Data scientists use data to explain and understand the phenomena that surround them and help organizations make better decision.

Data scientists are in high demand and can provide intellectual challenge, analytical satisfaction, and the opportunity to be at the forefront of technology advances. As big data becomes more important in the decision-making process of organizations, data scientists are becoming more and common. What they do and who they are is explained in detail.

What is a data scientist?

Data scientists decide what questions they and their team should ask, then figure out the best way to answer them using data. They develop predictive models to theorize and forecast. Read more here, Data Science Course in Pune . 

On a daily basis, a data scientist may perform the following tasks:

·        Find patterns and trends to gain insights

·        Create algorithms and data models for forecasting outcomes

·        Use machine learning to improve data quality or product offerings

·        Communication of recommendations to senior staff and other teams

·        Use data tools like Python, R SAS or SQL for data analysis

·        Stay on top of the latest innovations in data science

What is the difference between a data analyst and a data scientist?

Both data analysts and data scientists are able to find patterns or trends in data that can help organizations make better decisions. Data scientists are considered to be more senior and have greater responsibility than data analysts.

Data analysts may be asked to support teams who already have goals set. A data scientist may also spend more time on developing models, using advanced programming, or incorporating machine learning to find and analyze the data.

Data scientists often begin their career as statisticians or data analysts.

How to become a Data Scientist

To become a data scientist, you will need formal training. Here are some things to think about.

1. Earn a data science degree.

Although it is not always necessary, employers like to see that you have some sort of academic background to prove to them that you are capable of handling a data scientist job. A related bachelor's can be helpful. Try studying computer science, data science or statistics to gain an edge in the field.

2. Sharpen relevant skills.

Consider taking a course online or attending a bootcamp if you want to polish your skills in hard data. Here are a few skills that you should have.

Data scientists will spend time using programming language to analyze and manage large amounts of data. Popular programming languages used in data science include:

·        Python

·        R

·        SQL

·        SAS

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Data visualization: Creating charts and graphs are important skills for a data scientist. You should be familiar with the following tools to prepare yourself for the work.

·        Tableau

·        PowerBI

·        Excel

Machine learning: Using machine learning or deep learning in your data science work will allow you to improve the quality of data and predict future outcomes. You can start with the basics by taking a course on machine learning.

Some employers will want to know if you are familiar with the concept of big data. Hadoop and Apache Spark are two of the most popular software frameworks for processing big data.

Communication: Even the most brilliant data scientist will not be able make a difference if they can't communicate their findings effectively. Data scientists are often asked to demonstrate their ability to communicate ideas and results in both written and verbal language.

3. Find an entry-level job in data analytics.

Although there are several paths to becoming an expert in data science, beginning with a job that is closely related can be a great first step. Look for positions that deal heavily with data such as those of a data analyst, business analyst, statistician or data engineer. As you gain more knowledge and experience, you can progress to become a scientist.

4. Prepare for interviews in data science

After a few years working in data analytics, it's possible that you feel ready to transition into data science. Prepare answers to possible interview questions once you have been offered an interview.

You may be asked both technical and behavioral questions. Prepare for both and speak your answer out loud. You can make yourself appear more confident by preparing examples of your previous work or academic experience.

Here are some questions that you may encounter:

·        What are the advantages and disadvantages of a linear-model?

·        What is a randomly-generated forest?

·        How can you use SQL in order to find all duplicates within a set of data?

·        Tell us about your experiences with machine learning.

·        What happened when you couldn't solve a problem? What did you do at that time?

IBM - Learn Data Science

Data scientists are in high demand and can have a challenging career. Start with the basics in Data Science Training in Pune.