We are at a point where more data is generated than ever before in history. These data are extremely useful when analyzed to generate insights to make business decisions. And also, increased computing capabilities that lead to the construction of systems that can analyze all this data in a reasonable time.
1. Math Fundamentals
To get started in data science you need to learn the concepts of linear algebra, calculus, optimization, and functions.
- Part 1 of deeplearningbook.org
- Math track of Khan Academy
- Linear algebra by fast.ai
You must choose some programming language that allows you to interact with data, you must also learn the CS fundamentals.
- Python crash course
- CS50x by Harvard
3. Data Wrangling and Visualization
Understanding Data Wrangling techniques (data collection, cleaning and, exploration) is very important as a data scientist. Also, learn how to create and study the visual representation of the data.
- Python for Data Analysis by Wes McKinney
- Towards Data Science (Medium Publication)
- Hands-On Data Analysis with Pandas
4. Statistics and Probability
Learn descriptive and inferential, associative, and differential statistical concepts and also probability (Bayes’ Theorem is very important)
- Khan Academy
- Intro to Statistics by Udacity
- MIT OpenCourseWare
Most of the companies use relational databases instead of text files to store data. You must bring the data from somewhere.
- SQL for Data Science (Coursera)
- SQL and Database course by freecodecamp
- MongoDB university
6. Machine Learning
First, understand the terminologies around machine learning and its types (supervised and unsupervised learning, dimensionality reduction techniques, time series, etc.)
- Machine Learning by Andrew Ng (Coursera)
- Fast.ai Machine Learning course
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
7. Resources to Practice
Get good hands-on experience by practicing what you learned by doing projects, participate in competitions, meet fellow data scientists and learn from them.
- LinkedIn and GitHub
- Fast.ai forum
8. Big Data
A single machine is not enough when you are working with huge amounts of data. Learn Big Data Fundamentals, Hadoop Ecosystem, MapReduce, Apache Spark, etc. Also, learn how to deploy the models and maintain them.
- Big Data Specialization by UCSanDiego (Coursera)
- Getting started with Apache Spark by James A. Scott
9. Find a Job
The best way to test your skills is to work on a real-life problem. You can get a job, an internship or take a Bootcamp, or work on your own venture where you can do this. Start with an internship and with the time you will get a full-time job as a Data Scientist.
- Follow and engage with the community
- Find jobs in LinkedIn, Angel.co, Kaggle, etc.
- Create a perfect resume before applying to the jobs
10. Advance Concepts
It is time to learn advanced concepts as Deep Learning. Based on your job requirements or your own projects (or your own curiosity), learn the related concepts.
- Natural Language Processing
You must bear in mind that you will never finish learning everything. There are many different libraries, many machine learning algorithms, many resources. There is so much information that it is impossible to keep up to date. You have to make sure you always keep learning a little more. If you do that, learn new things and practice daily, you will never be short of job offers.