Introduction to DATA ANALYTICS
With Prakash Kandpal
(PL-300, Data Analytics,
CCNA, CCNP, DCA,
ADCHN, CEH, MCA )
Prakash Kandpal
(CCNA, CCNP, CEH, DCA,
ADCHN, MCA, Data Analytics,
PL-300, )
13+ years of Training experience
There are two terms Data & Information. So
What is Data?
What is Information?
Module 1: Data Analytics
DATA
Information
( Raw Facts & Instructions)
( Insights, Outcomes & Conclusions )
Do Remember, Data is your Asset.
Module 1: Data Collection Methods
Collection of Raw facts, through analysis we make predictions, draw
conclusions and decisions.
Overall we have two type of data collection & organisation methods :1. Online Method (On the Web or Apps)
❑ Databases (SQL, Oracle, Azure, AWS, MS-Access etc.)
2. Offline Method (On Standalone Computer System)
❑ Application Software - MS-Excel, MS-Word, Notepad etc.
Module 1: Data Analytics
Data Analytics is the process of examining data
sets in order to find trends and draw conclusions
about the information they contain.
For any organization data could be real-time,
historical, unstructured, qualitative. Data
Analytics helps to identify patterns, trends and
insight generation for an organization.
Data Analytics for Retail Business
Retail Business :Tracking Inventory
Identifying purchase Habits
Detecting end-user trends
Patterns
Recommending Purchase
Determining price optimization
Identifying and stopping frauds
Introduction
Data and information is the most strategic business asset.
Overview of Data Analysis
Data Analysis is telling a story
with data.
Five categories of analytics:
• Descriptive
• Diagnostic
• Predictive
• Prescriptive
• Cognitive
Roles in Data
Business
Analyst
Data
Analyst
Data
Engineer
Data
Scientist
DA-100
DP-203
DP-100
Database
Administrator
DP-300
Tasks of a Data Analyst
Prepare
Model
Visualize
Analyze
Manage
Touring and Using Power BI
What is Data Science ?
Data science is a field that uses statistics, scientific computing, methods, processes,
algorithms and systems to extract and explore knowledge and insights from noisy,
structured, and unstructured data.
CRISP-DM
Cross Industry Standard process for Data Mining
Data science is Basically a analysis of data for the hidden facts and insight so that we can solve a business problem.
Level of this analysis is depend on the type of data, volume of data, category of data
What is Artificial Intelligence ?
AI is the ability of a computer or a robot controlled by a computer to do tasks
that are usually done by humans because they require human intelligence
and discernment.
Data Science
AI is possible by data collection, data cleaning, data analysis that is actually the Data Science
Roadmap to become a
Data Scientist
A Programming Language like Python Core
➢ Numpy
➢ Pandas
➢ Sci-kit Learn
❖ Statistics
❖ Data Visualization
➢ Seaborn
➢ Matplotlib
❖ Machine learning (Algorithms)
➢ Supervised Learning
➢ Unsupervised Learning
❖ Deep learning
❖ Projects
❖
Why Python
• Simple
• Strong in AI
• Open Source
• Multi Paradigm
• General Purpose
• Platform Independent (Portable)
• Interpreted
Career Possibilities
Career Possibilities
Career Possibilities
Tools used for Data Analysis
Data Analysis with MS-Excel
Data Analysis and visualization with Microsoft
Power BI
Data Analysis and visualization with Tableau
Data Analysis with SQL
Data Analysis with Python
Machine Learning
Review Questions
• Q01 – Which data role enables advanced analytics capabilities
through reports and visualizations?
✓ A01 – Data Analyst
• Q02 – Which data analyst task has critical performance impact on
reporting and data analysis?
✓ A02 – Model
• Q03 – What is a key benefit of data analysis?
✓ A03 – Informed business decisions.
Thanks!
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