Math in Data Science

Home / Math in Data Science
0 student
Course Fee

₹ 15,000 ₹ 7,000

Course Insides

Level of Course

Beginners to Advance

Duration

2 Months

Classes Mode

ClassRoom|Online

Classes

Weekend(Sat & Sun)

Live Project

3 Lives Project

Exercises

12 Exercises

About the course

Mathematics & Statistics are the founding steps for data science and machine learning. Most of the successful data scientists I know of, come from one of these areas – computer science, applied mathematics & statistics or economics. If you wish to excel in data science, you must have a good understanding of basic algebra and statistics.

However, learning Maths for people not having background in mathematics can be intimidating. First, you have to identify what to study and what not. The list can include Linear Algebra, calculus, probability, statistics, discrete mathematics, regression, optimization and many more topics. What do you do? How deep to you want to get in each of these topics? It is very difficult to navigate through this by yourself.

Who can take up the course?

Data Analyst

Data Scientist

Business Analyst

 

Benefits

  • The course is absolutely practical and real-time based on theory material provided in advance.
  • The sessions are interactive and interesting
  • All the queries are answered.
  • Easily access raw data files & data in from an external database. Read and write almost any data format!
  • Manage data using tools for data entry, editing retrieval, formatting & conversion
  • Analyze data using descriptive, statistics, multivariate techniques, forecasting, modeling, linear programming
  • Advanced analytics helps you to make changes and improvements in business practices.
  • Report formation with perfect graphs
  • Operations research and project Management
  • Data updating and modification
  • Powerful data handling language
  • Excellent data cleansing functions
  • Interact with multiple host systems

Modules

  • Introduction to Probability
  • The Basic Probability Formula
  • Computing Expected Values
  • Frequency
  • Events and Their Complements
  • Fundamentals of Combinatorics
  • Permutations and How to Use Them
  • Simple Operations with Factorials
  • Solving Variations with Repetition
  • Solving Combinations
  • Symmetry of Combinations
  • Solving Combinations with Separate Sample Spaces
  • Combinatorics in Real-Life: The Lottery
  • A Practical Example of Combinatorics
  • Practice, Questions and exercise
  • Sets and Events
  • Ways Sets Can Interact
  • Intersection of Sets
  • Union of Sets
  • Mutually Exclusive Sets
  • Dependence and Independence of Sets
  • The Conditional Probability Formula
  • The Law of Total Probability
  • The Additive Rule
  • The Multiplication Law
  • Bayes’ Law
  • Bayes’ theorem
  • A Practical Example of Bayesian Inference
  • Practice, Questions and exercise
  • Fundamentals of Probability Distributions
  • Types of Probability Distributions
  • Characteristics of Discrete Distributions
  • Discrete Distributions: The Uniform Distribution
  • Discrete Distributions: The Bernoulli Distribution
  • Discrete Distributions: The Poisson Distribution
  • Characteristics of Continuous Distributions
  • Continuous Distributions: The Normal Distribution
  • Continuous Distributions: The Standard Normal Distribution
  • Continuous Distributions: The Students’ T Distribution
  • Continuous Distributions: The Chi-Squared Distribution
  • Continuous Distributions: The Exponential Distribution
  • Continuous Distributions: The Logistic Distribution
  • A Practical Example of Probability Distributions
  • Practice, Questions and exercise
  • Population and Sample
  • Types of Data
  • Levels of Measurement
  • Mean, median and mode
  • Skewness
  • Variance
  • Standard Deviation and Coefficient of Variation
  • Covariance
  • Correlation Coefficient
  • Introduction to Inferential Statistics
  • What is a Distribution
  • The Normal Distribution
  • The Standard Normal Distribution
  • Central Limit Theorem
  • Standard error
  • Estimators and Estimates
  • What are Confidence Intervals
  • Confidence Intervals; Population Variance Known; Z-score
  • Confidence Intervals; Population Variance Unknown; T-score
  • Margin of Error
  • Confidence intervals. Two means. Dependent samples
  • Introduction to Hypothesis
  • Null vs Alternative Hypothesis
  • Type I Error and Type II Error
  • Test for the Mean. Population Variance Known
  • p-value
  • Test for the Mean. Population Variance Unknown
  • Test for the Mean. Dependent Samples
  • Test for the mean. Independent Samples
  • Chi Square
  • ANOVA
  • Practice, Questions and exercise
  • What is linear algebra?
  • Vectors Operaions
  • Tensor
  • Overview of Matrices
  • Matrix Rank
  • Matrix Determinant
  • Matrix Inverse
  • Projections and Orthogonalization
  • Eigendecomposition
  • Singular value decomposition
  • Quadratic form and definiteness(PCA)
  • Practice, Questions and exercise
  • Foundation of Calculas-Functions
  • Limit and Continuity
  • Multivariate Calculus
  • Derivatives and gradients
  • Gradient Descent
  • Practice, Questions and exercise
Curriculum is empty

Instructor

User Avatar Mukesh Sharma

0.00 average based on 0 ratings

5 Star
0%
4 Star
0%
3 Star
0%
2 Star
0%
1 Star
0%
₹ 15,000 ₹ 7,000
NOTE: You have to pass these courses before you can enroll this course.