This document provides tips and techniques for presentation skills, interpersonal skills, and project management skills for data professionals. It discusses elements of an effective presentation including defining your message, supporting your message with visuals, and presentation style. It also outlines Dale Carnegie's advice for dealing with people by appealing to emotions rather than logic. Finally, it summarizes popular project management methodologies like CRISP-DM, Kanban, Waterfall, and Scrum and notes their pros and cons for data science projects.

1. START
IT UP!
A summary for self development of data people
BUSINESS MODULE

2. MAIN TOPICS
Presentation Skill Interpersonal Skill Project Management Skill

3. Big Spark Solutions | 2020
What will you talk about?
What is your message?
ELEMENTS
IN
PRESENTATION
How do you support your
message with visuals?
Your style, your voice, your
gestures, eye-contact, etc.

4. GET AS MUCH INFORMATION
ABOUT YOUR AUDIENCE AS YOU
CAN!
1 SLIDE = 1 CHART + 1 SENTENCE.
DONE.
HAVE A ONE SENTENCE TAKE-
AWAY AND YOU HAVE TO BUILD
YOUR WHOLE SPEECH AROUND IT
DO NOT JUST LIST STATISTICS –
TURN YOUR DATA INTO A STORY!
PRESENTATION TIPS

5. ALWAYS REMEMBER!
- DALE CARNEGIE, AUTHOR OF HOW TO
WIN FRIENDS AND INFLUENCE PEOPLE
“When dealing with people, let us
remember we are not dealing with
creatures of logic. We are dealing with
creatures of emotion, creatures bristling
with prejudices and motivated by pride
and vanity.”

6. Don’t criticize, condemn or complain
Give honest and sincere appreciation
Arouse in the other person an eager want
1.
2.
3.
FUNDAMENTAL
TECHNIQUES IN
HANDLING PEOPLE

7. Become genuinely interested in other people
Smile
Remember that a person’s name is to that person
the sweetest and most important sound in any
language
Be a good listener. Encourage others to talk
about themselves
Talk in terms of the other person’s interests
Make the other person feel important – and do it
sincerely
1.
2.
3.
4.
5.
6.
SIX WAYS TO MAKE
PEOPLE LIKE YOU

8. The only way to get the best of an argument is to avoid it
Show respect for the other person’s opinions. Never say, ‘You’re wrong’
If you are wrong, admit it quickly and emphatically
Begin in a friendly way
Get the other person saying ‘yes, yes’ immediately
Let the other person do a great deal of the talking
Let the other person feel that the idea is his or hers
Try honestly to see things from the other person’s point of view
Be sympathetic with the other person’s ideas and desires
Appeal to the nobler motives
Dramatize your ideas
Throw down a challenge
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
HOW TO WIN PEOPLE TO
YOUR WAY OF THINKING

9. Begin with praise and honest appreciation
Call attention to people’s mistakes indirectly
Talk about your own mistakes before criticizing
the other person
Ask questions instead of giving direct orders
Let the other person save face
Praise the slightest improvement and praise every
improvement. Be ‘hearty in your approbation and
lavish in your praise’
Give the other person a fine reputation to live up to
Use encouragement. Make the fault seem easy to
correct
Make the other person happy about doing the
thing you suggest
1.
2.
3.
4.
5.
6.
7.
8.
9.
BE A LEADER: HOW TO CHANGE PEOPLE WITHOUT GIVING OFFENCE
OR AROUSING RESENTMENT

10. CRISP-DM
Pros : flexible and cyclical nature; task-focused
approach; easy to implement.
Cons : does not work for teams; does not cover
communication issues.
KANBAN
Pros : emphasis on work in progress; it is flexible and
easy to use; work is greatly visualized.
Cons : no accent on dates and deadlines (on the
contrary, it is a plus for data science projects).
WATERFALL
Pros : requirements, tasks, dates, assignees are known from
the very beginning; includes plenty of clear visual details;
easy to follow; works well for team communication.
Cons : does not cover change management though cutting-
edge Gantt chart applications allow doing it.
SCRUM
Pros : focus on customers; adaptive and flexible with a
great degree of autonomy; in terms of data science, it
allows optimizing predictability.
Cons : time-bound nature may cause troubles during the
estimation phase when there are many unknown issues;
does not work for long-term projects.
DATA PROJECT MANAGEMENT