7 productivity tips for data science newbies
Data science is an exciting field and continues for the rapid development and evolution of technology. Knowing the theoretical principles enables you to analyze data and extract information, innovate and build applications, and produce unique solutions to real-world problems. However, data science can be quite intimidating if you are new to it. Here are some tips to improve yourself as an aspiring data scientist.
1. Learn a programming language and start coding
Programming is considered to be the heart of data science, and it is an essential skill that any data scientist should have. Two programming languages that are a good place to start would be SQL and python. Python is a simple and beginner-friendly programming language suitable for people with no coding experience. It is versatile, simple to learn and easy to use. Python has an active community and is easy to understand because it has a lot of online resources.
Structured query language or SQL is used to manage the data used for analysis. SQL helps you create arrangements and store the data collected for analysis. Once you have familiarized yourself with these programming languages.
2. Get a good understanding of the basic concepts
The vast field of data science contains theoretical concepts and advanced algorithms. As a beginner, take your time to study and understand the concepts. It is beneficial to have an excellent working understanding of the concepts. With a good knowledge of Python programming and the statistics and math required to implement algorithms, you can implement various algorithms from scratch without using the built-in libraries. The hands-on implementation will help you better understand general concepts and improve your programming skills. While it can be tempting to use built-in libraries and frameworks, implementing them will help you master your basics and support you in the long run.
3. Learn math and statistics
Mathematics is a fundamental requirement for a data scientist. If programming is considered the heart of data science, mathematics is its soul. Mathematical concepts like calculus, probability, statistics, and linear algebra are the concepts you need to understand the basic concepts of data science. Mathematics is needed to create predictive machine learning models, understand probabilistic and deterministic approaches to solving Bayesian problems, understand backpropagation in deep neural networks, analyze gradient descent, and much more. Most of them are concepts covered in your schooling and shouldn’t be too complicated to understand. Data science is essentially the intersection of programming and statistics. Professionals in this field often say that a data scientist knows more statistics than a programmer but more programming than a statistician.
4. Explore different libraries and algorithms
Python is a simple language to learn, and using it for data science projects is relatively easy, thanks to the variety of valuable libraries available. Most of them are easy to install and provide efficient and straightforward solutions, allowing data scientists to perform complex tasks with very few lines of code.
Python has a variety of library modules used to create machine learning models for data science projects. Some of the most commonly used popular libraries include NumPy, pandas, matplotlib, seaborn, Scipy, NLTK, etc. Numpy is used for numeric operations, Scipy for scientific sparse matrix processing, pandas to display data sets in a manner. The scikit-learn module in python is used to develop machine learning models via the various algorithm options available in the sklearn library. Additionally, you can build deep learning models using frameworks like TensorFlow and Pytorch. Matplotlib and seaborn are the two best library modules for visualizing and performing exploratory data analysis tasks. The algorithms used in machine learning models operate on data and are used for analysis and predictions. These are used in various applications used in the real world for classification, identification, detection, grouping, etc.
5. Work on data science projects that solve real business problems
When you have mastered the basics of programming, math, libraries, and algorithms, the next step is to come up with a problem statement and start working on data science projects. To really understand data science, you have to try to work on a lot of projects. You can start with simple beginner level projects and then move on to complex projects. Kaggle competitions are also a good starting point for beginners and freshers.
Theoretically, understand the intuition of machine learning and AI concepts and math behind these data science concepts. However, it would be best if you also learned how to implement the projects in real life scenarios. You shouldn’t be afraid to get your hands dirty with programming and implement these projects yourself. If you have completed some beginner level projects, you can aim a little higher for some intermediate level projects. You need to understand your skills and keep working to improve them. Don’t give up and persevere until you have completed your machine learning or data science projects.
6. Collaborate, analyze and explore
“Many ideas grow best when transplanted into a different mind than the one in which they germinated,” said Oliver Wendell Holmes. Collaboration among data scientists plays an important role in career progression and research in data science. While you can work on your own for competitions, most real world projects involve a lot of work in areas like data cleansing, data visualization, deployment, and more. Collaborating with other data scientists will help you progress faster.
Platforms like Stack overflow and GitHub are some of the most popular sites to receive in-depth solutions to problems or errors you encounter while running or installing your program or debug errors.
Collaboration plays an important role in analyzing, exploring and finding better solutions to various problems. Communicating with other data scientists and experts while sharing ideas is a great way to learn, share your views, and gain insight. You get better ideas and better interactivity by talking to more people, which will come in handy when working with a team on data science projects. It is always a good practice to consider alternatives and various other methods or improvements that you can make to get better results for your solutions.
7. Research and keep learning
Research plays an important role in the development of data science projects. Research will help you understand terminology and gain crucial insights into current trends in data science. It will help you develop your analytical and critical skills. We recommend that you watch videos, read articles and also research publications and books to transform your skills and gain knowledge on different aspects of data science.
The ability to think creatively, analytically, and critically enables data scientists to come up with innovative, out-of-the-box ideas for real-world problems. Most importantly, the essential quality seen in most data scientists is learning and constant improvement. It would be best if you had a constant need for motivation and passion for knowledge in order to be successful in data science and have a long, successful career ahead of you. We hope the guidance we have provided will help you in your quest to learn and develop to become a successful data scientist.