How to Learn Data Science Effectively in 2021?

01-Feb-2021

Technology has created a revolution in just about every sector and every industry it has entered into. As the world progresses into the information age, every sphere of activity is becoming dependent on technology. More and more fields are becoming digitalized and conducting their affairs through technology. The effect of becoming reliant on technology is that all the business processes start producing a lot of electronic data. This electronic data is the main byproduct of technology.

But this electronic data is not just a byproduct. It is one of the most valuable assets a business or an organization can possess. It is just waiting to be utilized. Businesses and organizations have realized this and have therefore started to seek out skilled and competent data scientists. They do this because they know that only data scientists can help them convert the mounds of electronic data which they generate on a daily basis into a usable format. This is also a big reason for why so many individuals today are seeking to learn data science by enrolling in a Post Graduate Program in Data Science course. In this article, we will examine some ways in which one can learn data science effectively.

How should one go about learning data science?

1. One should have an innate interest in data -

Learning data science is not an easy affair. Proceeding down this path can become daunting very soon. When one sees enough formulas, starts dreading big numbers, and becomes absolutely confused by neural networks, it can become very difficult to keep moving forward on this path. We suggest that one start taking an active interest in data science. In the beginning one will have to push themselves to study data science and learn the core concepts of data science. But after some time, one will get a certain level of competence in the field of data science. One can reach this stage faster by taking a Post Graduate Program in Data Science course.

Once they reach that level, it will become much easier to study data science because the phase of struggling with concepts will have passed. Also, one should start doing something which lets them have a real taste for data science. This can be done in many ways. One can, for instance, try to make a software application which predicts the stock market’s performance. By attempting this personal project, one will be able to get lots of practice in data science and will grow more and more comfortable with it. Eventually, one will start loving it.

2. One should try to get experience by doing -

When one starts out in the field of data science, they usually have many misconceptions. The first one is that data science is all about machine learning. While machine learning is an integral part of data science, it is not the be all and end all of data science. There are many other fields in Data Science such as image recognition and neural networks. These fields are covered in any data science certification training.

Also, when one starts working in the field, they will realize that most of a data scientist’s job doesn’t consist of applying complex and advanced algorithms to massive data sets. It consists of cleaning data 90% of the time. Also, he will discover that the algorithms used don’t vary too much between projects. From one project to the next, a data scientist usually has to use the same algorithms which are considered best practice by others in the field. He will only have to modify and fine-tune the algorithms so that it is suitable for the data set he is working with.

He will only learn about these things once he starts actually working on personal projects which involve extensive use of data science techniques. This is why we recommend that one learn data science by doing.

3. Master all the popular and gold standard data science libraries -

Some popular libraries from the Python programming language, the most popular language for data science, are -

• Scikit-learn for dealing with basic ML algorithms like classification, linear and logistic regressions, clustering etc.
• Pandas for dealing with high-level data structures and carrying out analysis. It allows gathering data from external sources like Excel and merging and filtering data.
• Keras helps with deep learning. It allows prototyping and fast calculations by making use of the GPU.
• TensorFlow helps to enter the field of deep learning. It can set up, train, and utilize artificial neural networks with the help of massive datasets.
• Matplotlib for creating 2D plots, histograms, charts, and other forms of visualization.
• Matplotlib for creating charts, histograms, 2D plots, and other kinds of visual mapping of data.
• NLTK for carrying out processing, natural language recognition, and computational linguistics.
• Scikit-image helps with processing of images.
• PyBrain for reinforcement learning, unsupervised learning, and neural networks
• Caffe for deep learning that allows switching between the CPU and the GPU and processing 60+ mln images a day using a single NVIDIA K40 GPU.
• StatsModels for statistical algorithms and data exploration.

4. Learn the fine art of communicating meaningful insights -

A data scientist has to interact with people who are not necessarily from a technical background quite often. So he has to learn how to communicate the insights he gleans from data sets to non-technical people in simple terms so that they can understand him. A data science certification course teaches this method of communication.

Related Blog Posts:

1. Best Way to Learn Data Science Effectively
2. Data Science: A Thriving Field in 2022 With a Promising Future
3. A Comprehensive Overview of Data Science Courses
4. Top 10 Books on Artificial Intelligence for Beginners
5. Are Data Scientists in Demand?

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