This is a training slide on Data Science and Analytics presented by Tobe M. (The founder of Datatechcon and an expert Data scientist).

1. INTRODUCTION TO DATA SCIENCE &
ANALYTICS
Learn how to leverage data & analytics to help increase business value.
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2. 5
Progress
6
Future Plans
3
Timeline
4
Our Team
INTRODUCTION
1
About Us
2
Our Services
• Fortune 200 Data Scientist
• Founder of Data Techcon
• 10+ years experience in tech
• MIT certified analytics expert
• Data Subject Matter Expert at compTIA
• Trained more than 500 students.
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3. DATA SCIENCE & ANALYTICS TERMS
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01. Data Science is an inter-
disciplinary field that uses
scientific methods & algorithms
to extract insights from data. It
is an umbrella term for a
group of fields like DA,
ML……
02. Data: is any pieces of
information represented in the
form of text, image, numbers,
sounds etc.
03. Data Analytics is a sub
field in data science that
focuses on utilizing data to
draw meaningful insights and
solving problem
04. Data Analysis: is a process
in data analytics workflow. It is
the application of statistics to
derive a summary of data.
05. Business Intelligence:
combines business analytics,
data mining, data visualization,
data tools and infrastructure,
and best practices to help
organizations to make more
data-driven decisions.

4. DATA SCIENCE
& ANALYTICS
TERMS
• 06. Machine Learning: is a sub field of AI and
DS. The ability of machines to produce outcomes. It's
all about implementing algorithms that lets machine
receives data and uses the data to make prediction
and identify patterns and give recommendation. ML
cannot be implemented without data. Demand for
real-time dashboards open opportunities for ML.
• 07. Artificial Intelligence: is simulating human
knowledge and decision making with computers. We
reach ai through Machine Learning.
• 08. Augmented Analytics: is the use of enabling
technologies such as machine learning and AI to
assist with data preparation, insight generation and
insight explanation to augment how people explore
and analyze data in analytics and BI platforms. We
leverage augmented analytics to eliminate iterative
processes like improve data quality, monitor data,
prepare data and derive quick insights.
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5. 2 MAJOR CATEGORIES OF DATA SCIENCIST
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BUSINESS DATA SCIENTIST-
FOCUS ON ANALYZING
AND DERIVING INSIGHTS
FROM HISTORIC DATA TO
UNDERSTAND WHAT
HAPPENED IN THE PAST…..
PRODUCT DATA
SCIENTIST– FOCUS ON
APPLYING ML ALGORTHMS
AND BUILDING MODELS
THAT FORECAST FUTURE
OUTCOMES……

6. HOW TO GET
INTO DATA
SCIENCE &
ANALYTICS
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ACCREDITED DEGREE
PROGRAMS
ONLINE CERTIFICATION
TRAINING OR BOOTCAMPS
SELF STUDY

7. DATA
CHALLENGES
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Poor Data Quality – Messy Data
Inaccessible Data – Database
Data Collection – lack of real-time data
Data Silo
Data Inconsistency – Disparate sources
Data Management – SQL , NO SQL
Cost – Tools, softwares, hardwares

8. This analysis historic data to uncover
insights on past incidents such as
revenue, sales, cost etc.
Descriptive
As the name suggests, predictive
analytics is about predicting the
future outcomes
Predictive
Prescriptive analytics determines
which action to take to improve a
situation or solve a problem.
Prescriptive
DATA ANALYTICS
Data Analytics refers to process of analyzing raw data to uncover insights, identify trends & patterns
to make informed business decision. Data is extracted from various sources and is cleaned and
categorized to analyze different behavioral patterns. The techniques and the tools used
vary according to the organization or individual.
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9. KEY BENEFITS OF DATA ANALYTICS
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Increase
revenue
1
Mitigate risk of
wasteful
investment
2
Improve
operational
value
3
Monitor & Track
KPIs towards
goal projection
4
Automate
reporting
5

