How do I learn data science by myself?
10 steps to become a pro data scientist:
1. Develop Skills in Algebra, Statistics, and ML
2. Learn to Love (Big) Data
3. Gain a Thorough Knowledge of Databases
4. Learn to Code
5. Master Data Munging, Visualization, and Reporting
6. Work on Real Projects
7. Look for Knowledge Everywhere
8. Communication Skills
9. Compete
10. Stay Up to Date with the Data Scientist Community
Understand Data: Data is useless and can (and should) be misleading without the context. Data needs a story to tell a story. Data is like a colour that needs a surface to even prove its existence, as colour red for example, can’t prove its existence without a surface, we see a red car, or red scarf, red tie, red shoes or red something, similarly data needs to be associated with its surroundings, context, methods, ways and the whole life cycle where it is born, generated, used, modified, executed and terminated.
I have yet to find a “data scientist” who can talk to me about the “data” without mentioning technologies like Hadoop, NoSQL, Tableau or other sophisticated vendors and buzzwords. You need to have an intimate relationship with your data; you need to know it inside out. Asking someone else about anomalies in “your” data is equal to asking your wife how she gets pregnant. One of the distinct edge we had for our relationship with the UN and the software to secure schools form bombings is our command over the underlying data, while the world talks about it using statistical charts and figures, we are the ones back home who experience it, live it in our daily lives, the importance, details, and the appreciation of this data that we have cannot be find anywhere else. We are doing the same with our other projects and clients.
Understand Data Scientist: Unfortunately, one of the most confused and misused words in data sciences filed is the “data scientist” itself. Someone relate it to a mystic oracle who would know everything under the sun, while others would reduce it down to statistical expert, for few its someone familiar with Hadoop and NoSQL, and for others it is someone who can perform A/B testing and can use so much mathematics and statistical terms that would be hard to understand in executive meetings. For some, it is visualization dashboards and for others, it’s a never-ending ETL process.
For me, a Data Scientist is someone who understands less about the science than the ones who create it and little less about the data than the ones who generate it but exactly knows how these two works together. A good data scientist is the one who knows what is available “outside the box” and who he needs to connect with, hire, or the technologies he needs to deploy to get the job done, one who can link business objectives with data marts, and who can simply connect the dots from business gains to human behaviors and from data generation to dollars spent
For someone who is considering self-learning, there would be a couple of learning pathways, you may follow the one below or develop your own schedule.
But a psychological prerequisite that is important is having a genuine interest to look at data, understand data and work with it.
From a knowledge perspective, you would need to start learning to programme, in case of no prior experience.
In the First Step: Learn Programming (R or Python), become proficient.
I did this FREE- E-Book Latest E-Book on R Programming Language for Beginners
Second Step: Gain the knowledge up to Intermediate Statistics, re-learn College Algebra, Linear Algebra, understand theoretical foundations, logic & applications of Machine Learning algorithms
Third Step: Work with independent projects, you can get the data sets from platforms like Kaggle. Try to implement your learning in a step by step fashion while solving the objectives of these projects.
my suggestion is a beginner level Ebook
Premium Ebook — Python with Data Science Handbook
Introduction to R for Data Science | Data Science Tutorial
but you will find the best path once you begin learning by doing