Learn Maths for Data science

This course teaches Data Science with Maths statistics from basic to advanced level.

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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.