Data science is one of the best career options that doesn’t require any higher-level degrees or qualifications. Well, it’s good to have masters in any field related to business or analytics but trust me it’s not required. So how do you learn data science? Well, every career has two major stages; theoretical knowledge and practical skills, so like other careers, data science also needs theoretical knowledge and practical skills. So in this article, I will present a complete roadmap of how to learn data science step by step for you.
How To Learn Data Science Step By Step?
So, as mentioned above, data science also has two major stages which are theoretical concepts and practical skills. You can’t escape any of them if you want to become a data science expert. The theoretical knowledge needed in data science is used to analyze the data and make decisions based on the tools we use to work with the data, which contributes to the practical skills needed for data science.
Also, Read – 200+ Machine Learning Projects Solved and Explained.
Theoretical Concepts Needed for Data Science:
So let’s start by looking at the theoretical concepts you need to learn step by step for data science:
- Linear Algebra
- Statistics and Probability
- Mathematics and Machine Learning Algorithms
- Neural Networks
These topics above look like a handful of topics, but they have a lot of concepts that will help you analyze and make decisions by looking at the data.
Practical Skills Needed for Data Science:
Now while learning the above topics you should learn all the practical tools needed to learn data science so that when you are done with the theoretical concepts you will have practical skills to work on data science projects. So let’s take a look at the tools you need to learn the practical data science skills.
Excel: Excel is a spreadsheet by Microsoft. The life cycle of data starts with excel and ends in excel only. What happens is that a Data Scientist process the data from an excel file and a Business Analyst do report using excel sheets. So it is an important skill for learning data science.
Tableau: Tableau is a Data Visualization software. It is mostly used by Business Analysts, Data Analysts, and Data Scientists. It is also an important skill to have. So before creating visualizations by using a programming language it is always good to understand the data by visualizing it using a software like Tableau.
Python: Python is the most used programming language for Data Science. For data science, you must be good in a programming language, so as Python is the most used programming language in the industries so I will also recommend you to learn Python only.
Jupyter Notebook: The Jupyter Notebooks are open source web application based programming tool that allows you to create and share your codes, visualizations, and output with your team members or with the complete data science community. In the future, you will be using a Jupyter notebook for showing your Data Science projects.
NumPy and Pandas: From NumPy and Pandas you will start implementing what you learnt in the theoretical concepts needed for Data Science. NumPy means Numerical Python, so we use this library for creating mathematical equations and Pandas for working with data.
Matplotlib and Seaborn: The matplotlib and seaborn are the most basic and easy to learn Python libraries for data visualization. So it will be acceptable if you don’t know any data visualization library except Matplotlib and seaborn.
Scikit-Learn: So we use Python for Data Science mainly because it has good support of libraries and frameworks. One of the most important libraries for implementing machine learning algorithms is Scikit-Learn. All the machine learning algorithms that you will study in the theoretical part can be implemented by using the Scikit-Learn library.
TensorFlow: TensorFlow is mainly used for Deep Learning. The time you invest in learning Scikit-Learn, the same time you have to invest in learning TensorFlow. It is used to train the neural networks that we use while processing exceptionally large data with a large number of important features.
PyTorch: PyTorch is an alternative for TensorFlow. Earlier only TensorFlow was used for training Neural networks but now PyTorch is considered equally valuable.
So these were the most important theoretical concepts and the practical skills that contribute the most in a Data Science roadmap. But while working on projects you will need to learn some more libraries for a specific task. So for that, you have to keep researching and exploring irrespective of you have got a job in data science or not you just need to keep exploring to keep upgrading your data science skills.
I hope you liked this article on how to learn data science step by step. Feel free to ask your valuable questions in the comments section below.