You will be aware of the hottest term circling in the market right now which is none other than-

**Data Science**. In 2019, 71% of respondents deployed data science and machine learning in the research and development department. Data science’s use is also prominent in the finance department now.

Although the term is very familiar to people, what it actually has to offer and

**how to become a data scientist**, is still not clear to some, so in this article, we will be getting some insights into this topic so that you could decide what is in it for you.

Data science deals with the process of training your machines to identify their patterns using various tools,

**machine learning**principles, and

**algorithms**to use them in real-world situations efficiently.

**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.

Data Science is the amalgamation of analytics and algorithms. Taking into consideration, all the above domains you can design algorithms using Data Science. You can also design self-driving cars and weather forecasting systems etc.

But you should have a strong command of data engineering, computer science, mathematics, and statistics as it requires you to crack complex problems using the latest technologies to reach valuable conclusions. For this, majorly artificial intelligence is used. The two main fields of artificial intelligence that are machine learning and deep learning, helps data scientists to design efficient and innovative algorithms.

**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 AnalysisThe 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**that we have gathered for you.

### 1. Data Analysis & Visualization

Duration - 16 weeksThis 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

Duration - 8 weeksThis 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**

Their role is to design applications that interact efficiently with the user which involves the concepts of data science.

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

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

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.

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.

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.

**-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.

**by**

**Prajakta Kera**

image credits: pixabay.com

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