COLLABORATION WITH

IBM: Data Science Methodology

Duration: 2 Weeks
Price: Rs 6499 (+18%GST)

Exam Objective Register Now
IBM: Data Science Methodology
  1. 1. Self Paced ~ 50 Hrs
  2. 2. IBM Catalyst Certificate on successful completion
  3. 3. Unlimited lifetime access to the course

Earning Potential
  1. 1. $101,730 on an average annually.
  2. 2. Prove that you have the DS skills it takes to build a better world. Earning your IBM: Data Science Methodology can supply the foundation you need to build your career.
What will you learn?
  1. 1. Basic and Advanced Python
  2. 2. Concept of Statistics
  3. 3. Conceptualization and working of Machine Learning
  4. 4. Artificial Intelligence and Neural Networks
  5. 6. Capstone Project
Skills Gained
  1. 1. Gain knowledge of common ML workloads and how to implement them.
  2. 2. Python for Data Science, R, Data Visualization, Scala Analytics, Data Privacy, Data Science Tools
  3. 3. IBM: Data Science can be used to prepare for other role-based certifications like ML Scientist

Our Pricing Plan

Rs 6,499

(+18%GST)

  • Self Paced
  • Microsoft Certificate
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Program Lessons

Data Science - Prerequisite

Chapter 1. Introduction Python - Pre - Learning Session
  • 1.1-Python Crash course Introduction
  • 1.2 Python Demo n install
  • 1.3 Python Intro and Installation
  • 1.4 Basic python and datatype
  • 1.5 Basic, Number, string
  • 1.6 Data types
Chapter 2 - Control flow
  • 2.1 If else conditions
  • 2.2 While & for loop conditions
Chapter 3 - Exception Handling
  • 3.1 Exception Handling
Chapter 4 -Functions
Chapter 5 - OOPS
  • 5.1 CLASSES
  • 5.2 OOP
Chapter -6 Deep Learning
  • 6.1 Logistic Regression vs DL
  • 6.2 TesorFlow and Keras
Chapter -7 Libraries
  • 7.1 Introduction to Libraries
  • 7.2 Library Introduction
  • 7.3 Matplolib
  • 7.4 Numpy
  • 7.5 Pandas
Chapter 8 - Mathematics
  • 8.1 Data
  • 8.2 Linear Algebra
  • 8.3 Statistics
  • 8.4 Stats - Probs
Chapter 9. Intro to Probability & DV
  • 9.1 Introduction to Probability, Statistics & SQL
  • 9.2 Data Visualization with Tableau
  • 9.3 LSTM Regression
Chapter 10 - Machine Learning Models
  • 10.1 Clustering
  • 10.2 Evaluation Metrics
  • 10.3 Logistic Regression - Feature Regression
  • 10.4 Logistic Regression
  • 10.5 Simple Linear regression
  • 10.6 Multiple Linear regression

Data Science Methodology- IBM

Module 1: From Problem to Approach
  • Business Understanding
  • Analytic Approach
Module 2: From Requirements to Collection
  • Data Requirements
  • Data Collection
Module 3: From Understanding to Preparation
  • Data Understanding
  • Data Preparation
Module 4: From Modeling to Evaluation
  • Modeling
  • Evaluation
Module 5: From Deployment to Feedback
  • Deployment
  • Feedback

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