If you are looking to start a career in data science, you may find it overwhelming at first. You may think that you have to learn all of statistics, calculus, linear algebra, programming, distributed computing, databases, machine learning, experimental design, clustering, visualization, natural language processing, deep learning, and more. However, that is not true.
So, what is data science all about? It is the process that involves asking questions and answering them with the use of data. In general, data science involves:
- Asking a question
- Gathering data to help answer the question
- Cleaning the data
- Exploring, analyzing, and visualizing the data
- Evaluating and building a machine learning model
To do all these, you don’t necessarily need to have a mastery of deep learning or advanced mathematics. However, you need to know to program and be able to work with data using a programming language. While mathematical fluency is required for getting good at data science, a basic math understanding is good enough for getting started. You may indeed need other specialized skills for solving some data science problems in the future. However, for starting your career in the field, you don’t need to have expertise in all these skills.
Get comfortable with programming
When it comes to programming languages in data science, both R and Python are good choices. R finds more use in academia while Python is preferred in the industry. However, there are abundant packages in both languages for supporting data science workflow.
There is no need to learn both R and Python, to begin with. Rather, your focus should be to learn one language and get acquainted with its data science packages. You may consider undergoing data science training in Mumbai to learn a programming language and how to use it in data science.
There is no need to become a programming expert before learning other things in data science. Your focus should be to get a good understanding of data structures, data types, imports, conditional statements, functions, loops, comparisons, and comprehensions. If you are familiar with these topics, you have a basic understanding of the programming language.
Learn how to analyze, manipulate and visualize data
If you are using Python to work with data, you should know about the pandas library. This library provides a data structure for tabular data having columns of various types, similar to the SQL table or Excel spreadsheet. It includes tools to read and write data, handle missing data, clean data, filter data, merge datasets, visualize data, and a lot more. If you learn pandas, you will become more efficient while working with data. Pandas has immense functionality and provides numerous ways of accomplishing a task. It can seem challenging and overwhelming to learn pandas and discover the best practices. You may want to take up a course to get better at using the tool.
The scikit-learn library is provided by Python for machine learning. The most lucrative aspect of data science is predicting the future or extracting insights from data automatically. There are many reasons why scikit-learn is a highly popular machine learning library:
- Provides a consistent and clean interface to a variety of models
- Provides several tuning parameters but selects sensible defaults for each model
- It has exceptional documentation which helps in understanding the models and how they can be used popularly.
Machine learning is still a quickly evolving field and is fairly complex. There is a steep learning curve associated with scikit-learn as well. So, you may have to take some time to get a grasp of the tool as well as machine learning fundamentals.
Given the complex nature of machine learning, scikit-learn might not be enough to gain a more in-depth understanding of machine learning. If you want to gain expertise in machine learning, you will need experience as well as further studying.
Keep practicing and learn more
Whether you want to launch your data science career or take it to the next level, you need to be willing to learn. The key to improving your skills in data science is finding what motivates you. You need to practice what you learn and keep learning more. There are many ways to learn and keep improving yourself, including online courses, Kaggle competitions, reading blogs, reading books, attending conferences or meetups.
- With Kaggle competitions, you get the opportunity to practice data science and you don’t even have to come up with the problem on your own. No need to worry about your current position. You should focus on learning something new when you participate in each competition. It is worth noting that you will not only practice asking questions, but also data gathering, and result communication, which are important aspects of data science workflow as well.
- Contribute to an open-source project if you want to practice collaboration with others. You need to be accustomed to GitHub for the same. If Git is new to you, you would want to get introduced to it first before collaborating on projects.
- Creating data science projects on your own is great as well. If you do so, make sure to share your projects on GitHub. You should include write ups as well. It shows that you have the necessary skills and know how to work with data science.
- Engaging with the Python community can be rewarding as well. Attending data science conferences can help you make connections and learn new things as well. You may even consider subscribing to email newsletters
Data science continues to evolve at a rapid rate and it is truly exciting to become a data scientist If you are just beginning your journey in data science, there is so much more to learn. It is impossible to master everything in this vast and complex field. You just need to get started and keep learning and practicing throughout. Through your learning process, simultaneously continue to build your profile to get recognition from potential recruiters. Eventually, you will develop the required skills and get noticed. That should launch a prosperous career for you.