Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

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Mathematics for Data Scientist

Mathematical for Data Scientist

To excel in the field of data science, especially as a data scientist, I would recommend you have good command over the topics mentioned below. There are many YouTube channels that you can use for this purpose. Because this is 10+2 level mathematics, and it is just a matter of revision. So I am not offering any course unless there is a specific need for some group, organization.

Linear Algebra

  • Introduction to Linear Algebra
  • Eigenvalues And Eigenvectors
  • Calculating Eigenvalues and Eigenvectors
  • Eigen decomposition of a Matrix
  • Eigenvectors: What Are They?

Vectors, Matrices & Linear Transformations

  • Vector & Vector Spaces
  • Vectors: The Basics
  • Basis Vector
  • Norm of a vector
  • Identity matrix or operator
  • Determinant of a matrix
  • Column and Null Space
  • Rank of a matrix
  • Transpose of a matrix
  • Inverse of a matrix
  • Least Squares Approximation
  • Linear Transformations
  • Matrices: The Basics
  • Matrix Operations
  • Matrix operations and manipulations
  • Dot product of two vectors
  • Linear independence of vectors

## Multivariable Calculus

  • Critical Points, Maxima and Minima
  • Differentiation
  • Functions and Derivatives
  • Functions: Primer
  • Multivariable Functions
  • Partial Derivatives
  • Taylor Series and Linearization
  • The Hessian
  • The Jacobian
  • Vector-Valued Functions

Probability

  • Introduction to probability – probability, events, additive & multiplicative rule
  • Basics of probability – random variables, probability distribution, expected value
  • Joint and Conditional Probability
  • Probability Rules
  • Bayes’ Theorem

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