Are you wondering how to start your data science journey? We provide a roadmap for data science in this article. Here we cover the skills, knowledge, and experience necessary to succeed in your data career as a data analyst and data scientist.
Learning data science involves a number of phases, and the particular steps may change depending on your goals. A data science roadmap helps you to know the steps in your path. Here are some fundamental actions that are important in your learning journey.
The Important Steps in Data Science Roadmaps
- Get familiar with basic quantitative skills like mathematical and statistical foundation, including data types, descriptive statistics, regression modelling, data exploration etc.
- Acquire skills in data wrangling, cleaning and formatting to prepare data for analysis including data validation, missing values, outliers etc. Data Analysis with Python – a data analytics course by IBM covers basic data preparation and data analysis using python
- Improve your abilities in data visualisation – Learn how to depict summarised data through charts and which charts to use when. Develop your skill in a data visualisation tool like PowerBI, Tableau. In python, libraries like matplotlib, seaboard, plotly etc. are used for charting and data visualization.
- Learn the foundations of a computer language like Python or R which are still most popular and in-demand in the data science world. Though low-code languages and applications are getting popular these days, having strong logical reasoning and programming skills will set you apart during your job hunt and interviews.
- Get familiar with databases and develop your ability to handle datasets. As a data analyst or data scientist, you will be working with data residing in different systems. So knowing a query language like SQL will be very helpful.
- Build your knowledge in machine learning techniques – Learn supervised and unsupervised machine learning using python such as clustering, classification, and regression algorithms so that you can apply those in your projects. Machine Learning Course by Andrew Ng is a popular and easy to follow course for learning machine learning.
- Work on projects and learn from issues that involve real-world applications of data science and build a project portfolio such as a dashboard on analysing customer data, developing prediction models on automotive sales etc. At this stage, it is important to have a guide who can validate your approaches that you are following and mentor you on your projects along the way.
When you have covered the initial stages of learning and working on your projects, you can also look up Artificial Intelligence projects on GitHub on NLP or image cognition to get an exposure. It is important to follow the given roadmap and gear up for the advanced methods of AI. You may also read about our blog 5 skills needed to become a great data analyst.
Data science is a large field that is always changing, so it is necessary to keep studying and stay up-to-date with the newest methods and tools. While you are travelling following the data science roadmap, attending conferences or workshops, taking part in online forums or communities, or enrolling in data science training programmes or certifications help in staying abreast with the latest developments in tools and technologies in the data science and AI world. Though there are lots of things to learn in this field, it is important to take the right step and start the data journey.