After I decided I wanted to become a data scientist, I did research into various free online courses, tutorials, and resources. I found a data science specialization via Coursera and a data analyst "nanodegree" program via Udacity.
I did some research and tried to decide which program I would focus on. Since both have free materials available, I would probably use some materials from both programs, but I wanted to complete all the coursework in one of the programs. One of Udacity's selling points is that their courses are designed by industry professionals. On the other hand, Coursera's classes come from professors in academia. Since I am looking to stay in the industry, that was definitely worth considering. However, I wanted R to be my primary language and Coursera's specialization focused on R while Udacity's program seemed to be split amongst various technologies. I do think it is important to have exposure to multiple technologies, but I wanted to get enough practice in one technology to be able to work with it professionally.
One final factor was cost. All of Coursera's courses are available completely for free. Udacity offers free materials, but to access certain things you need to pay.
At this point, I had decided that I was going to focus on the programming language "R" and the Coursera data science specialization, but I thought I'd need to understand more than just the material in specialization courses to be successful. Early in my college career, I took many courses on computer science and mathematics theory without understanding real world applications of information technology. It was not until I changed my major to information technology that I learned how to develop a web application or learned the basics of system administration so that I could actually write code for an application and put it on a server myself. After learning that, I was amazed that I could quite possibly have graduated college with a degree in computer science without knowing a lot of basic things about computers. When I decided to go into data science, I was determined to make sure I got some brief exposure to closely related fields so I would have a bigger picture understanding of what I was doing.
I had seen a lot of big data jobs mention Hadoop and I also found this infographic by Data Camp which has a list of steps to become a data science and they mention Hadoop as well so I decided it was something worth exploring. This blog entry is getting a bit long so I'm going to wrap it up here and continue talking about Hadoop in the next entry.
I did some research and tried to decide which program I would focus on. Since both have free materials available, I would probably use some materials from both programs, but I wanted to complete all the coursework in one of the programs. One of Udacity's selling points is that their courses are designed by industry professionals. On the other hand, Coursera's classes come from professors in academia. Since I am looking to stay in the industry, that was definitely worth considering. However, I wanted R to be my primary language and Coursera's specialization focused on R while Udacity's program seemed to be split amongst various technologies. I do think it is important to have exposure to multiple technologies, but I wanted to get enough practice in one technology to be able to work with it professionally.
One final factor was cost. All of Coursera's courses are available completely for free. Udacity offers free materials, but to access certain things you need to pay.
At this point, I had decided that I was going to focus on the programming language "R" and the Coursera data science specialization, but I thought I'd need to understand more than just the material in specialization courses to be successful. Early in my college career, I took many courses on computer science and mathematics theory without understanding real world applications of information technology. It was not until I changed my major to information technology that I learned how to develop a web application or learned the basics of system administration so that I could actually write code for an application and put it on a server myself. After learning that, I was amazed that I could quite possibly have graduated college with a degree in computer science without knowing a lot of basic things about computers. When I decided to go into data science, I was determined to make sure I got some brief exposure to closely related fields so I would have a bigger picture understanding of what I was doing.
I had seen a lot of big data jobs mention Hadoop and I also found this infographic by Data Camp which has a list of steps to become a data science and they mention Hadoop as well so I decided it was something worth exploring. This blog entry is getting a bit long so I'm going to wrap it up here and continue talking about Hadoop in the next entry.
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