Data Science Sunday Episode 13, 2024

Statistics accounts for uncertainties, putting a figure to uncertainties

. — Peter Okwukogu

Thanks to Everyone at Colab kaduna Data Science Sundays, most especially the convener Sanusi, and the facilitator Peter Okwukogu

It was a moment we had Data scientists from across the globe, from Africa, Asia, South America etc all in one call.

This was the 13th meet for individuals interested in learning more about Python from scratch, Grasp key Data Science , Data Analysis Concepts.

We engaged in Hands-on Coding Exercises, Participated in Q&A Sessions, Networked with like-minded individuals.

We started off with why the Data Science Sundays is important and key elements of learning Data science for impact, Learn Python, Why Python, Industry Standards.

We discussed at length on some of the reasons why the benefits of data science can not be ignored. data Analysis, Python and Statistics.

We were reminded to organize and structure our learning.

We were taken through Google Colaboratory, Jupyter Notebook.

We went through Python Docs and Glossary.

We Engaged in Hands-on Coding Exercises, Participate in Q&A Sessions, Networked with like-minded individuals.

We had the opportunity to be given links to resources on Data Science, Statistics , Anaconda , and Python Basics on YouTube ,and Links to previous episodes.

This was the fifth series with Statistics and Data Analysis.

We were reminded that statistics — Statistical Estimates — Are critical to making decisions.

We started the session with a lot of questions from attendants. The first question was career opportunities in Python. The career opportunities in Python range from Data Analyst, Data Scientist, Machine Learning Engineer, Data Engineer, Business Intelligence Analyst. This key career paths are broken down below:

1. Data Analyst: This role involves analyzing data to help businesses make informed decisions. You’ll work with data collection, processing, and visualization tools. Proficiency in Python libraries like pandas, NumPy, and Matplotlib will be beneficial.

2. Data Scientist: Data scientists dive deeper into data to build predictive models and advanced analytics. They use machine learning algorithms to analyze trends and patterns. Skills in Python libraries such as scikit-learn, TensorFlow, and PyTorch are crucial.

3. Machine Learning Engineer: These professionals focus on designing and implementing machine learning applications. It’s a more specialized role that requires a strong understanding of both the theoretical and practical aspects of machine learning and deep learning.

4. Data Engineer: Data engineers build and maintain the infrastructure and architecture that allow big data to be processed efficiently. Familiarity with Python scripting, along with knowledge of databases, ETL (extract, transform, load) tools, and frameworks like Apache Hadoop or Spark, is key.

5. Business Intelligence Analyst: They turn data into insights for strategic decision-making. This role involves creating dashboards, reports and visualizations that communicate trends and patterns in the data.

A suggested learning path was the python course on Coursera, also some python documentation was necessary to guide the learner, Python documentation has a section called “ Whetting your appetite” to encourage. newbies in python.

Peter showed his YouTube page as a source of resource too for newbies.

A question was asked on Boolean's in Python.

Boleans in Python

# Data Types — One of two values — True or False

# Boolean values are fundamental to logic and reasoning

# They control the flow of programmes

# If statements, while loops, for loops — evaluate True or False

# A few rules & operators

# Greater than — It evaluates as True if the value(s) on the left of the operator

# is greater than the one on the left

# < less than — It evaluates as True if the value on the left is less than that on

# on the right

a=10

b=14

# Greater than

a > b

False

# Less than

a < b

True

# Conditional statements

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is_raining = False

is_sunny = True

# If statements evaluate if True

# Else evaluates if False

if is_raining:

print (“Take an umbrella.”)

else:

print (“Wear sunglasses.”)

Wear sunglasses.

We used a portion of the responses from a recent meetup to explain boolean’s earlier taught.

We were introduced to the book Think Stats by Allen B. Downey, we were shown the part of the first chapter on Exploratory Data Analysis:

Chapter 1

Exploratory data analysis

The thesis of this book is that Data combined with practical methods can answer questions and guide decisions under uncertainty.

As an example, I present a case study motivated by a question I heard when my wife and I were expecting our first child: do first babies tend to arrive late?

If you Google this question, you will find plenty of discussion. Some people claim it’s true, others say it’s a myth, and some people say it’s the other way around: first babies come early.

The opening statement in the book shows us Data combined with practical methods can answer questions and guide decisions under uncertainty. This is one of the contexts under which we have Data Science Sundays.

We went over some Estimates of Variability,

Mean Absolute Deviation

Variance

Standard Deviation

Median Absolute Deviation

We discussed career opportunities for Product Managers in Data Science and Python.

We went over Tuples in python, using the tuples constructor, creating tuples. Tuples are a good example of making intentions clear , and explicit in code.

We looked at responses from a pre-test survey to understand some of the concepts we discussed in the course of the study .

My take Away :

• You have many defined career opportunities and paths with Python.
• Statisticians seek for estimates.
• Statistical estimates help you understand locations.
• With data science we are focused on any business case.
• You have several career opportunities as a product manager skilled in data science and python
• Empty Tupules help to give clarity and intent, assist in comparisons and operations, make safer parameter on lists.

In conclusion, Data combined with practical methods can answer questions and guide decisions under uncertainty.

You can be part of the next episode , which is episode 14, The registration link to the. meeting. 📺 is below ⬇️

#personalgrowth

#personalgrowthjourney

#personalbranding

#communitybuilding

#communitydevelopment

#GoogleCollaboratory

#Anaconda

#Mentorship

#productmanagement

#Python

#Data Visualization

#Data Science

#Statistics

#Mean Absolute Deviation

#Variance

#Standard Deviation

#Median Absolute Deviation

#tupules