Data science helps you in analysing the data .uses techniques and theories like mathematics, statistics, domain knowledge and information science.

- Class:1- Getting started with Mathematics.

Class:2- Basics of Multivariable Calculus.

Class:3- Understand the Function of variables, Derivatives, and gradients.

Class:4 - Tutorial on Step function, Logit function , Sigmoid function, ReLU (Rectified Linear Unit) function.

Class:5 - Session on Cost function and Plotting of functions

Class: 6 - Understand the Minimum and Maximum values of a function.

Class:7 - Fundamentals of Linear Algebra.

Class: 8 - Vectors and Matrices tutorials.

Class:9 - Concept of Transpose of a matrix and Concept Inverse of a matrix.

Class: 10 - Finding the determination in the matrix?

Class: 11 - Term “Dot product”

Class: 12 - Tutorial on Eigenvectors and Eigenvalues.

Class: 13 - Session on Objective function/Cost function.

Class: 14 - Overview of the Likelihood function.

Class: 15 - Overview of the error function.

Class: 16 - Understand the Gradient Descent Algorithm.

Class: 17 - The basic idea behind Programming.

Class: 18 - Basics of r syntax.

Class: 19 - Learn about different R programming concepts - Data types, vectors arithmetic, indexing, and data frames.

Class: 20 - Advance tutorial on R Operators.

Class:21 - Introduction to R studio.

Class:22 - Concept of object-oriented programming (OOPS)

Class:23 - Session on Jupyter notebook.

Class:24 - Different types of Python libraries.

Class:25 - Data basics in Data science.

Class:26 - Tutorial on Manipulating data (Using different data format)

Class: 27 - Session on Data - Part 1(Clean data, Impute data, and Scale data)

Class: 28 -Session on Data - Part 2 (Import and Export data, and Scrap data)

Class: 29 - Tutorial on pandas, NumPy, pdf tools, stringr.

Class: 30 - Advance Concepts in Data.

Class:31- Basics of Statistics and Probability.

Class:32- Explanation about Mean, Median, and Mode.

Class:33 - Tutorial on Standard deviation.

Class:34 - Session on Correlation and the covariance.

Class:35 - What are Probability distributions and p-value.

Class:36 - Introduction to Baye’s Theorem.

Class:37 - A/B Testing technique.

Class: 38 - Fundamentals of Data Visualization.

Class: 39 -Learn about some Components in data visualization such as Data Component, Geometric Component, Mapping Component - Part 1

Class: 40 - Learn other Components in data visualization - Scale Component, Labels Component, and Ethical Component.

Class: 41 -Basics of Linear Regression.

Class: 42 - Fundamentals of Machine Learning.

Class: 43 - Continuous Variable Prediction tutorial.

Class: 44 - Discrete Variable Prediction tutorial.

Class: 45 - Session on k means clustering algorithm in Python.

Class: 46 - Machine Learning tools in Python.

Class: 47 - Overview of Time Series Analysis.

Class: 48 - Introduction to Exponential Smoothing method. (Technique)

Class: 49 - A complete view of “ARIMA” (Technique)

Class:50 - A complete view of “GARCH” (Technique)

Class:51 - Gain Knowledge about Productivity Tools.

Class:52 - Learn about important Productivity Tools.

Class:53 - How to plan the data science project at a basic level.

Class:54 - Training on a different model in Data Science.

Class:55 - Advance training in Planning the Project.

Class:56 - Session:1 - Some important tools you need to learn.

Class:57 - Session:2 - Some important tools you need to learn.

Class: 58 -How to upskill your knowledge in Data Science.

Class: 59 - Cv preparation and Job-Orientation.

Class: 60 - Latest updates in Data Science.

- The duration of the Course is 2 Months (60 days)

Weekdays - Monday to Saturday.

Weekend classes - Saturday and Sunday.

Class Consist of both Theory and Practical session.

Materials will be provided.

Assignments will be given at regular intervals.

Please note that the Training Structure may vary for each and every student.