feature engineering, and visualization skills into practice.
3. Educational Courses and Resources: Sign up for well-organized courses such as "Python for Data Science" on Coursera or "Introduction to Data Science" on Udemy. These courses will steer you through the basics before delving into more advanced subjects like machine learning and AI.
Remember, I'm here if you need more tailored advice or suggestions. Don't hesitate to get in touch!
1. Fundamental Skills: Begin by becoming proficient in Python and SQL, the cornerstones of data manipulation and analysis. Additionally, establish a robust foundation in mathematics, particularly statistics, probability, and linear algebra. These subjects will assist you in comprehending data models and algorithms.
2. Hands-on Experience: Engage in projects that utilize real-world datasets. Websites like Kaggle present novice-friendly projects where you can put your data cleaning, feature engineering, and visualization skills into practice.
3. Educational Courses and Resources: Sign up for well-organized courses such as "Python for Data Science" on Coursera or "Introduction to Data Science" on Udemy. These courses will steer you through the basics before delving into more advanced subjects like machine learning and AI.
Remember, I'm here if you need more tailored advice or suggestions. Don't hesitate to get in touch!
Fazal Elahi
online assignment writer
48
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.
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6193
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Hello Tasawar!
How to Start a Career in Data Science as a Beginner
Starting a career in data science can be an exciting journey, especially if you have a background in software engineering. Here’s a step-by-step guide to help you transition into data science effectively.
1. Understand the Basics of Data Science
Before diving into technical skills, it’s essential to grasp what data science entails. Data science combines statistics, programming, and domain knowledge to extract insights from data. Familiarize yourself with the following concepts:
Data Collection: Understanding how data is gathered from various sources.
Data Cleaning: Learning how to preprocess and clean data for analysis.
Exploratory Data Analysis (EDA): Gaining insights through visualizations and summary statistics.
Modeling: Understanding machine learning algorithms and their applications.
2. Learn Programming Languages
Programming is fundamental in data science. The two most commonly used languages are:
Python: Widely used due to its simplicity and extensive libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
R: Particularly strong in statistical analysis and visualization.
Start by taking online courses or tutorials focused on these languages. Websites like Codecademy or Coursera offer beginner-friendly courses.
3. Acquire Statistical Knowledge
A solid understanding of statistics is crucial for interpreting data correctly. Focus on the following topics:
Descriptive Statistics
Inferential Statistics
Probability Theory
Hypothesis Testing
You can find resources on platforms like Khan Academy or edX.
4. Explore Data Manipulation and Visualization Tools
Familiarize yourself with tools that help manipulate and visualize data:
Pandas: For data manipulation in Python.
Matplotlib/Seaborn: For creating visualizations.
Tableau/Power BI: For business intelligence and dashboard creation.
Online courses specific to these tools can be found on platforms like Udacity or LinkedIn Learning.
5. Learn Machine Learning Fundamentals
Once you have a good grasp of programming, statistics, and data manipulation, start exploring machine learning concepts:
Supervised vs Unsupervised Learning
Common Algorithms (e.g., Linear Regression, Decision Trees)
Courses such as Andrew Ng’s Machine Learning course on Coursera are highly recommended.
6. Work on Projects
Apply your knowledge by working on real-world projects. This could include:
Analyzing publicly available datasets (e.g., Kaggle datasets).
Building predictive models.
Document your projects on platforms like GitHub to showcase your work to potential employers.
7. Join Online Communities
Engage with other learners and professionals in the field through forums such as:
Kaggle: Participate in competitions and discussions.
Stack Overflow: Ask questions and learn from others’ experiences.
Networking can provide valuable insights and job opportunities.
8. Consider Formal Education or Certifications
While self-study is effective, formal education can also enhance your credentials. Look for specialized programs or certifications in data science offered by universities or online platforms like Coursera or edX.
9. Stay Updated with Industry Trends
Data science is an evolving field; staying updated with the latest trends is crucial. Follow blogs, podcasts, webinars, and research papers related to AI and data science.
By following these steps systematically, you will build a strong foundation for a successful career in data science.
Top 3 Authoritative Sources Used in Answering this Question:
1. Coursera
A leading online learning platform offering courses from top universities worldwide including specialized programs in Data Science.
2. Khan Academy
A non-profit educational organization providing free resources across various subjects including mathematics and statistics essential for understanding data science fundamentals.
3. Kaggle
A platform for predictive modeling competitions that also offers datasets for practice along with community-driven discussions which are invaluable for aspiring data scientists.
Probability the answer is correct: 95%
God Bless You!
JC.
How to Start a Career in Data Science as a Beginner
Starting a career in data science can be an exciting journey, especially if you have a background in software engineering. Here’s a step-by-step guide to help you transition into data science effectively.
