The document provides an overview of data analytics, covering its processes, types, and essential skills needed to become a data analyst. It details how data analytics is used for decision-making and includes information on various analytical methods such as descriptive, diagnostic, predictive, and prescriptive analytics. Additionally, it mentions training opportunities available at the NR Computer Learning Center, including certification programs and hands-on experience.

2. NR Computer Learning Center (NRCLC)
•
•
•
 Hands-on classroom training
 Online Training
 Virtual Live Training
 Private Lesson

4. Agenda
• Introduction to Data Analytics
• Data Analytics Process
• Data Analytics Skills
• Certifications

6. WHAT IS DATA ANALYTICS USED
FOR?

7. PREDICT FUTURE SALES OR PURCHASING
BEHAVIORS

8. BOOST CUSTOMER ACQUISITION AND
RETENTION
• Netflix collects data from its 163 million global subscribers
• what users watch
• when, what device do they use
• whether they pause a show and resume it
• how they rate certain content
• what they search for when looking for something new to watch.
• Based on key trends and patterns within each user’s viewing behavior, the
recommendation algorithm makes personalized suggestions for what the user might like to
watch next.
Uses Recommendation Algorithm to predict other items a
customer may want to watch based on the user’s viewing behavior.

9. HELP AND PROTECT AGAINST FRAUD

10. ANALYZE THE EFFECTIVENESS OF
MARKETING CAMPAIGNS

11. WHAT IS DATA
ANALYTICS
• Data Analytics is analyzing data to draw out
meaningful, actionable insights used to inform and drive
smart business decisions.
• It is a process of finding patterns, evaluating cases and
effects, and making estimations from data by applying a
logic-based approach such as statistical analysis.
• A data analyst examines large datasets to identify
trends and patterns.
• A data analyst uses “visualization” tools to display their
findings through charts, graphs, and dashboards.
• A data analyst shares these visualizations with key
stakeholders to help make informed, data-driven
strategic decisions.

12. TYPICAL PROCESS OF A DATA ANALYST?
DEFINE THE
QUESTION
COLLECT THE
DATA
CLEAN THE
DATA
ANALYZE THE
DATA
INTERPRET
AND SHARE
THE RESULT

13. DATA ANALYTIC PROCESS
Aggregate
Data
Database, Data Mart, Data
Warehouse, ETL Tools,
Integration Tools
Present
Data
Enrich
Data
Inform a
Decision
Reporting Tools,
Dashboards, Static
Reports, Mobile Reporting,
OLAP Cubes
Add Context to Create
Information, Descriptive
Statistics, Benchmarks,
Variance to Plan or LY
Decisions are Fact-based
and Data-driven

14. 1. PURPOSE
• Clearly articulate the problem or research question you
want to address through data analysis.

15. 2.COLLECTING THE DATA
• Import, Store, and export data
• ETL (Extract, transform, and load)
• Data manipulation tools (Advanced Excel, Python, R,
Advanced SQL, XML, JSON)

16. TYPES OF DATA
• Structured Data – Data that is highly organized and follows a predefined
format or schema. This includes data stored in a relational database or
spreadsheet, where data is organized in rows and columns.
• Unstructured – Data that lacks a specific structure or organization. The
data is often free text, images, video, posts, emails, audio, etc.
• Semi-Structured – Data possesses some organizational structure or
metadata but does not adhere to a strict schema. This includes data
containing tags, labels, XML, web data, sensor data,

20. 3. CLEAN THE DATA
Handling NULL, special characters, trimming spaces,
inconsistent formatting, removing duplicates, etc.,
validating data
• Parsing
• Correcting
• Standardizing
• Matching
• Consolidating

21. 3. ORGANIZE DATA
• Organizing Data - Sorting, filtering, slicing,
transposing, appending, truncating
• Aggregating Data – Grouping, merging, summarizing,
etc.

22. 4. ANALYZE THE DATA
• Types of Data Analysis include:
• Descriptive analysis, diagnostic analysis, predictive
analysis, prescriptive analysis …
• Searching, filtering, unique values, aggregate
functions such as Sum, Max, Min, Count, Avg/Mean,
Mode, Median, Std Dev.
• Identifying trends, determining expected values,
interpreting results of predictive modes, p-values, t-
tests, and regression analysis.

