Updated on Apr, 2024
Language - English
Pushpa Mahapatro
e
Excellent
Data Science made Science
Easy and Quick Data Science Tools
Data & Analytics,Data Science and AI ML
Lectures -36
Duration -2 hours
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Course Description
To enable the students to:
1. To Develop statistical and analytical modelling using data science concepts
2. To develop data visualization
3. To Gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences
Goals
What will you learn in this course:
1. Apply quantitative modeling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.
2. Recognize and analyze ethical issues in business related to intellectual property, data security, integrity, and privacy.
3. Apply ethical practices in everyday business activities and make well-reasoned ethical business and data management decisions.
4. Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
5. Apply principles of Data Science to the analysis of business problems.
6. Use data mining software to solve real-world problems.
7. Employ cutting edge tools and technologies to analyze Big Data.
8. Apply algorithms to build machine intelligence.
9. Demonstrate use of team work, leadership skills, decision making and organization theory
Prerequisites
What are the prerequisites for this course?
None
Curriculum
Check out the detailed breakdown of what’s inside the course
Overview
36 Lectures
-
Overview
01:46
01:46
-
Prerequisites
07:19
07:19
-
Python & Anaconda Installation
04:01
04:01
-
Creating Data Model Using Cassandra
12:51
12:51
-
TEXT Delimited CSV Format - HORUS
11:13
11:13
-
XML to HORUS
01:55
01:55
-
JSON to HORUS
01:57
01:57
-
Database to HORUS
02:00
02:00
-
Audio to HORUS
03:31
03:31
-
Image to HORUS
06:03
06:03
-
Video to HORUS
06:03
06:03
-
Fixer Utilities
04:31
04:31
-
Data Binning or Bucketing
02:37
02:37
-
Averaging Data - Utilities and Auditing
01:57
01:57
-
Outlier Detection - Utilities and Auditing
02:26
02:26
-
Audit Logging - Utilities and Auditing
02:23
02:23
-
Retrieve Attributes - Retrieving Data
02:15
02:15
-
Loading IP_DATA_ALL - Retrieving Data
01:19
01:19
-
Loading Vermeulen PLC - Retrieving Data
01:37
01:37
-
Scheduling Of Jobs - Retrieving Data
00:48
00:48
-
Krennwallner AG - Retrieving Data
02:39
02:39
-
Online Visitor Data - Retrieving Data
00:42
00:42
-
XML Processing - Retrieving Data
01:16
01:16
-
Hillman Ltd Warehouse Rules - Retrieving Data
02:07
02:07
-
Drop Columns _ Any Elements Missing Values - Assessing Data
01:49
01:49
-
Drop Columns _ All Elements Missing Values - Assessing Data
02:58
02:58
-
Drop Rows _ Missing 3 or More Values - Assessing Data
01:28
01:28
-
Network Routing Diagram in R - Assessing Data
01:17
01:17
-
Forecasting - Processing Data
01:58
01:58
-
Linear Regression - Transforming Data
01:17
01:17
-
Power BI Installation - Power BI
02:20
02:20
-
Connecting Excel & Overview - Power BI
03:52
03:52
-
Import Data From OData Feed - Power BI
06:24
06:24
-
Manage Relationship & Generating Reports - Power BI
05:52
05:52
-
Sample Questions Set 1
07:00
07:00
-
Sample Questions Set 2
07:00
07:00
Instructor Details
Pushpa Mahapatro
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