The ideal path to securing a job as a data scientist is as follows:
- Getting started
- Data visualization
- Data processing
- Machine Learning and deep learning
- Natural language processing
- Polishing skills
Getting started: Learning any programming language is the best way to start your journey as a data scientist. The most common programming languages are the R and Python programming. Having an idea of what data science is and what type of jobs it entails should be the first priority.
Mathematics: Data science is the study of data. It requires raw data to be stored, segregated and finally interpreted, which requires both mathematics and statistics. Having good command over few of the aspects of statistics can be quite helpful in data science, like:
- Descriptive statistics
- Linear algebra
- Inferential statistics
Libraries: Data science is an advanced level of inventory making. Thus it not only preprocesses the data, but plots it as structured data and then uses AI algorithms on it to create databases. Some of the most popular libraries are:
- Sci-kit learn
Data Visualization: Having the presence of mind to categorize the raw data, finding similarities and being able to simplify the data for easy understanding is to visualize the data. One of the popular forms is graph. There are various libraries you can use to make it easier for you:
Data preprocessing: Data scientists start with a large mass of data that needs to be preprocessed in order to be analysis ready. The preprocessing is done with feature engineering and variable selection. After this it is fed to ML tools for analysis.
Deep learning and ML: Machine Learning and deep learning are the medium through which data is analyzed. The preprocessed data will work only with deep learning algorithms in order to analyze such huge number of data. Both deep learning and ML are mandatory for your job application to be even considered. One should spend a few weeks reading up on CNN, RNN and neural networks.
Natural Language processing: One should have knowledge of NLP as it helps in analyzing text form of data and classifying them as well.
Polishing skills: There is no end to knowledge and competitions are a great way to brush up on your programming skills. Online platforms like Analytics Vidya or Kaggle have opportunities to keep working on your data science concepts. Outside online platforms you can make your own projects and study it.