Data Science-Pathway for Beginners

Ever since 'Data Science- The sexiest job of the 21st century' appeared on Forbes, the demand and desire of a Data Scientist have stormed. Manipulating data and getting paid highly is a dream of a Tech enthusiast.

And beginners often confuse Data Scientist with other roles like Data Engineer, Data Analyst, Machine Learning Engineer. However, these all overlap with each other on the responsibilities and skills needed. The confusion is mainly due to the label companies are giving to their employees. So, a data scientist at one company can be a data engineer at other. It depends on the size of the company too.

Then comes Machine learning, a subset of AI, and these all combine to give a migraine-Esque headache to beginners. So, from my experience and some research, I've tried to simplify things up regarding what they are and how to get one`s hands onto them.

There's this world we live in and there are numerous problems that we have to solve. These can be any sort belonging to a business, government, education, health, or almost anything. And we need a huge amount of data to solve the problem. We can collect data through various surveys, data usages, and simulations. After we have obtained and stored our data, comes tasks of data cleaning and analyzing. We have various tools to analyze the data and find a pattern. Analyzing data basically means restructuring them according to our needs and also dealing with missing data. This can sound mammoth for a beginner, but simple projects like a website dashboard or an app will be enough to get hands-on experience. Then we perform several statistical analyses and tests on our data, to kind of tell if the data is meeting our requirement or not. This process is done by a Data Scientist or a Data Analyst. Then he/she creates a report about the visualization of the data and submits it to the project employer. Furthermore, if we want something, which communicates with the real world, we need to implement various machine learning algorithms and test them out to create what is called a Data Product. This is also done by a Data Scientist or a Machine Learning Engineer, again depending on the label companies give.

That was just a basic overview of what is done by a Data Scientist. Keep in mind, as a beginner we don`t need to dive right in, we can do simple starter projects as I mentioned above for actually grabbing an insight.

Here, I lay out a basic pathway to becoming a Data Scientist. This is just from my experience and the way I took several courses as a beginner.

1. Python for Data Science
   a) Functions and Strings
   b) List and Tuples
   c) Sets and Dictionaries

2. Numpy

3. Pandas

4. Matplotlib

5. Seaborn Data Visualizations

6. Data Analysis Capstone Projects

A lot of courses on Udemy, Coursera, Cognitive Class cover all these topics from the very basics. There are also several resources on Github, Youtube, and Kaggle where we can learn and view other's work. And one of the dilemmas I got myself was into spending several days on finding the right course. Then I realized there's no such thing as a perfect course, we can learn from any of the verified resources online, we should just have the grit. And after covering the basics, we shouldn`t jump right into Deep Learning and Neural networks. The best way to learn is through practice, so it's always better to do several projects which will definitely help us gain experience and boost our learning confidence.