1. Understand the Basics of Data Science
Before diving into technical skills, it’s essential to grasp what data science entails. Data science combines statistics, programming, and domain knowledge to extract insights from data. Familiarize yourself with the following concepts:
Data Collection: Understanding how data is gathered from various sources.
Data Cleaning: Learning how to preprocess and clean data for analysis.
Exploratory Data Analysis (EDA): Gaining insights through visualizations and summary statistics.
Modeling: Understanding machine learning algorithms and their applications.
2. Learn Programming Languages
Programming is fundamental in data science. The two most commonly used languages are:
Python: Widely used due to its simplicity and extensive libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
R: Particularly strong in statistical analysis and visualization.
Start by taking online courses or tutorials focused on these languages. Websites like Codecademy or Coursera offer beginner-friendly courses.
3. Acquire Statistical Knowledge
A solid understanding of statistics is crucial for interpreting data correctly. Focus on the following topics:
Descriptive Statistics
Inferential Statistics
Probability Theory
Hypothesis Testing
You can find resources on platforms like Khan Academy or edX.
4. Explore Data Manipulation and Visualization Tools
Familiarize yourself with tools that help manipulate and visualize data:
Pandas: For data manipulation in Python.
Matplotlib/Seaborn: For creating visualizations.
Tableau/Power BI: For business intelligence and dashboard creation.
Online courses specific to these tools can be found on platforms like Udacity or LinkedIn Learning.
5. Learn Machine Learning Fundamentals
Once you have a good grasp of programming, statistics, and data manipulation, start exploring machine learning concepts:
Supervised vs Unsupervised Learning
Common Algorithms (e.g., Linear Regression, Decision Trees)
Courses such as Andrew Ng’s Machine Learning course on Coursera are highly recommended.
6. Work on Projects
Apply your knowledge by working on real-world projects. This could include:
Analyzing publicly available datasets (e.g., Kaggle datasets).
Building predictive models.
Document your projects on platforms like GitHub to showcase your work to potential employers.
7. Join Online Communities
Engage with other learners and professionals in the field through forums such as:
Kaggle: Participate in competitions and discussions.
Stack Overflow: Ask questions and learn from others’ experiences.
Networking can provide valuable insights and job opportunities.
8. Consider Formal Education or Certifications
While self-study is effective, formal education can also enhance your credentials. Look for specialized programs or certifications in data science offered by universities or online platforms like Coursera or edX.
9. Stay Updated with Industry Trends
Data science is an evolving field; staying updated with the latest trends is crucial. Follow blogs, podcasts, webinars, and research papers related to AI and data science.
By following these steps systematically, you will build a strong foundation for a successful career in data science.
Top 3 Authoritative Sources Used in Answering this Question:
1. Coursera
A leading online learning platform offering courses from top universities worldwide including specialized programs in Data Science.
2. Khan Academy
A non-profit educational organization providing free resources across various subjects including mathematics and statistics essential for understanding data science fundamentals.
3. Kaggle
A platform for predictive modeling competitions that also offers datasets for practice along with community-driven discussions which are invaluable for aspiring data scientists.
Probability the answer is correct: 95%
God Bless You!
JC.
Biju Baburajan
Product Leader, Startups, Software development & AI
12
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.
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.
Damien Rodriguez
Business Analyst
1
Answer
San Antonio, Texas
Updated
Damien’s Answer
Hi Tasawar,
Gaining technical expertise is crucial, but the real key is applying that knowledge in real-world scenarios. I suggest you find data projects that match your interests. This will keep you engaged and motivated as you delve into the subject matter. Here are some steps to guide you. As you navigate through these projects, you'll develop a richer understanding of the topics involved. Once you're done, consider setting up a website to display your work. This can be a great asset during job interviews, as it can enhance your credibility and make you a more attractive candidate. Good luck!
Steps for Project Execution:
1) Determine your problem statement
2) Find sources for raw data
3) Use Python/SQL for data extraction into a database, a process known as ETL
4) Transform and clean the data
5) Implement data science models
6) Try writing code in Python Markdown, enabling you to display both the code and its output directly to others.
Gaining technical expertise is crucial, but the real key is applying that knowledge in real-world scenarios. I suggest you find data projects that match your interests. This will keep you engaged and motivated as you delve into the subject matter. Here are some steps to guide you. As you navigate through these projects, you'll develop a richer understanding of the topics involved. Once you're done, consider setting up a website to display your work. This can be a great asset during job interviews, as it can enhance your credibility and make you a more attractive candidate. Good luck!
Steps for Project Execution:
1) Determine your problem statement
2) Find sources for raw data
3) Use Python/SQL for data extraction into a database, a process known as ETL
4) Transform and clean the data
5) Implement data science models
6) Try writing code in Python Markdown, enabling you to display both the code and its output directly to others.