Here we learned a lot today about animations in Figma. Now you have time to

While taking this machine learning course, I realized I need to develop more statistics knowledge. I took a probability and statistics course in college. But that wasn’t enough. This course on Coursera helped me a lot:

There are a lot of boot camps out there, charges 10000 USD for their courses and just teach you the very basics. But they will sell you the courses by saying that they will make you a data scientist in 12 weeks or 4 months. Do not fall for those traps and spend your money there. If you can find a Bootcamp that tells you they will teach you programming and algorithms in 4 months that is the best starting point.

I secretly wanted to work in the Data Science and Artificial Intelligence industry but I thought that’s probably too hard. I thought I needed more coding expertise before I can start that.

After Bootcamp, I kept practicing PHP, SQL, JavaScript, and WordPress! I was still teaching and was hating my job. I was desperate to find another career. I think most people start thinking of a different career because they are not happy in their current job or not making enough money. And then because of a lot of buzz about Data Science, focus shifts there.

One morning I received an email that LaunchCode is offering a free BootCamp in Miami that is called CS50. Miami Dade College will host it. That was Harvard University’s introduction to the computer science course. They videotape their classrooms and LaunchCode uses that for their Bootcamp. That course is also available in edx and can be taken for free using the audit option.

I started searching for some free resources on Google. Because I had no idea at all how coding looks and feels like. I just wanted some free experience before spending any money on it. I came across the LaunchCode website. They had some free courses and practice platforms. I even didn’t have to install anything. The first course was an HTML course. As I did not have any idea and even did not talk about it to anyone, I started with HTML. When I learned to code a few lines of HTML and I saw some output, it was very thrilling. So I kept doing it. After HTML, I also took the CSS and JavaScript courses for free in the LaunchCode platform. Now I know that all was just introductory level courses.

I made a community of coders and got familiar with the essentials of coding like data structures and algorithms. They also teach some HTML and CSS. For the first time, I used SQL there. That experience gave me the realization that I have a long way to go before I can call myself a software developer. Yes, after LaunchCode, that was the goal. I wanted to be a web-based software developer.
If you really want to be a data scientist and grow in this profession, you will need a lot of different skills. SQL and NoSQL databases, statistics is essential and programming as I mentioned earlier. These are the basics. Spend enough time to learn them well. Rushing will never help. Here is an article on that:
I shared my own journey, some great resources I used on the way, and some of my ideas in this article. I hope it will be helpful for some of you. I am sure many people will relate to some of my experiences. Please feel free to ask if you have any questions for me.
Unlike most major cities, San Francisco allows residents to plant their own street trees, resulting in an urban forest as diverse as the people who live here. Though the city’s Mediterranean climate can be hard on trees, nearly 125,000 of them live here with more than 500 species from all over the world.
Since stumbling upon a self-guided Covid-19 tree tour organized by Sullivan and fellow tree nerds Jason Dewees and Richard Turner, getting to know my street trees has become a favorite pandemic activity. At first, I followed chalk markings left by the trio on sidewalks, detailing the common and Latin names of those they found noteworthy. Then I ventured out on my own, using Sullivan’s book and the app LeafSnap to identify trees in my neighborhood.

In the three blocks between my home and the nearest coffee shop, there are over 20 species of trees from six continents. There’s the adorably short fig tree in my neighbor’s yard, a silver tree that glistens under the sun, and a cork oak with a squishy trunk. There’s also a grapefruit tree, a row of Marina strawberry trees, a Japanese blueberry tree, and an Indian hawthorn. All live and thrive on a single street in San Francisco.

All these courses teach how to use different libraries to analyze data and make predictive models. But I wanted to learn to develop the machine learning models from scratch, not only from a library. I found another great machine learning course offered by Professor Andrew Ng of Stanford University.

A Complete Guide to Confidence Interval, and Examples in Python
Deep Understanding of Confidence Interval and Its Calculation, a Very Popular Parameter in

“San Franciscans enjoy unusual stuff,” says Mike Sullivan, author of The Trees of San Francisco and one of three organizers of the Covid-19 tree tours that first sparked my love for my street’s trees. “People choose to be unusual here. We love diversity in human beings and diversity in our trees.”
After taking some of the courses, I did an internship and a few projects. I attended some conferences, presented some personal projects there, joined several Meetups, and made some good friends, and learned a lot from them. Currently, I am doing my MS in Applied Data Analytics at Boston University.
This is very common to switch to data science. Most data scientists I know out there do not have a degree in data science. They switched from another area. I also know many people who are trying to switch from another major. I meet many people being confused if it is the right career track for them. Well, the decision is yours. Everyone may have a different journey and may have different opinions. In this article, I decided to share my own experience. For some people, it could be an interesting read and for some people, it might be helpful information.

As you can see I use Coursera a lot! It has a lot of great courses. But it takes some time to find a good course that is suitable for you. A lot of time I started a course and after I have done it halfway I realized this is not for me. So, there will be some time laps there. If you are totally new to Coursera and do not know how to take courses for free there, here is a six minutes tutorial for that:
A Complete and Clear Overview of Different Types of Discrete Probability Distributions and R…
Ideas, Formulas, and Examples of Each Type of Distribution. Implementation of the formulas manually and also with
The first mistake I made was getting scared. I spent so much time learning web development and also worked as a front-end web developer knowing that I actually wanted to be a Data Scientist. I was desperate to change my teaching job as fast as I could.
I took more classes in Udemy and Udacity and was applying for jobs as a web developer. I worked as a web developer in a startup for a couple of years. While working there, I thought I probably know enough to try Data Science now.
3. If you do not have any math or technical background, you may have to spend some extra time. But there is nothing to be pessimistic about. If you want something, you need to spend time and put some effort into it.
A Complete Guide to Confidence Interval, and Examples in Python
Deep Understanding of Confidence Interval and Its Calculation, a Very Popular Parameter in
I will write a separate article on that. But if you ask me, if I find it useful. My answer is yes. I am loving it. It was a great decision. On the other hand, If you ask me if it is possible to become a successful data scientist without a degree. My answer to that is also yes. I have met many data scientists in the meetups and data science conferences who do not have any data science or a computer science degree.

Writing mostly to myself. Sharing some of it with you. Hope it helps.