How to Data (Joshiverse- Journey of a Budding Data Scientist)

Georgia shares her experience and story of becoming a seasoned Genomics Data Scientist by mostly self-learning!

In this mindblowing conversation, we touched on key aspects of the career path, the industry practice, tools and technologies used, and growth tips for professionals in the space.

Also, catch a glimpse of how she managed to fast-track her growth and recently switch jobs.

Resources mentioned in the podcast:

Codecademy - (https://www.codecademy.com)

Code First Girls - (https://codefirstgirls.com/courses/classes/coding-kickstarter/)

Github links - https://github.com/czbiohub-sf/learn-bioinformatics - https://github.com/harvardinformatics/learning-bioinformatics-at-home

In this Q&A session, I addressed concerns around the need for creativity in data visualization, how to stay consistent, how to apply learnings from Data courses to real-life use cases, and many others.

If you have any questions, put them in the comment section and I will respond to them ASAP!

Dive into the world of data analytics as we unleash the practical guide to doing it right! Discover powerful strategies, expert tips, and game-changing insights in this captivating episode that will elevate your analytics skills and career. Don't miss out on this data journey!

Data Analytics - How to Do it Right

In this first part of the discussion, Hamid Abdulsalam, a Data Scientist with Factual Analytics shared critical insights into how to get started as a Data Analyst.

Bringing over 8 years of experience as a Data Scientist and Statistician, he demystified the best strategy for gaining and retaining proficiency in Data Analysis.

We looked into the common misconceptions around Data Analysis and expatiated on the primary focus areas for rapid career growth in the Data Space.

Links mentioned by Hamid (https://www.linkedin.com/in/bigbirdhamid/) --

for publishing data content;

for learning data science concepts; https://towardsdatascience.com

Join us on this ride to discovery!

I briefly discussed the less technical requirement for becoming a Data Analyst/Scientist in this introductory episode. Anticipate subsequent live podcast interviews.

A brief episode on Relational Data, Data Visualization, and Exploration. A need to understand the relationship between tables in a dataset for the purpose of proper data wrangling.

A look into data collection through webscrapping, using beautifulsoup and requests libraries in python.

Here we have a look at Algorithms, Pseudocode, Time Complexity and Bubble Sort.

While we gather momentum to learn some core python coding concepts; This episode albeit short seeks to clarify the need for python standalone files and to show the difference between Scripts and Modules in python.

In this episode I bring the series on Collection Types to an end by discussing Tuples and Sets. I also talked about their importance and how to utilize them. Enjoy!