How I made learning Data Science easy: intro

Please read my bio first so that you know who is talking to you :)

Let's first understand what Data science (DS) #datascience is and whether it is for you. DS is the analysis of data to understand its "story" and extract insights from it to generate business value. DS is a multidisciplinary field of technology, inference and algorithm development to solve business problems analytically.

Being an interdisciplinary field, the main building blocks of DS are techniques and strategies in Statistics, Mathematics, Computer science and Information Technology.

Is Data Science for you?

Do you enjoy working with various forms of data and extracting hidden patterns from it? Do you enjoy being like an investigator that looks at terabytes of data over and over?

You may not have a clue what to expect before analyzing the data. The data may show nothing on the surface but when you dig deeper the data may convey interesting pattern.

Why did I choose Data Science as a career?

I have always enjoyed working in multidisciplinary teams and collaborating with people with various backgrounds. As a Data Scientist, you get to work in a team of individuals with diverse experiences: you learn from your colleagues things you never studies before and you may also get a chance to tell them about a cool technique you know. This mutual transfer of knowledge is a unique experience and has a great impact on the overall productivity of the project and more importantly on your personal growth. These interdisciplinary collaborations bring solutions to a problem from various aspects, making the team more powerful and your work more valuable; This is what I love the most about being a Data Scientist.

What Data Scientists often do?

Before reading the rest of this post, I recommend taking a look at my other post outlining "Do Data Scientists really exist?"

Now that you have a better understanding of the overall field let's continue. The overlapping duties of different types of Data Scientists are mainly around dealing with the data itself: cleaning the data to make it ready for analysis (AKA data munging (wrangling) to transform/map the raw data in a better format for analysis). Data come from various resources (several CSVs, databases, multiple tables), so Data Scientists may be involved in ETL-type processing to ingest the data. Side note: what's ETL? ETL stands for Extract, Transform and Load, mainly a topic in database management (e.g. SQL), which I will cover soon.

As a Data Scientist, you should be looking at online resources to see what's going on in technology to improve the techniques and the models you use. (e.g. #Kaggle)

https://panoply.io/data-warehouse-guide/3-ways-to-build-an-etl-process/

Good luck Data Scientists to Be!