Landing a Career in Data
Recently, I have searched about the career of data in Udacity website — a great online course that gives us a free course about Intro to Data Science, when I checked the website there is a course about The Data Science Nanodigree Program that makes me interested. So, I when I checked more, I found a document about the complete guide to landing a career in data that has lists about many things like career option in data, in-demand skills and many more.
When choosing in a career, there are safe paths to pursue, and there are risky ones. A career in data has offers the best of both worlds. On the one and, it is a secure choice, demand for data talent continues to increase, and shows no sign of abating. With data skills in your toolkit, you’re going to be in demand virtuality any industry. The good news is, that no matter which route to take, the secure one, the risky one, or something in between, there are ample career opportunities out there for anyone interested in data.
The first step on your path to professional data, talking stock your three main career option: Data Analyst, Data Scientist, and Data engineer.
A data analyst is essentially a junior data scientist, data analyst don’t have the mathematical or research background to invent new algorithms, but they have a strong understanding of how to use existing tools to solve problems.
Skills and tools
Data analysts need to have a baseline understanding of five core competencies: programming, statistics, machine learning, data wrangling (data munging), and data visualization. Beyond technical skill, attention to detail and the ability to effectively present result are equally important to be successful as a data analyst.
How it translates
Data analyst are given direction from more experienced data professional in their organization. Based on the guidance, they acquire, process, and summarize data. Data analysts are the ones managing the quality assurance of data scraping, regularly querying the database for stakeholder requests, and triaging data issues to come to timely resolutions. They also then package the data to provide digestible insights in narrative or visual form.
Some companies treat the titles of “Data Scientist” and “Data Analyst” as synonymous. But there’s really a distinction between the two in terms of skill set and experience. Thought data scientists and data analysts have the same mission in the organization, to glean insight from the massive pool of data available.
A data scientist is someone who can do undirect research and tackle open-ended problems and questions. Data scientists typically have advanced degrees in a quantitative field, like computer science, physics, statistics, or applied mathematics, and they have the knowledge to invent a new algorithm to solve data problems.
An enduring curiosity about data and close examination of evolving best practices and tools serves all data professionals well.
Skills and tools
Whereas a data analyst might look at data from only a single resource, a data scientist explore data from many different sources. Data scientists use tools like Hadoop (the most widely used framework for distribute file system processing), they use programming languages like Python and R, and they apply the practices of advanced math and statistic.
The exact set of skills differs by organization and project, but this example from Data Science London gives a sense of how complex the Data Scientist’s toolkit can be:
How it translates
Data Scientist essentially leverage data to solve business problems. They interpret, extrapolate from, and prescribe from data to deliver actionable recommendations. A data analyst summarizes the past, a data scientist strategized from the future.
Data scientists could identify precisely how to optimize website for better customer retention, how to market products for stronger customer lifecycle value, or how to fine-tune a delivery process for speed and minimum waste.
Data engineer builds a robust, fault-tolerant data pipeline that cleans, transforms, and aggregates unorganized and messy data into database or data source. Data engineers are typically software engineer by trade. Instead of data analysis, data engineer are responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery system into place.
Data engineer essentially lay the groundwork for a data analyst or data scientist to easily retrieve the needed data for their evaluations and experiments.
Skills and tools
As such, data engineers have deep knowledge of and expertise in:
- Hadoop-based technologies like MapReduce, Hive, and Pig
- SQL based technologies like PostgreSQL and MySQL
- NoSQL technologies like Cassandra and MongoDB
- Data warehousing solutions
How it translate
Data engineers do the behind-the-scenes work that enables data analysts and data scientist to do their jobs more effectively.
If you’re new to the field of data science, you’ll want to star as you develop your skills and gain experience, you’ll be able to progress to data scientist or data engineer.
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