What is Data Science | How to Become a Data Scientist 2023

For this, you require two important domains: Analytics and Machine Learning

– Prescriptive Analytics – This field is all about training your machines. You can create models that run by analyzing the previously processed data. Not only this, inculcating efficient algorithms in them will result in a model that has an intelligence of its own and can analyze the data on its own at the time of execution, taking all the possibilities into consideration.

– Causal Analytics – This domain helps your machines to analyze the previous data provided to them and then make the decisions. If the machines want to execute a particular task, they can know what the possibilities of that task are by analyzing the previous scenarios and then execute the proper step.

– Machine Learning – This field is itself very popular but is an important part of the Data Science world. The algorithms that we are talking about from the beginning of this article are all designed using Machine Learning only. It helps the machine to recognize hidden patterns in a dataset and then make efficient predictions. Pattern discovering algorithms like Clustering etc. are designed using this.

You can break Data Science into 5 main stages

Data Preparation – Data entry, signal reception, data acquisition, data extraction.

Maintaining the data – Data cleansing, data staging, data warehousing, data architecture, data processing.

Model Building – Data modeling, data summarization, clustering, data mining.

Communicating the data – Data visualization, data reporting, decision making, business intelligence.

Analyzing – Predictive analysis, text mining, regression, qualitative analysis.

To give you a more clear picture, here is an organized list of the skills required for data science

Domain: Data Analysis
The skill required: Statistics, Python, R

Domain: Data Warehousing
The skill required: Hadoop, SQL, ETL, Apache Spark

Domain: Data Visualization
The skill required: Python libraries, R

Domain: Machine Learning
The skill required: Algebra, Machine Learning, Statistics, Python

You should also have a strong knowledge of every important machine learning algorithm like Clustering, Regression, Support Vector Machines, Decision Trees, Naive Bayes.

Interested in Data science but still worried about how to become a data scientist?

Let’s clear your confusion!

The very first step for you should be to work on Linear Algebra, Multivariable Calculus, and Python to give you a sense of important domains of data science which are probability, stats, and machine learning. There are 4 main languages for you to learn in the beginning, learn to use and setup R, Python, Sublime Text, and SQL on your own. You can use various free online courses to learn them.

Learn how to apply the concepts of Probability and statistics using these tools, you can use the underlying resources to learn it.

For R, you can use An Introduction to Statistical Learning.

For Python, you can use Think Stats (free pdf).

Also, you can take the guidance of your mentors who are specialized in this field, or read articles and blogs or attend insightful sessions of experienced and influential data scientists.

And all of the above, you can take the help of some of these useful and absolutely free data science courses (data science certifications) that we have gathered for you.

1. Data Analysis & Visualization

Duration – 16 weeks

This course is offered by Georgia Tech on the Udacity platform. The course helps you to understand high dimensional complex data and teaches you data modeling and visualization using R language. The two key concepts which make this course different is that you get to learn how to use TensorFlow for deep learning and Kotlin with Advanced Android.

2. Data Science (Data Science Certifications)

Duration – 8 weeks

This data science course is provided by Harvard which is one of the most elite universities. And therefore the course is highly detailed and makes you an expert in the data science domain. The course however requires some prior knowledge in the fields of maths, statistics, and programming. Harvard also grants you a Data Science degree on the completion of this course.

However, there are different types of Data Scientists working in different exclusive domains so let’s discuss them as well.

-Machine Learning Engineer

They need strong programming and statistical skills. Their role is to design machine learning models.

-Machine Learning Scientist

Their role is to work on researches and algorithms used in adaptive systems.

-Applications Architect

-Data Architect

They ensure to build algorithms for better performance and design efficient analytics for the application

-Data Engineer

They conduct the processing of data and it’s their responsibility to communicate or transport the data to the organization safely.

-Statistician

Their responsibility is to collect, analyze, interpret the data, and then establish a relationship that can be used by the algorithms to operate appropriately. They also have to design and communicate the data to the companies.

-Data Analyst

A Data Analyst has to identify the patterns of the data and then conduct a statistical analysis to apply it to the algorithms needed to solve real-world problems.

For all of you who are wondering how much a Data Scientist earns?

A beginner in Data Science can earn $67K per annum whereas data scientists with experience of work ranging from 1 to 4 years can earn $77 per annum.

Overall, the average data scientist salary is $134K per annum.

Data Science is a highly interesting and rewarding field so don’t waste your time and explore the intriguing areas of it.