The Professional Course

Learning outcomes

Project Assistance after course completion

Practical’s included in Training

COURSE DURATION: 50 Hrs

Course Overview:-

Commvault Administrative Training course is inclined towards the professionals that are responsible for day to day administration and management of Commvault Simpana Software.

This course is designed with all aspects of essential concepts, in detail knowledge and best practices for the user security and management, system settings, policy configuration and use, media and library management. The course also explains the in – detail concept of job management including job activity and status monitoring.

Commvault Administrative Training will help candidates to learn the effective and efficient ways of managing the data movement like backup, auxiliary copy and restore within CommCell Group.

Key Benefits:-

This course will derive the students towards gaining the knowledge of all enhanced applications of Data Science, Machine Learning and Artificial Intelligence and help them to get:

• This course will help you to improve your knowledge on data analysis.
• It will improve your decision-making ability with the concept of decision tree.
• To leverage the knowledge of the concepts like Data Visualisation.
• Provide Knowledge of the concepts like Data Mining and Data Collections.
• It will help you to learn and hands on for real time projects and case studies.

This course is best suited for the data science career desired candidates as well as professionals those who are working in related fields.

Course Prerequisite:-

There are no major Prerequisite are designed for this course program. Still having prior knowledge on below mentioned will help you on smooth completion of course with better understanding.

• Well Understanding on Computer Literacy.
• Well Understanding on the scientific discipline concepts.
• Basic knowledge on Programming concepts.

This course is best suited for the candidates and working professionals who are career oriented and intended to learn in depth knowledge on this field. We feel that this course is also best suited for working professionals like managers, Data Administrators, Business Analysts, Network Administrators and IT professionals.

Course Content:-

1. OCERVIEW

• Introduction To Data Science
• Where does this world reside
• Machine Learning and Artificial Intelligence
• Python for Data Science

2. PYTHON 101

• Python Anatomy
• Conditionals
• Loops
• Functions
• Modules and Packages The Jupyter notebook

3. DATA ANALYSIS

• Exploratory Data Analysis
• Quantitative Technique
• Graphical Technique
• Data Analysis Predictions
• Statistical and Non-statistical Analysis
• Descriptive and Inferential Statistics
• Probability Theory
• Conditional Probability

4. MATHEMATICAL COMPUTING WITH PYTHON

• NumPy Overview
• NumPy Arrays
• Basic Operations
• Mathematical functions of NumPy
• Calculus: Maximizing and minimizing algebraic equations
• Matrix manipulation and multiplication
• Matrix transformation

5. SCIENTIFIC COMPUTING WITH SCIPY

• Introduction to SciPy
• SciPy Sub-packages
• Integration and Optimization
• Statistics, Weave and IO

6. DATA WRANGLING

• About Pandas
• Series and DataFrames in Pandas
• Data Operations
• Pandas SQL Operation
• Accessing CSV and JSON Data
• Data cleaning and tranformation

7. MACHINE LEARNING

• Machine Learning Approach
• Supervised Learning
• Unsupervised Learning
• Generalization
• Regularization
• Classification
• Pipelines

8. SUPERVISED AND UNSUPERVISED LEARNING USING SCIKIT LEARN

• Data Classification
• kNN model
• Overfitting and Underfitting
• Linear Regression
• Cross-Validation
• Logistic Regression and the ROC curve
• Data Clustering
• Feature Transformation

9. DATA VISUALIZATION USING MATPLOTLIB

• Introduction to Data Visualization
• Types of plots
• Plotting multiple graphs
• Legends, Annotations and Styles
• Working with 2D Arrays

10. DEEP LEARNING AND NEURAL NETWORKS

• What is Deep Learning?
• Introduction to Neural Networks
• Training Neural Networks
• Multi-class Neural Networks
• Data Dependencies