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7 Productivity Tips for Data Science Beginners

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Data science is an exciting field and continues for rapid development and evolution in technology. Knowing the theoretical principles allows you to analyze data and extract information, innovate and build applications, and produce unique solutions to real-world problems. However, Data Science can be pretty intimidating if you are a beginner in this field. Here are a few tips to improve yourself as an aspiring data scientist.

1. Learn a Programming Language and Get Started with Coding

Programming is considered the heart of data science, and it is an essential skill that every data scientist must-have. Two programming languages that are a good starting point would be SQL and python. Python is a beginner-friendly, simple 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 many online resources. 

Structured query language or SQL is used to manage data used for analysis. SQL helps you create arrangements and maintain the data gathered for analysis. Once you are familiar with these programming languages.

2. Get a Good Understanding of Fundamental Concepts

The humongous field of data science contains theoretical concepts and advanced algorithms. As a beginner, take your time studying and understanding the concepts. It is beneficial to get an excellent practical 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 inbuilt libraries. Practical implementation will help you better understand the overall concepts and improve your programming skills. Even though it might be enticing to use inbuilt libraries and frameworks, implementing them will help you master your basics and assist you in the long run.

3. Learn Maths and Statistics

Mathematics is a fundamental requirement for a data scientist. If programming is considered the heart of data science, Mathematics is the soul. Mathematical concepts like calculus, probability, statistics, and linear algebra are the concepts you require to understand the basic concepts of data science. Mathematics is needed for building predictive machine learning models, understanding probabilistic and deterministic approaches to solve Bayesian problems, understanding backpropagation in deep neural networks, analyzing gradient descent, and so much more. Most of these are concepts covered in your schooling and should not be too much of a hassle to understand. You can also use the online resources available at eMathHelp to practice and check your math work. Data science, essentially, is 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 straightforward language to learn, and using it for data science projects is relatively easy, thanks to the variety of valuable libraries available. Most of these are easy to install and provide efficient and straightforward solutions, enabling data scientists to complete complex tasks with very few lines of code.

Python has a variety of library modules used to build 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 numerical operations, Scipy for scientific processes for dealing with sparse matrices, pandas to view the datasets in an organized and efficient manner. The scikit-learn module in python is used to develop machine learning models through the various algorithms options available in the sklearn library. Also, you can build deep learning models by using frameworks such as TensorFlow and Pytorch. Matplotlib and seaborn are the two best library modules for visualization and performing exploratory data analysis tasks. The algorithms used in Machine learning models work on data and are used for analysis and predictions. These are used in various applications used in the real world for classification, identification, detection, clustering, and so on.

5. Work on Data Science Projects that Solve Real-World Business Problems

When you are well equipped with the basics of programming, mathematics, libraries, and algorithms, the next step would be to find a problem statement and start working on data science projects. To truly understand data science, you need to try and work on many projects. You can start with simple beginner-level projects and then move on to complex ones. Kaggle competitions are also a good starting point for beginners and freshers.

Theoretically, understand the intuition of machine learning and AI concepts and mathematics behind these data science concepts. However, it would be best if you also learned how to implement the projects in real-life scenarios. You should not be afraid to get your hands dirty with some programming and implement these projects yourself. If you have completed some beginner-level projects, you can aim a bit higher for some intermediate-level projects. You must understand your skills and keep working on improving them. Dont give up and persevere until you finish your machine learning or data science projects.

6. Collaborate, Analyze and Explore

“Many ideas grow better when transplanted into another mind than the one where they sprang up,” said Oliver Wendell Holmes. Collaboration among data scientists plays a significant role in career progression and research in data science. Even though you can work alone for competitions, most real-world projects involve a lot of work in areas like data cleaning, data visualization, deployment, and so on. Collaboration with fellow 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 the problems or errors you are encountering with running or installing your program or debugging errors.

Collaboration plays a significant role in analyzing, exploring, and finding better solutions to various problems. Communication with other data scientists and experts while sharing ideas is a great way to learn, share your views, and obtain knowledge. You get better ideas and interactivity by talking to more people, which will be very useful while 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 achieve better results for your solutions.

7. Research and Keep Learning

Research plays an important role in data science project development. Researching will help you understand the terminology and get crucial ideas about the current trends in the field of data science. This will help you develop your analytical and critical skills. We recommend you watch videos, read articles and also research publications and books to transform your skills and gain knowledge of different aspects of data science.

The ability to think creatively, analytically, and critically results in data scientists coming up with out-of-the-box, innovative ideas for real-world problems. But above all, the essential quality seen in most data scientists is learning and improving constantly. It would be best if you had a constant need to drive and passion for knowledge to succeed in the field of data science and to have a long successful career ahead. We hope the tips we provided assist you in your quest for learning and growing into a successful data scientist.