10. WHEN TO USE ANALYTICS
50%
22%
28%
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11. DATA ANALYTICS TOOLS
Most required tools and technologies based on research and job interviews
SQL BI EXCEL PYTHON R
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12. Abilities to leverage technologies, tools or
software
HARD SKILLS
Industry or functions or area of
specialization
DOMAIN EXPERTISE
Communication, presentation, problem
solving skills
SOFT SKILLS
DATA ANALYTICS SKILLS CATEGORIES
The goal of becoming a successful data analytics professional is by having a
combination skillsets of hard, soft and domain expertise. This will give you
competitive edge over other applicants in a position or role.
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13. KPI METRICS
KPI is also known as Key Performance Indicator.
These are important metrics used to track and
measure performance towards business goals.
01. Ecommerce: Revenue, Profit, Profit
margin, ROI
02. Healthcare: Total Patients, response
time, Recovery rate, Length of stay
03. Marketing: CTR, conversion rate, CPL,
Customer LTV, ROAS, ROI
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14. To analyze patient’s health &
Predict recovery cycle.
HEALTHCARE
To help detect customer that
are likely to default in loans.
FINANCE
To increase brand awareness
and customer conversion rate.
MARKETING
To forecast sales and
customer’s lifetime value
SALES
To predict top performing
products for restocking.
LOGISTICS
To identify fraudulent claims
to help save cost.
INSURANCE
APPLICATION OF DATA ANALYTICS
Succesfull companies harness the power of data and analytics to make data-driven
decisions.
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15. DATA ANALYTICS JOB ROLES
• Data Analyst
• Business Data Analyst
• Business Intelligence Analyst
• Data Analytics Specialist
• Data Visualization Engineer
• Product Data Analyst
• Marketing Analyst
• Healthcare Data Analyst
• Financial Analyst
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• Digital Marketing Analyst
• Reporting Analyst
• HR Analyst
• Customer Insights Analyst
• Web Analyst
• CRM Data Analyst
• Manager of Insights & Analytics
• Analytics Manager
• BI Analyst

16. Understand the
purpose of the
analysis
BUSINESS GOAL
Identify data sources
and collect data
DATA COLLECTION
Manipulate and
transform your dataset
DATA CLEANSING
Apply statistical
analysis to dataset
Analysis
Create visualizations
& Present the data
Visualization
DATA ANALYTICS FRAMEWORK
Data analytics lifecycle workflow
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17. www.datatechcon.com 17
DATA SCIENCE DEVELOPMENT FRAMEWORK

18. DATA ANALYTICS
CRISP-DM FRAMEWORK
CRISP-DM stands for Cross Industry Standard Process for
Data Mining (6 phases)
• BUSINESS UNDERSTANDING – Understanding the
business projects and objectives
• DATA UNDERSTANDING – Identifying data sources
and databases, collecting & exploring the datasets.
• DATA PREPARATION – data preprocessing and data
cleaning(the most time-consuming phase).
• MODELING – Building model in using machine learning
algorithms
• EVALUATION – Evaluate the performance of the model
• DEPLOYMENT – Deployment to production
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19. DATA ANALYTICS
PROJECT QUESTIONS
• Questions to ask when tasked with an analytics end to end
project.
• What is the goal of the project?
• What is the main problem and solution goal?
• What are the data sources and databases?
• What data is available?
• Is there a data dictionary?
• What type of analysis is requires? Trend, exploratory,
performance?
• Who are the audience or end users?
• How are the data related in each tables?
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20. DATA
GOVERNANCE
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Data quality ensures we have quality
data that is secured and have integrity.
• Data quality
• Data dictionary
• Data analysts work within the plan

21. COMMON MISTAKES
BEGINNERS MAKE
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Assuming it's easy -
when u start skills its
easy to assume that
consolidating the data
is easy.
Records count - check
the record count 50
states showing 100
Verify your
calculations - don't
trust the numbers. Use
a calc
Failing to ask for data
dictionary - create
one if it doesn’t exist
Making assumption -
don't assume ask
questions
Making calculations
hard to use -
document
Joins - spend more
time working with
multiple tables to
improve joins
Limited access to the
database - working
with only csv or excel

22. LEARNING
TAKEAWAYS
• Learn how to leverage data & analytics to make
data-driven decisions.
• Understand data analytics workflow
• Develop analytics interactive dashboards.
• Learn how to uncover insights & tell a data story
• Learn how to use core data analytics tools.
• Become job ready for a data analytics roles
DATA ANALYST LEARN MORE
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23. THANK YOUQuestions & Answers
PERSONAL IG @omozara
BUSINESS IG @datatechcon
WEBSITE: www.datatechcon.com
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