Complete Road Map for Data Science & ML for Begineers

Data Science involves: Statistics, Excel, Linear Algebra, Power BI, Machine Learning, SQL

What you'll learn
- Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems. Mining data from primary and secondary
- Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
- 1. Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML
- 5 Different Practical Data Science projects with ipython Notebooks

Requirements
- There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. etc. but I never learnt it, yet I am able to work on machine learning.

Description
Why Data Science?(Decide the Goal First?)

So before jumping into the complete Roadmap of Data Science one should have a clear goal in his/her mind that why he/she wants to learn Data Science? Is it for the phrase “The Sexiest Job of the 21st Century“? Is it for your college academic projects? or is it for your long-term career? or do you want to switch your career to the data scientist world? So first make a clear goal.Why do you want to learn Data Science?For example, if you want to learn Data Science for your college Academic projects then it’s enough to just learn the beginner things in Data Science. Similarly, if you want to build your long-term career then you should learn professional or advanced things also. You have to cover all the prerequisite things in detail. So it’s on your hand and it’s your decision why you want to learn Data Science.

How to Learn Data Science?

Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas.

Domain Knowledge

Math Skills

Computer Science

Communication Skill

Domain Knowledge

Most people thinking that domain knowledge is not important in data science, but it is very important. Let’s take an example: If you want to be a data scientist in the banking sector, and you have much more information about the banking sector like stock trading, know about finance, etc. so this is going to be very beneficial for you and the bank itself will give more preference to these type of applicants more than a normal applicant.

Math Skills

Linear Algebra, Multivariable Calculus& Optimization Technique,these three things are very important as they help us in understanding various machine learning algorithms that play an important role in Data Science. Similarly, understandingStatisticsis very significant as this is a part of Data analysis.Probabilityis also significant to statistics and it is considered a prerequisite for mastering machine learning.

Who this course is for:
- Beginner into Machine Learning
- Beginner into Python
- Non CS Students
- Career transition from Non Technical into Data Science
- Fresher to get job into Machine Learning Engineer
More Info