23. 7 TYPES OF DATA ANALYTICS
1. Descriptive Analytics - It focuses on summarizing and interpreting historical data to
understand what happened in the past. It involves using basic statistical measures,
data visualization techniques, and exploratory data analysis to describe patterns,
trends, and relationships with the data.
2. Diagnostic Analytics – It focuses on investigating the root cause of why certain events or patterns occurred. It
involves using techniques such as data mining, correlations analysis, and regression analysis to understand
the factors influencing the observed results.
3. Predictive Analytics – It involves using historical data to make predictions or forecasts about future events or
outcomes. It uses statistical modeling and machine learning algorithms to identify patterns and relationships in
the data to make predictions.

24. TYPES OF DATA ANALYTICS (CONT.)
4. Prescriptive Analytics – It involves using optimization techniques, simulation models, and decision-
making algorithms to evaluate different scenarios and suggest optimal actions or decisions to
achieve the desired outcomes. Prescriptive analytics is useful in the area of resource allocation,
supply chain optimization, and strategic decision-making.
5. Text Analytics – It involves extracting information and insights from unstructured textual data, such
as emails, customer comments, social media posts and documents. It uses the techniques such as
Natural Language Processing (NLP), sentiment analysis, topic modeling, and text classification to
analyze and derive meaning from the text.

25. TYPES OF DATA ANALYTICS (CONT.)
6. Spatial Analysis – It focuses on understanding patterns, relationships, and
trends within a geographic or spatial component. It includes techniques such
as geographic information systems (GIS), spatial clustering, spatial
interpolation, and network analysis.
7. Social Network Analysis: It focuses on analyzing the relationships and
interactions between individuals and entities in a social network to uncover
the structure of a network, identify key influencers or communities, and
understand the flow of information or resources within the network. It
includes techniques such as social media analysis, organizational analysis,
and marketing research.

26. 5. INTERPRET AND SHARE THE
RESULT
• Effectively display in the format in tables and charts
• Explain when and why to disaggregate data
• Identify data visualization practices
• Identify visualization Types
• Analysis questions – comparison, time/trend, part-to-
whole, relationship, distribution, correlation graphs, box
and whisker diagram, scatter chart, scatter plot, bar
chart, Sankey diagram, histogram, pie chart, column
chart
• Translate a visual representation of data into words.

27. DATA VISUALIZATION
• Data visualization is using visual
elements such as charts, graphs, maps,
and other visualization to analyze data
find patterns in data, and report
insights gleaned from data.
• Using visualizations makes it easier to
find and show patterns, trends, and
correlations in data.

28. THE NEED TO VISUALIZE DATA
Use a picture. It's worth a thousand words.
-Tess Flanders, 1911
90 percent of the information transmitted to the brain is visual.
-MIT News, January 16, 2014
The human brain processes images 60,000x faster than text.
-Persuasion and the Role of Visual Presentation Support: The UM/3M Study, 1986

29. SKILLS NEEDED TO BECOME A DATA
ANALYTICS
• Problem-Solving skills
• Mathematics & Statistics
• Advanced Excel
• Advanced SQL
• Programming languages such as Python, R, …
• Excellent Communication skills
• Visualization tools such as Microsoft Power BI, Tableau

30. KEY REASONS
TO BECOME A
DATA
ANALYST
High in-demand
Well-paying job
Evolving workplace
environment
Help organization improving
product standards

31. COMPANIES
• Accenture
• Tata Consultancy Services
• Ernst & Young
• HSBC
• Mckinsey
• Bain
• BCG
• Deloitte
• Gartner
• KPMG
• Smart Cube
• Tiger Analytics
• EXL

32. CERTIFICATION
• IT Specialist in Data Analytics by Certiport.
• Topics
• Data Basic – Data types, basic data structure, Qualitative, Quantitative, big
data
• Data Manipulation – import, store, export, clean, organize, and aggregate data
• Data Analysis –Descriptive, diagnostic, predictive, and prescriptive analysis;
using aggregate functions; data mining; identifying trends, interpreting results
• Data Visualization – tables, charts – box and whisker diagram, scatter chart,
scatter plot, bar chart, Sankey diagram, histogram, pie chart, column chart.
• Responsible Analytics Practices – privacy laws and best practices
https://certiport.filecamp.com/s/GESxv0YPqKw5XRw3/fi

33. • Recommendation: The target candidate has approximately 150 hours of instruction and hands-
on experience with the exam topic.
• Online Course material, Practice Tests, and Vouchers can be purchased at Shop.nrclc.com
(Option to purchase online private lessons at a discount)
• NR Computer Learning Center provides AT-Home exam.
• 50 min test.

34. THANK YOU
Dr. Vazi Okhandiar, PMP, MBA, MSCS, BSEE, MCT
info@nrclc.com
(714) 505-3475
NR Computer Learning Center
California, USA