data science and cloud
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Data Science and Cloud

Data Science and Cloud

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  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing
  • Types of problems and business
  • Objectives in various industries
  • How leading companies are harnessing the power of analytics?
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem solving framework List of steps in Analytics projects Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project
  • Why Python for data science? Module 2
  • Overview of Python- Starting with Python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
  • Understand Jupyter notebook & Customize Settings Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Installing & loading Packages & Name Spaces Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions Variable & Value Labels – Date & Time Values
  • Basic Operations - Mathematical - string - date
  • Reading and writing data
  • Simple plotting
  • Control flow & conditional statements
  • Debugging & Code profiling How to create class and modules and
  • how to call them? Module 3
  • NumPy Basics: Arrays and Vectorized Computation
  • The NumPy ndarray: A Multidimensional Array Object
  • Creating ndarrays
  • Data Types for ndarrays
  • Arithmetic with NumPy Arrays
  • Basic Indexing and Slicing
  • Boolean Indexing
  • Fancy Indexing
  • Transposing Arrays and Swapping Axes
  • Universal Functions: Fast Element-Wise
  • Array Functions Array-Oriented Programming with Arrays
  • Expressing Conditional Logic as Array Operations
  • Mathematical and Statistical Methods
  • Methods for Boolean Arrays
  • Sorting
  • Unique and Other Set Logic
  • File Input and Output with Arrays
  • Linear Algebra
  • Pseudorandom Number Generation
  • Example: Random Walks
  • Simulating Many Random Walks at Once Module 4
  • Getting Started with pandas
  • Introduction to pandas
  • Data Structures
  • Series
  • DataFrame
  • Index Objects
  • Essential Functionality
  • Reindexing
  • Dropping Entries from an Axix
  • Indexing,Selection, and Filtering
  • Integer Indexes
  • Arithmetic and Data Alignment
  • Function Application and Mapping
  • Sorting and Ranking
  • Axis Indexes with Duplicate Labels
  • Summarizing and Computing Descriptive Statistics
  • Correlation and Covariance
  • Unique Values, Value Counts, and Membership Module 5
  • Python for Data Visualization-Matplotlib
  • Introduction to Matplotlib Matplotlib Exercises Overview
  • Matplotlib Exercises – Solutions
  • Python for Data Visualization-Seaborn
  • Introduction to Seaborn
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Grids
  • Style and Color
  • Seaborn Exercise Overview Seaborn Exercise Solutions Module 6
  • Introduction to Cloud Computing
  • Registration with AWS
  • Virtual Instance Module 7
  • S3 Basics
  • Databases
  • Athena Module 8
  • AWS Data Exchange
  • Module 9
  • AWS Data Pipeline Module 10
  • Project

Description

Python programming language is powerful open source language. It is used with AI & Data Science tools that are used to simplify and easily access the data and store the data easily. By python programming language we can easily manipulate the data, also it can help in the analysis of data, we can create the wonderful visualization and helps to access the high-quality content.

Expectations and Goals

Interns can analyze data

Interns can upload data into AWS cloud

Prerequisites

Anybody interested in Data Science with Cloud Computing can take this training.

Will be updated soon
Industrial
Professional

Course Modules

  • Industrial


    1. Duration - 40 Hours (4 to 6 Weeks)
    2. Daily / Weekly Classes
    3. 2 Case Studies & 1 Live Project
    4. Industrial Courses are only for students. You can avail this by producing valid proof of studentship. Work on assignments, case studies, and projects.
    5. WHEN WILL YOUR COURSE/TRAINING/INTERNSHIP START AFTER ADMISSION?

      As per our policy, we start the course/training/internship within 10 days from the date of enrollment. If you enroll for future month/date, our schedule team will coordinate with you and assign your class. To communicate with our schedule team for preferred timing, email to training@ardentcollaborations.com. All courses/internships can be scheduled in customised manner as per your requirements.
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  • Professional


    1. Duration - 60 Hours (8 to 24 Weeks)
    2. Daily / Weekly Classes
    3. 4 Case Studies & 1 Live Project
    4. Professional Courses are more comprehensive. The learning is case study based and project oriented. Ideal for passout students and working professionals.
    5. WHEN WILL YOUR COURSE/TRAINING/INTERNSHIP START AFTER ADMISSION?

      As per our policy, we start the course/training/internship within 10 days from the date of enrollment. If you enroll for future month/date, our schedule team will coordinate with you and assign your class. To communicate with our schedule team for preferred timing, email to training@ardentcollaborations.com. All courses/internships can be scheduled in customised manner as per your requirements.
    6. Download Content
    7. SUMMER OFFER - SAVE 54%

      35000 16100

Reviews

Ruchika Sinha says
"I found this meeting really helpful. One of the most enjoyable and informative seminars I have ever attended. Thank you for organizing and a very special thanks to the great speaker"
Soumyadeep Garai says
"Awesome"