Learning data science can be a challenging, but rewarding process. Here is a recommended curriculum for learning data science:
Start by learning the basics of linear algebra, calculus, and statistics. These concepts form the foundation of many data science algorithms and models.
You will need to be proficient in a programming language such as Python, R, or Scala. These are the most commonly used programming languages in data science and you should choose the one you feel most comfortable with.
Data manipulation and cleaning
You should learn how to manipulate and clean data using tools such as Pandas, Numpy, and Scipy in Python or dplyr and tidyr in R.
Data visualization is a critical aspect of data science and you should learn how to create meaningful and effective visualizations using tools such as Matplotlib, Seaborn, and Plotly in Python or ggplot2 in R.
This is the core of data science and you should learn the basics of supervised and unsupervised learning, decision trees, random forests, linear and logistic regression, and more.
This is a more advanced area of machine learning and involves using neural networks for complex tasks such as image and speech recognition.
Big data and distributed systems
In order to work with big data, you should learn about distributed systems such as Hadoop, Spark, and MapReduce.
You can learn these skills through a combination of online courses, books, and hands-on projects. A good way to start is by taking an online course such as those offered by Anaconda, Coursera, Udemy, or edX. Additionally, you can participate in online data science competitions such as Kaggle to apply your skills in a real-world setting and to build your portfolio.