Data Science
Welcome back to the exciting world of a graduate student in data science!
In today's digital age, data has become the fuel that powers innovation and drives decision-making across industries. Data scientists are at the forefront of this revolution, using their expertise to uncover hidden patterns, derive valuable insights, and make data-driven predictions that shape the future. This post is a start of a series, as I dive into the day-to-day life of a data scientist, exploring the dynamic and ever-evolving nature of our work.
But first, some background
After receiving my bachelors and immediately entering the working force as a full time analyst, I discovered a new passion for learning more about data. The world of data became more of a priority after being trained in data integrity, data collection, and data analysis. As a quality control analyst in early development pharmaceuticals, data integrity, and GMP are taken very seriously and I developed new skills to organize and extract data. Then, sometime in early 2022, I took a leap of faith, and decide to pursue a masters in Data Science before applying to medical school. I wanted to gain skills in data manipulation and visualization using R, Python, and SQL. My ultimate career goal is to have a solid understanding of statistics and machine learning techniques and successfully apply these methods to real-world problems in hopes to be a better researcher before pursuing medicine.
What really got me excited about the field of data science was hearing people say, "Every day is a new adventure with new challenges and opportunities." As a data scientist, you start your day by diving into data and checking on the progress of ongoing projects. Armed with your technical skills and an insatiable curiosity, you dig deep into datasets, using fancy statistical analysis and data visualization techniques to really understand what's going on. But here's the thing that caught my attention: collaboration is a huge part of being a data scientist. You get to work closely with all sorts of professionals to make sure your findings actually matter to the organization. Being able to explain complex stuff in a way that people can actually understand and act on, is important to me. But being a data scientist isn't just about coding and crunching numbers. There's more to it. So, I’m writing thisdata science series to share all kinds of info about data science, what to expect as a grad student, the latest tools and techniques, and of course, my own journey into this exciting profession.
MORE BACKGROUND ON DATA SCIENCE
Data Science
I love data
In recent years, data science has become increasingly popular. It's an area that combines statistical methods, computer science, and domain expertise to reveal valuable insights and knowledge from historical data. Data science right now, is now one of the most sought-after fields in the job market.
Data science focuses on compiling, analyzing, and interpreting data. It utilizes a spectrum of techniques and tools to extract valuable insights and knowledge from data. This field finds applications in various industries such as business, healthcare, finance, and marketing. It plays a crucial role in making informed decisions, predicting future trends, and identifying patterns in data.
The field of data science involves three main areas: data engineering, data analysis, and machine learning. Data engineering involves the collection, storage, and cleaning of data. Data analysis involves the use of statistical methods to analyze and interpret data. Machine learning involves the use of algorithms to develop models that can make predictions based on data.
One of the key skills required for a data scientist is the ability to code in programming languages such as Python and R. These languages are used to manipulate and analyze data. Data scientists also need to have a good understanding of statistics, machine learning, and data visualization. They should also have good communication skills to present their findings to stakeholders.
The field of data science is rapidly growing, and there is a high demand for skilled data scientists. According to Glassdoor, data scientist is the number one job in America based on job satisfaction, salary, and job openings. The average salary for a data scientist is $113,000 per year, making it one of the highest-paid jobs in the industry.
I gravitated towards the career because of how different day to day life can be, how many opportunities for growth and learning there is, and I was specifically interested in learning how to code. I learned that data scientists play a critical role in helping organizations make informed decisions based on data. As the world becomes more data-driven, the demand for skilled data scientists will continue to grow. If you are interested in pursuing a career in data science, there are many resources available online to get you started.
My favorite resources for learning code are:
Other resources
data science weekly
Open Source Data Masters
Flowing Data
Simplilearn PG in Data Science
Code with Google – Applied Computing Series
California Institute of Technology Learning From Data Course
Master of Information and Data Science (MIDS) at UC Berkeley School of Information
Ready For A Job In Data Science?
Let’s get prepared!
1. Data Science Fundamentals:
- Gain a solid grasp of fundamental concepts in statistics, probability, and linear algebra.
- Refresh your knowledge on key terms and concepts in data science, such as correlation, regression, hypothesis testing, and clustering.
2. Programming Skills:
- Proficiently code in a programming language commonly used in data science, such as Python or R.
- Enhance your skills by practicing coding exercises and algorithms related to data manipulation, data cleaning, and data visualization.
- Familiarize yourself with widely used data science libraries, like pandas, NumPy, scikit-learn, and matplotlib (Python) or tidyverse (R).
3. Machine Learning:
- Develop a strong understanding of various machine learning techniques, including supervised and unsupervised learning.
- Study different algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors.
- Learn about evaluating models using performance metrics, cross-validation, and avoiding overfitting.
4. Data Manipulation and Analysis:
- Sharpen your skills in working with real-world datasets by performing tasks like data cleaning, preprocessing, and feature engineering.
- Acquire knowledge of SQL for querying and manipulating databases.
- Explore data visualization techniques using libraries like matplotlib, seaborn, or ggplot2.
5. Big Data and Distributed Computing:
- Familiarize yourself with distributed computing frameworks, such as Apache Hadoop and Apache Spark.
- Understand concepts related to processing and analyzing large-scale datasets, such as MapReduce and parallel computing.
6. Probability and Statistics:
- Review probability distributions, hypothesis testing, confidence intervals, and statistical significance.
- Comprehend concepts like A/B testing, sampling, and experimental design.
7. Data Science Workflow and Problem Solving:
- Gain knowledge of the end-to-end data science process, encompassing problem formulation, data collection, exploratory data analysis, model building, and evaluation.
- Practice solving case studies or real-world data science problems.
- Recognize the significance of feature selection, model interpretation, and effective communication of results.
8. Additional Topics:
- Deepen your understanding in specific areas of interest, such as natural language processing (NLP), computer vision, time series analysis, recommendation systems, or reinforcement learning, based on your preferences and the job requirements.
I have gotten suggentions to combine theoretical understanding with hands-on practice by engaging in projects, participating in Kaggle competitions, or contributing to open-source data science projects. Additionally, consider reviewing common interview questions and conducting mock interviews to enhance your communication and problem-solving skills in high-pressure situations. Best of luck with your data science interview preparation!
