Getting started with Data Science
By: Simran Yadav
Tags: Tech, Machine Learning, Towards Data Science, Data Science

Getting started with Data Science

Summarizing my speech at WomenTech Network 2020 on “how can get started with Data Science — sharing some tips and sources and, my journey from being a humanities student to getting a Master’s in Data Analytics.

Starting off by explaining what is data science:

There are numerous definitions of Data Science, some people might say its computer programs do really cool stuff. Or, data wizards would define data science as doing magical stuff with data!

Or there are people who like to define it as an human-computer relationship: they might see — Data Science as “converting human problems, putting them in a computer — thus, making it a computer problem, and expecting the computer to solve that original human problem.”

Here, we are essentially making our problem, placing it across and making it someone else’s problem to solve.

My story: From Humanities to Data Science:

Photo by Simran Yadav on Prezi

When I moved from India in 2015, I started off as an economics and international studies student. But, in my sophomore year as a part of my curriculum I had to take a class in Information systems and statistics. Through these classes I accumulated the knowledge of how we can use MS access, MS excel and statistical tools like regression in the real world. I got genuinely interested in information systems and decided to major in that too.

Studying three very different subjects was a hassle initially, but I am glad I did that. Through information systems I realized that I undoubtedly enjoyed understanding the world through numbers and mainly through trends.

By the end of my bachelors, I knew I wanted to get into the data and information systems field. But, at that time I was not completely comfortable with analytics and definitely lacked working knowledge and needed improvements in data science. So I decided that the best way to improve my skills was to go back to Grad school. (There are other ways to improve your Data Science skills, some of those are mentioned towards the end of this article). But, I decided to apply to the Business data analytics program at Loyola University (it is the best MSBDA program in the Chicago land area and ranked the 33rd best in the country, so it instantly felt right.).

This program basically prepared me for working with statistics in Excel, how data can be employed in marketing and supply chain (that domain knowledge really helps in making sense of all the number crunching that you are doing). Along with that, one thing that I really appreciate about this program is the curriculum’s overall emphasis on hands-on experience to learn different machine learning approaches. So, since the last 8–9 months I have been intensely involved with 6 different projects, some of them are with actual companies and involve aiding them with solutions to a business problem that they are facing. And, for some I had the flexibility to work in the area I was interested in and was able to choose a dataset on my own and build models that I identified would be the most impactful to solve the problem.

Tip: While choosing a Master’s program in Data Analytics or Data Science,

look for a program that is a hybrid of theory and practical knowledge. It will immensely help in acquiring knowledge and later on help in demonstrating those skills professionally.

Lastly, over the course of my masters, I learnt soft skills like putting together a really good presentation, and how to create compelling visuals that tell a story.I think one of the things that is really overlooked in Data science is the story telling aspect. Yes, coding is important! Yes, modelling is important!

But, to really put those in use we need people to comprehend what a message is, how your findings can be used in a business sense. A lot of that depends on how you frame it, deliver it and generate interest in your audience to understand it.

So what am I doing right now? I am currently wrapping up my Masters with working at Greenwich HR. It is a labor market intelligence company and collects Real-time, comprehensive hiring and pay data. Extremely interesting but challenging since we are working with dynamic updates in data almost every single day due to COVID-19.

Photo by Simran Yadav on Prezi

Some tips:

1. Definitely learn coding: If you don’t have much coding skill, that’s okay, according, to a study out of all data scientists about 22% of them have 1–2 years of coding and about 19% have less than a year of coding skill.

Python and R are the industry standard. About 70% Data Scientists prefer Python. It’s especially good for unstructured data. I personally prefer R, that just because of my inclination towards Statistics. But either one of those are fine.

Get comfortable with storing data, retrieving, accessing, manipulating and making the best use of it for fruitful outcomes to take decisions.

2. Possess some mathematical knowledge: Nothing too extensive like advanced calculus or derivatives (I had to do those, but that was for my Econ major.) But yeah be comfortable with algebra, statistics, and probability.

3. Domain knowledge: is helpful if you want to get in a specific field of data science like sports analytics, finTech or supply chain analytics.

How can you acquire and improve the above skills?

1. Online-learning: Select and do courses and get some online-certifications. Coursera has a really good Intro to Machine learning course, IBM Big data university has learning paths in different languages like Python, R and even Hadoop.

2. Storytelling skills: Tableau and Power BI good for data visualisation. Tableau even has a learning path for Data scientists, data analysts and so on. The catch here is that, They are offering these courses for free right now. Otherwise, they usually range from $200–300 per course.

3. Real-time projects: Apache projects, Kaggle and Pivigo have tonnes of resources and good sources of getting involved with real-time projects.

4. Get started: I think this is the most crucial, once you have some coding skills start working with a dataset you like and build initial models. Most people start with Titanic, or HR dataset but, for me working on a dataset that I am interested in has really helped me improve my skills since, I go above and beyond in those with analysis and visualization. Recently I started working with diamonds quality and Cartier jewelry datasets from Kaggle.

The process of becoming a thorough professional data scientist is never-ending so the most crucial part is to identify the problem, solve it with data-driven approaches and you will learn the models and technical skills as a barter.

Link to the above sources:

1. Online courses:https://www.coursera.org/learn/machine-learning-duke

https://www.coursera.org/professional-certificates/ibm-data-science

2. Story Telling: https://www.tableau.com/learn/get-started

https://www.udemy.com/course/powerbi-complete-introduction/

3. Real-time projects: https://www.kaggle.com

https://projects.apache.org/projects.html