Analytics Jobs
Reviews, Ratings & Feedback

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.

It was most likely one of my relative that drew me to Edureka. I mean, there are very few universities that promise job placement for their candidates. Every institute brags about the amount of job referrals or hiring partners. Edureka, in my opinion, is the only institute that offers a 100% money-back guarantee if you are not placed in any organization after completing the course.

When I filled in for my admission I was not looking for a money back but this made me secure about investing in them as this was said by one of the relatives, they are not cool but he is my relative and I cannot do anything but listen, and am happy that I listened. Sometime relatives are also nice.

Aside from that, non-programmers are provided a unique free lesson. It looks fascinating. They offer domain specialisation, which improves your chances of being hired at your ideal organisation.