What is the syllabus of data science course subjects?

As data science is in high demand and data science course subjects are a major need for the learners so that they have a clear understanding about data science. Data Science and its applications which is followed by a deeper dive into topics such as Data Collection, Data Visualization, Data Pre-processing, Machine Learning basic, Python Programming basics, Python Libraries required for Data Science along with Capstone Projects including Building a Movie Recommendation System, Cancer Prediction, etc. Following are some data science course subjects.
1. Statistics:
It is definitely among the most important data science course subjects. It involves maintaining records and important information. Statistics is the process of analyzing historical data, like customer search history.
1.1 Descriptive Statistics:
It helps to understand the data. By quantitative summarization of your data through numerical representations and graphs, data can be properly described. It includes Normal Distribution, Central Tendency, Kurtosis, and Variability.
1.2 Inferential Statistics:
Data is not of any use if you can’t draw a conclusion out of it. Making inferences about the population through a smaller sample can be done through this. The methods it includes are Central Limit Theorem, hypothesis testing, ANOVA and Quantitative Data Analysis.
2. Mathematics for Machine Learning:
It is also an important part of A.I and forms an important basis of data science course subjects. Machine Learning theory is a field that divides Linear Algebra and Calculus. With the right approach through the practical implementation of math, it can be quite fun!
3. Programming Knowledge: A good hold of programming languages
is a must in data science and it forms a basis in data science course subjects. These are the essential ingredients for Data science.
1)Advanced Microsoft Excel:
Excel is very useful to clean data because of its vast set of features.
2)Python:
Python is an interpreter based language as it interprets the Python code . You can check out over the internet for some good learning.
3)R:
R is customized to develop statistical models for analyzing a large amount of data.
4)SQL:
SQL helps to retrieve and manipulate data from the database.
In conclusion, I just want to say that even if you read 100 books, but are not able to apply that theoretical knowledge on numbers then it will not add value. Online courses are available and all you need to do is be self-motivated and inspired by yourself and you will see how you will be able to learn it in a short span of period.
These are some of the important data science course subjects which are required to make a career in data science.

Simply said, data science is an interdisciplinary field of study that uses scientific procedures, methods, systems, and algorithms to draw necessary conclusions and information from both structured and unstructured data. Big Data, ML, and Data Science Modeling are the three primary pillars of the data science curriculum. Statistics, coding, business intelligence, data structures, mathematics, machine learning, & algorithms are some of the key topics covered in the Data Science curriculum. The three main topics covered in the Data Science course syllabus are Big Data, Machine Learning, and Data Science Modeling. The contents span a variety of topics within these three key areas of this popular discipline. The whole Data Science Syllabus is available here.
Introduction to Data Science
Mathematical & Statistical Skills
Machine Learning
Coding
Algorithms used in Machine Learning
Statistical Foundations for Data Science
Data Structures & Algorithms
Scientific Computing
Optimization Techniques
Data Visualization
Matrix Computations
Scholastic Models
Experimentation, Evaluation and Project Deployment Tools
Predictive Analytics and Segmentation using Clustering
Applied Mathematics and Informatics
Exploratory Data Analysis
Business Acumen & Artificial Intelligence