How to become a Data Scientist with online resources in 2020
After being quoted “Sexiest job of the 21st century” by Harvard Business Review, data science has grabbed a lot of interest from the general public. Many people are fascinated by the job and dedicated their time to learn how to become data scientist. In the current market, specially after the COVID-19 pandemic, when we saw a humungous surge in the field of online learning, many tools and courses have started to flood in the market, some free of cost and some as a paid solution. But, let’s be honest with each other and admit the fact: for someone being unfamiliar with the field, all these options seems to be like multiple trees in the forest, hard to find which one to choose from.
As a Senior Researcher in the AI space, we at Technogram, listed down almost everything we know for becoming a data scientist. We have created a list of important things to be taken into consideration while the journey onto breaking into the Data Science domain. Along with the list of things, we have curated the best possible resource available out there for you to learn, many of which are free to access and some might be paid.
Resources Break down
Who is a Data Scientist?
A data scientist is someone who is better at statistics than any other software engineer and better at software engineering than any other statistician.
Data Scientists, typical background(Level of education)
Some might be wondering, which degree is the best and required to become a Data scientist? To be honest there is no such degree which determine the extent of the quality of data scientists. To make things clear, the above chart shows the statistics for the popular choice of degree for a Data Scientist.
Takeaways
- Either teach yourself, or get a masters in Data Science. A couple of surprise: astrophysics and philosophy made the list.
- Getting a degree should be looked at as a stepping stone, not a train ride to a destination. no single degree is likely to get you in the door.
- If you’re considering a non-DS degree, you’ll probably want to supplement your lack of DS experience with an internship or boot camp
- If you don’t have a degree, then you’re going to want a lot of practical experiences.
Steps to follow to become a data scientist
Get good at Math, stats and machine learning
The initial requirement to have a good Mathematics, statistics and Machine learning knowledge to grab the attention of any interviewer. To gain expertise in them, refer the suggestions below:
- Math track of Khan Academy
- Linear Algebra by MIT Open Courseware
- Intro to Statistics by Udacity
- OpenIntro Statistics
- Machine Learning by Andrew Ng (Stanford Online)
- Practical machine learning by John Hopkins (Coursera)
Learn to code
- Computer Science Fundamentals: CS 50x on EdX
- Learn end to end development
- Codes you write will be integrated into production
- Choose a first language: Open Source: Python, R, Scala, etc and Commercial: SAS. SPSS, etc.
- Learning by doing interactively: Platform like DataCamp for R.
- Codecademy, codechef for Python.
Understand databases
A Data Scientist, in real life, often work with data in text files. However, once you enter the industry, a database is not always used to store data. It’s going to be stored in MySQL, Postgres, MongoDB, Cassandra, etc.
Resources:
Master Data Munging, visualization and reporting
Data Cleaning
The process of converting one raw form of data into another format for more convenient consumption.
- Getting and cleaning data by John Hopkins
- Tools:
- Data Wrangler alpha
- dplyr
- Pandas
Data Visualization
It involves the creation and study of the visual representation of data.
- Tools Commonly used:
- d3.js
- ggplot2
- plotly
- matplotlib
Reporting
In every data analysis scenario, putting the analysis and the results into a comprehensible report is the final hurdle to take.
Step up with Big Data
When one start operating with data at the scale of the web, the fundamental approach and process of analysis must change. Most data scientists are working on problems that can’t be run on single machines. They have large datasets that require distributed processing.
Hadoop
Hadoop is an open source software framework for storage and large-scale processing of dataset on cluster of commodity hardware.
MapReduce
MapReduce is the programming paradigm that allows for massive scalability across the servers in a Hadoop cluster.
Apache Spark
Apache Spark is Hadoop’s speedy Swiss Army knife. It is a fast running data analysis system that provides real-time data processing functions to Hadoop.
Get experience, practice and connect with fellow data scientists
- Participate in competitions over platforms like Kaggle, Skillenza, Hackerearth.
- Connect and meet fellow data scientists.
- Develop a pet project.
- Enhance your intuition
Internship, Bootcamp and get a job
The best way to find out whether you are a true data scientist or not is to take the bull by the horns and to enter the real life jungle of data analysis and science with your freshly acquired skill set. Search for internships from platform like Internshala, Indeed, LinkedIn, etc.
Follow and engage with the community
Sites to follow
Need Data?
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