If you are on this page, chances are that you are thinking of becoming a data scientist. Without any doubt, data science has been rising in popularity across all of the industries out there. This is one of the main reasons the demand for data science professionals has been on the rise since the past two decades. Therefore, more and more people are joining this world to secure their future and make a lot of money. In this article, we will find out if you can learn data science in 2 months. Read on to find out more.
The short answer is, you can only get basic familiarity with data science and just 2 months. Another words, these two months will teach you what data science is about, how professionals use it in different industries, and how they generate insights from large scale data.
As a general rule, you need to spend at least 6 months to assume the role of a data scientist. In just 30 days, you cannot do more than that getting a basic understanding of this world. After completing the course in 6 months, you will be able to create your resume and look for a job in just one month.
Duration of the courses
Different data science courses come with different time duration for learning. To give you an idea of how much time you need to spend to become a data science professional, we have discussed the duration of some common data science courses that you need to take to improve your skills and gain experience in the relevant field.
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The good news is that some of these courses are available online. Therefore, you can take this courses and improve your skills and experience from the comfort of your home. What you need to do is choose a good course and a reputable Institute. Your mentors should have plenty of experience and strong grip on the courses you want to take.
Now let’s talk about a few modules that can help you learn data science. We will also discuss the duration of each course so you can get a pretty good idea of how much time you need to dedicate to pass the course.
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Maths
Without learning mathematics, you cannot get started in the field of data science. This is one of the primary reasons mathematics is taught in schools and colleges. If you are an engineering student, you must be familiar with mathematics.
In the field of data science, data scientist use machine learning and other programming language. For machine learning, data scientists have to gain experience with statistics, probability, linear algebra, and calculus. The good news is that even if you have no experience in this field, you can get some hands on experience in just one month. Learning the basics of machine learning is essential to become a data scientist.
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Programming knowledge
The implementation of data science requires knowledge of different programming language, such as python, java, C++ and R, to name a few. But the good news is that Python is an easy to understand programming language. Python is versatile and comes with the huge library.
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If you want to learn python, you need to spend at least two months to get the hang of the basics. And then you will need to spend another two months to learn to use the libraries, suchas NumPy, Seaborn, XGBoost, and Keras, to name a few.
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Machine Learning
In data science, machine learning has significant importance. Therefore, you may want to spend as much time as you can to improve your machine learning skills. Generally, machine learning involves reinforcement learning, supervisor learning, and unsupervised learning.
You need to spend at least 1.5 months to learn the basics of machine learning. And then you need to spend another month to handle different practice projects as well as case studies. The good news is that you can use Kaggle as a source of data.
Deep Learning
In case you don’t know, Machine Learning includes deep learning as well. As a matter of fact, Deep Learning is essential for Artificial Intelligence applications. A machine learning computer system uses neural network architecture to learn from large scale data. The good news is that deep learning is an interesting subject. Therefore, you may want to spend at least 20 days on this project.
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Natural Language Processing (NLP)
In case you don’t know, natural language processing is abbreviated to NLP. It is a combination of artificial intelligence, human language, and computer science. Natural language processing allow you to perform topic modelling, information retrieval systems, distance algorithms, text pre-processing, and text analysis, just to name of you.
If you want to understand natural language processing, you may want to spend at least 15 days. During this time, you will be able to get the hang of the basics of natural language processing.
Computer vision
As far as computer vision is concerned, it refers to the processing of images and photographs. In this field, computer systems are used to analyze images and gain desired insights. For this purpose, data scientist use machine learning models that are trained and deployed on Google cloud platform. You need to spend at least 7 days to learn CV and GCP.
Focus on projects.
We suggest that you incorporate relevant keywords in your resume. Apart from this, you may want to use your time sparingly and focus on the most important aspects of data science learning.
Data Science Placement Success Story
Long story short, you can learn some aspects of data science in 2 months. However it is not possible to become an expert in this field in just one or two months. For this purpose, you may have to spend at least 6 months to a year.
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