This article was written by Rosana de Oliveira Gomes, Omdena's chief machine learning engineer, and Joseph Itopa A , Omdena's junior machine learning engineer .
Transitioning from a traditional industry to a brand new data science career feels like boarding an airplane that has already taken off.
Data science majors are relatively new, which means that the careers of many data scientists and machine learning engineers do not start on this path. They also switched careers from other fields, perhaps like many people who read this article.
Therefore, this article will focus on the difficulties that this profession will encounter, what data processing tools and skills should be used to enter the data science industry, and provide practical suggestions on how to overcome these difficulties.
Technical difficulties and how to overcome them
01 Mathematics and Programming
Data science does not require any advanced mathematics knowledge learned in universities. But every artificial intelligence algorithm is based on some mathematical structures you need to understand , including some concepts in linear algebra and calculus . To explain the results of data analysis, knowledge of probability and statistics is required for statistical analysis.
Mathematics provides basic concepts, and programming languages are tools to make these concepts concrete. So learning a programming language is very necessary, people usually choose Python or R language, or maybe a combination of SQL and Bash. A survey of the programming software KDnuggets used by data scientists shows that Python has surpassed R as the data scientist's preferred data analysis tool.
But the choice of programming language can basically be boiled down to task requirements and style preferences. Python is easy to learn for people with programming experience and can be widely used in various industries and professional fields, such as data science and machine learning. If you have a background in statistics and are mainly engaged in analysis, R is also a good choice.
It has built-in tools and databases , and can analyze the results of data through data reports. After insisting on using a programming language, you can start building some data models after completing the basic knowledge.
From experience, to acquire the necessary data science skills, you have to choose only one programming language at a time to learn and stick to it.
02 learn to solve problems
Data science can be seen as the ability to solve problems with creative and logical thinking . This requires a certain programming knowledge and an in-depth understanding of algorithms through practice.
After mastering some basic knowledge of programming, you can use courses or platform exercises to solve data problems in reality.
GeeksforGeeks provides practical projects for competitors' coding , Python, JAVA and SQL . Solving some Kaggle competition problems can also improve the problem-solving ability, because it can easily use real data for practice and get a lot of help in it.
DataCamp's non-directed projects are a great way to find solutions for open projects.
It is important to get some achievements in the data science career . In a recent Omdena webinar, data science communicator ericweber said: “Don’t learn just for income, but learn for things that bring you happiness; otherwise, you may Soon exhausted."
03 Join a collaborative project
After practicing the algorithm on the project, you need to prepare for more advanced projects. This is the role of collaboration platforms. Collaborative data science projects rely on platforms to develop new projects in diverse and effective ways.
The consistency and interest of street zip codes can be found in the collaborative project . When fighting unstructured and messy data, you can learn from other data scientists and make new friends.
Inspiring collaborations include data types , science to data science, and the use of data science in society . These options are usually based on a specific location and may be costly or competitive due to limited availability and cumbersome use process.
Omdena is another option for collaborative projects. It starts several projects every month and follows a voluntary principle to solve real-world problems through online collaboration. Learners work with domain experts to help them stay motivated through webinars, courses, books, and blog posts.
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