Learn Maths for Data science

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About QuestPond: It started 15 years back with the one single goal of creating quality step-by-step IT programming-related lessons.

We saw many lessons online, either they are done too fast or too slow or are too complicated.

The main goal of QuestPond is to create Step by Step lessons on C#, ASP.NET, Design patterns, SQL, and so on. As years moved on I added other step-by-step lessons like Angular, SharePoint, MSBI, Azure and so on.

When you talk about data science the most important thing is Statistical MATHS .

This course teaches statistical maths using simple excel. My firm belief is MATHS is 80% part of data science while programming is 20%. If you start data science directly with python , R and so on , you would be dealing with lot of technology things but not the statistical things.

I recommend start with statistics first using simple excel and the later apply the same using python and R. Below are the topics covered in this course.

Lesson 1 :- What is Data science ?
  • Chapter 1 :- What is Data science and why do we need it ?
  • Chapter 2:- Average , Mode , Min and Max using simple Excel.
  • Chapter 3:- Data science is Multi-disciplinary.
  • Chapter 4:- Two golden rules for maths for data science.

Lesson 2 :- What is Data science ?

  • Chapter 4:- Spread and seeing the same visually.
  • Chapter 5:- Mean,Median,Mode,Max and Min
  • Chapter 6:- Outlier,Quartile & Inter-Quartile
  • Chapter 7:- Range and Spread

Lesson 3 - Standard Deviation, Normal Distribution & Emprical Rule.
  • Chapter 8:- Issues with Range spread calculation
  • Chapter 9:- Standard deviation
  • Chapter 10:- Normal distribution and bell curve understanding
  • Chapter 11:- Examples of Normal distribution
  • Chapter 12:- Plotting bell curve using excel
  • Chapter 13:- 1 , 2 and 3 standard deviation
  • Chapter 14:- 68,95 and 98 emprical rule.
  • Chapter 15:- Understanding distribution of 68,95 and 98 in-depth.
Lesson 4 :- The ZScore calculation
  • Chapter 16:- Probability of getting 50% above and 50% less.
  • Chapter 17:- Probability of getting 20 value.
  • Chapter 18:- Probability of getting 40 to 60.

Lesson 5 - Binomial distribution

  • Chapter 22:- Basics of binomial distribution.
  • Chapter 23:- Calculating existing probability from history.
  • Chapter 24:- Exact vs Range probability.
  • Chapter 25:- Applying binomial distribution in excel.
  • Chapter 26:- Applying Range probability.
  • Chapter 27:- Rules of Binomial distribution.

Do visit to enroll all courses :- https://www.questpond.com/python-r-programming-maths-for-data-science/cid21

Frequently Asked Questions

Are there any course requirements or prerequisites?
1-->No programming knowledge needed. 2-->Basic excel knowledge is added plus point.
Who are your target students?
Who want to learn statistic maths from data science perspective.
What will students learn in your course?
Fundamentals , What and Why of Data science. --> Descriptive statistics Average , Mode , Min and Max using simple Excel. -->Understanding importance of spread and finding spread using range.--> Quartile , Inter-Quartile , outliers, standard deviation , Normal distribution and bell curve .--> Understanding 1,2 and 3 standard deviation and applying 68,95 and 98 empirical rule.-->Finding probability of different scenarios of normal distribution.--> Calculating Z score to find the exact probability. --> Binomial distribution , exact and range probability , applying binomial distribution and rules of binomial distribution.