Breaking into the Data Science Field and Landing 3 Job Offers, What I Learned from it.
Breaking into a field with little-to-no related experience is not easy. If you’re considering it or are in the process of transitioning, let me just tell you that it is not easy.
If you love data science however, and can push through hardship, it can be one of the most rewarding experiences in your life.
Regardless of what path you decide to take, I just want to offer up a few pieces of advice that could potentially help you on your journey.
Everyone’s Journey is Different
Some people already have prior experience or education related to the field. Some people don’t. Some people get into a Masters program in data science. Others are self taught. Some people will get lucky and get an offer with their first application. Others will on their 500th application.
Everyone’s journey is different. Just focus on your own, try not to compare yourself. But use other people’s journeys as information to help guide your own.
Important Skills to Know
Basic math and statistics: Math operations, descriptive statistics(standard deviation, categorical/continuous variables, distributions, etc.), data visualization concepts(bar graphs, line charts, histograms, etc.)
Computer knowledge: RAM vs disk space, purpose of command line, purpose of servers, cloud storage.
Excel: A lot of companies still run on Excel, and if you’d like to break in to the field, being good at Excel will help. Some basic things you should know: formulas, charts, lookups, pivot tables, formatting, data cleaning.
SQL: SQL is used in most data science roles to get data and store data. While there are other options out there, SQL is probably the most popular at the moment, and has been for the past 20 years. Some basic things you should know: Commands, schema structure, one or two syntaxes(MySQL, Postgres, etc), functions, subqueries.
Python: The hot new tool for data science in the past 5 years. Although R is used as well, Python is a general purpose language that you can integrate more functionality into your work. Python can be used for data cleaning, visualizing, machine learning, web scraping, automation, interface development, statistics, and much much more. Some basic things you should know: Objects, classes, data types, storage types, loops, functions, notebooks vs IDEs, libraries, Pandas, NumPy, SciPy. More advanced things for specialized roles: Apache Spark, xgboost, etc.
Tableau/Looker/Power BI: Depending on how much your role involves reporting and data visualizations, there are a lot of roles where knowing the above softwares are a primary responsibility.
The Emotions you will Face
I decided to write this last part in another post linked here:
I felt like this part deserved it’s on page.
